Systems and methods for training sequence selection, transmission and reception
20 claims: 4 independent, 16 dependent
- 1複数のトレーニングシーケンスのセットからの少なくとも1つのトレーニングシーケンスを含むトレーニングシーケンスリポジトリであって、該 複数の トレーニングシーケンスのセットは、 から成る、トレーニングシーケンスリポジトリと、 該少なくとも1つのトレーニングシーケンス を 送信するように構成され てい る送信器と を備えている、デバイス。
- 2前記トレーニングシーケンスリポジトリは、前記複数のトレーニングシーケンスのセットからのトレーニングシーケンスの全てを含む、請求項1に記載のデバイス。
- 3前記複数のトレーニングシーケンスのセット内の各トレーニングシーケンスは、トレーニングシーケンスコード(TSC):と関連している、請求項2に記載のデバイス。
- 4前記トレーニングシーケンスリポジトリは、複数のトレーニングシーケンスの第2のセットからの少なくとも1つの第2のトレーニングシーケンスを含み、該 複数のトレーニングシーケンスの 第2のセットは、 から成る、請求項1に記載のデバイス。
- 5前記デバイスは 、 基地局である、請求項4に記載のデバイス。
- 6前記基地局は、搬送周波数および 複数の タイムスロットを用いて信号を生成するように構成され ており、 少なくともいくつかのタイムスロットは、複数の受信器 のための コンテンツを含み、各受信器のためのコンテンツ は 、少なくともそれぞれのトレーニングシーケンスを含み、該複数の受信器のうちの少なくとも1つ に対するそれぞれの トレーニングシーケンスは、 前記複数の トレーニングシーケンスのセットからの 前記 少なくとも1つのトレーニングシーケンスを含み、該複数の受信器のうちの少なくとも1つ に対するそれぞれの トレーニングシーケンスは、 前記複数の トレーニングシーケンスの第2のセットからの 前記 少なくとも1つの第2のトレーニングシーケンスを含む、請求項5に記載のデバイス。
- 7前記デバイスは、 移動 局である、請求項1に記載のデバイス。
- 8前記 移動 局は、前記少なくとも1つのトレーニングシーケンスを割り当てる割当を受信するように構成され てい る、請求項7に記載のデバイス。
- 9前記トレーニングシーケンスは、voice services over a daptive multi-user channels on o ne s lot(VAMOS)タイムスロットで送信される、請求項1に記載のデバイス。
- 10前記デバイスは、前記少なくとも1つのトレーニングシーケンスを使用するように構成され てい る 受信器を含む、 請求項1に記載のデバイス。
- 11デバイスにおける方法であって、 該方法は、 トレーニングシーケンスを送信すること を含み、 該トレーニングシーケンスは、該デバイス上のトレーニングシーケンスリポジトリに含まれ、 該トレーニングシーケンスは、 から成る 複数の トレーニングシーケンスのセットからの 1つのトレーニングシーケンス である、方法。
- 12前記トレーニングシーケンスは、voice services over a daptive multi-user channels on o ne s lot(VAMOS)タイムスロットで送信される、請求項11に記載の方法。
- 13前記トレーニングシーケンスを受信することをさらに含む、請求項11に記載の方法。
- 14前記デバイスは 、 モバイルデバイスであり、前記方法は、 前記 トレーニングシーケンスを割り当てる割当を受信することをさらに含む、請求項11に記載の方法。
- 15前記デバイスは 、基地 局である、請求項11に記載の方法。
- 16前記 複数の トレーニングシーケンスのセットからの前記トレーニングシーケンスを割り当てる割当を送信することによって、 該複数の トレーニングシーケンスのセットの うちの該 トレーニングシーケンスを第1の受信器に割り当てることと、 第2のトレーニングシーケンスを割り当てる割当を送信することによって、 該 第2のトレーニングシーケンスを第2の受信器に割り当てることであって、 複数の トレーニングシーケンスの 第2の セットからの 該 第2のトレーニングシーケンスは、 から成る、ことと をさらに含み 、 前記 トレーニングシーケンスを送信することは、 マルチユーザ信号を含む搬送周波数上でのタイムスロットに対して、 第1および第2の受信器の各受信器に対するそれぞれのトレーニングシーケンスと、各受信器に対するそれぞれのペイロードとを組み合わせることによりマルチユーザ信号を生成することを含み、該第1の受信器に対するそれぞれのトレーニングシーケンスは、 該 トレーニングシーケンスを含み、該第2の受信器に対するそれぞれのトレーニングシーケンスは、 該 第2のトレーニングシーケンスを含む、請求項15に記載の方法。
- 17プログラムが記録された コンピュータ読み取り可能媒体であって、 該プログラムは、複数のトレーニングシーケンスの第1のセットからの少なくとも1つのトレーニングシーケンスを送信器に送信させ、該複数のトレーニングシーケンスの第1のセットは、 から成る 、 コンピュータ読み取り可能媒体。
- 18前記少なくとも1つのトレーニングシーケンス は、 前記複数の トレーニングシーケンスの第1のセットの全て である、 請求項17に記載のコンピュータ読み取り可能媒体。
- 19前記プログラムは、複数のトレーニングシーケンスの第2のセットを前記送信器に送信させ、該複数のトレーニングシーケンスの第2のセットは、 から成る 、 請求項18に記載のコンピュータ読み取り可能媒体。
- 20から成る 複数の トレーニングシーケンス のセット からのトレーニングシーケンスを格納する手段と、 該トレーニングシーケンスを送信する手段と を備える、デバイス。
Independent claims20
80 paragraphs, as filed
(Related application) The present application claims the benefit of the preceding US Provisional Patent Application No. 61 / 089,712 filed on August 18, 2008, the provisional application being incorporated herein by reference in its entirety.
(Field of disclosure) The present disclosure relates to systems and methods for training sequence selection, transmission and reception.
(background) Mobile communication systems use signal processing techniques to counter the effects of time-varying and frequency-selective mobile radio channels to improve link performance. Equalization is used to minimize intersymbol interference (ISI) caused by multipath fading in frequency-selective channels. Since mobile radio channels are random and time-variable, the equalizer needs to adaptively identify the time-variable characteristics of the mobile channel through training and tracking. Time division multiple access (TDMA) wireless systems such as the Global System for Mobile communication (GSM) transmit data in fixed-length time slots, the receiver detects timing information, and channel estimation for further channel equalization. A training sequence designed so that the channel coefficient can be obtained via is included in the time slot (burst).
GSM is a successful digital mobile phone technology deployed around the world. Today, the GSM network provides both voice and data services to billions of subscribers and is still expanding. The GSM access scheme is TDMA. As shown in FIG. 1, the 900 MHz frequency band 100, the downlink 102 and the uplink 104 are separated, each having a bandwidth of 25 MHz including 124 channels. Carrier separation is 200kHz. The TDMA frame 106 consists of eight time slots 108 corresponding to one carrier frequency. The duration of the time slot is 577 μs. In a normal burst, one GSM time slot contains 114 data bits, 26 training sequence bits, 6 tail bits, 2 stealing bits, and 8.25 protection period bits. Currently, only one user's audio is transmitted in each time slot.
Eight training sequences for GSM normal bursts are defined in the 3GPP specification (TS45.002, see "GERAN: Multiplexing and multiple access on the radio path") and are the current GSM / EDGE Radio Access Network (GERAN). Widely used in systems for burst synchronization and channel estimation.
Increasing subscriber numbers and voice traffic are putting great pressure on GSM operators, especially in densely populated countries. Moreover, as the price of voice services declines, efficient use of hardware and spectral resources is desired. One way to increase voice capacity is to multiplex for more than one user in a single time slot.
Voice services over Adaptive Multi-user channels on One Slot (VAMOS) (GP-081949, 3GPP Work Item Description (WID): Voice services over Adaptive Multi-user See channels on One Slot) (Note: Multi-User Reusing-One-Slot: MUROS) (GP-072033, WID: Multi-User Reusing-One-Slot)) is a corresponding research item)) that multiplexes to at least two users at the same time on the same physical radio resource, i.e. multiple users have the same carrier frequency and It is an ongoing research item at GERAN that aims to increase the voice capacity of GERAN by about 2 times per BTS transmitter / receiver in both uplink and downlink by sharing the same time slot. Orthogonal Sub Channel (OSC) (GP-070214, GP-071792, see "Voice capacity evolution with orthogonal sub channel"), co-TCH (GP-071738, see "Speech capacity enhancements using DARP") ), And Adaptive Symbol Constellation (GP-080114 "Adaptive Symbol Constellation for" MUROS (Downlink) ") is one of the three leading technologies of MUROS.
Users sharing the same time slot in the OSC, co-TCH, and adaptive code uplinks use GMSK modulation (Gaussian minimum shift keying) in different training sequences. The base station uses signal processing techniques such as diversity and / or interference elimination to separate the data of the two users. Similar to the uplink, in the downlink of the co-TCH, two different training sequences are used for the DARP (Downlink Advanced Receiver Performance) enabled mobile to separate the two users. .. In the OSC or adaptive code group downlink, two subchannels are QPSK type or adaptive QPSK (AQPSK type) modulation (I-subchannel and Q-subchannel ratio can be adaptively controlled) I- and Q-Mapped to subchannels. The two subchannels use different training sequences as well.
Figure 2 lists eight 26-bit GSM training sequence codes, each of which has a circular sequence structure, namely a central 16-bit reference sequence and 10 protection bits (5). Protective bits are on each side of the reference sequence). The most significant and least significant 5 bits of the reference sequence are duplicated and placed attached to and preceded by the reference sequence, respectively. The protection bits can cover the time of intersymbol interference and form a training sequence that is resistant to time synchronization errors. Each GSM training sequence has an ideal periodic autocorrelation characteristic for non-zero shifts within [-5, 5] if only 16-bit reference sequences are considered.
In GP-070214, GP-071792, "Voice capacity evolution with orthogonal subchannel", a 26-bit length 8 that is optimized for the cross-correlation characteristics of each of the new training sequences with the corresponding legacy GSM training sequence. A new set of training sequences was proposed to OSC. The new sequence is listed in Figure 3. It can be observed that these new training sequences do not retain the cyclic sequence structure of the legacy GSM training sequences.
<p> A broad aspect of the disclosure is a computer-implemented method that optimizes the cross-correlation between the sequences of the first training sequence set and the target training sequence set in order to generate a second training sequence set. Steps to optimize the cross-correlation between the sequences of the second training sequence set to generate the third training sequence set, and the third training sequence set to generate the fourth training sequence set. Provides a method that includes a step of optimizing the cross-correlation between the sequence and the corresponding sequence of the targeted training sequence set and a step of outputting a fourth training sequence set for use in a multi-user transmission system. ..</p><p> Another broad aspect of the disclosure is a computer-implemented method that optimizes the cross-correlation between sequences in the first training sequence set to generate a second training sequence set, and the first. A step to optimize the cross-correlation between the sequences of the second training sequence set and the target training sequence set to generate the third training sequence set, and the third to generate the fourth training sequence set. Includes a step of optimizing the cross-correlation between the sequence of the training sequence set and the corresponding sequence of the target training sequence set, and the step of outputting a fourth training sequence set for use in a multi-user transmission system. Provide a method.</p><p> Another broad aspect of the present disclosure is a computer-readable medium encoded in a data structure, wherein the data structure is:</p><p><chemistry num="1"><img file="JP5111664B2_D0001.tif" /></chemistry>With at least one training sequence from the first set of training sequences consisting of</p><p><chemistry num="2"><img file="JP5111664B2_D0002.tif" /></chemistry>Provided is a computer-readable medium comprising, with at least one training sequence from a second set of training sequences consisting of.</p><p> Another broad aspect of the disclosure is a transmitter, comprising a signal generator configured to generate a signal using carrier frequencies and time slots, with at least some time slots receiving multiple receptions. The transmitter contains dexterous content, the content for each receiver and each slot contains at least each training sequence, and the transmitter.</p><p><chemistry num="3"><img file="JP5111664B2_D0003.tif" /></chemistry>Provided is a transmitter encoded in at least one training sequence from a first set of training sequences consisting of.</p><p> Another broad aspect of the present disclosure is to combine a respective training sequence for each receiver of at least two receivers with a respective payload for each receiver for a time slot at a carrier frequency containing a multi-user signal. , A step of generating a multi-user signal, each training sequence for at least one of multiple receivers</p><p><chemistry num="4"><img file="JP5111664B2_D0004.tif" /></chemistry>With steps, including the first training sequence from the first set of training sequences consisting of Provided are a step of transmitting the above signal and a method including.</p><p> Another broad aspect of the present disclosure is a receiver, comprising at least one antenna, wherein the receiver.</p><p><chemistry num="5-1"><img file="JP5111664B2_D0005.tif" /></chemistry></p><p><chemistry num="5-2"><img file="JP5111664B2_D0006.tif" /></chemistry>Encoded with at least one training sequence from the first set of training sequences consisting of The above receiver</p><p><chemistry num="6"><img file="JP5111664B2_D0007.tif" /></chemistry>At least one of the training sequences in the second set of training sequences consisting of is further encoded and Further, the receiver operates using a training sequence selected from at least one training sequence from the first set of training sequences and at least one training sequence from the second set of training sequences. Provide a receiver configured as such.</p><p> A broad aspect of the present disclosure is a method for a mobile device, wherein the mobile device is:</p><p><chemistry num="7"><img file="JP5111664B2_D0008.tif" /></chemistry>Has at least one training sequence from the first set of training sequences consisting of The above mobile devices</p><p><chemistry num="8"><img file="JP5111664B2_D0009.tif" /></chemistry>Further having at least one training sequence in a second set of training sequences consisting of Provides a method that includes at least one training sequence from the first set of training sequences and steps that operate using a training sequence selected from at least one training sequence from the second set of training sequences. ..</p><p> Another broad aspect of the disclosure is</p><p><chemistry num="9"><img file="JP5111664B2_D0010.tif" /></chemistry>Provided is the use of a training sequence from a set of training sequences consisting of, as a training sequence in a cellular radiotelephone.</p><p> Here, an embodiment of the present application will be described with reference to the accompanying drawings.<u style="single">For example, the present invention provides the following items.</u><u style="single">(Item 1)</u><u style="single"> It s a computer implementation method.</u><u style="single"> Optimizing the cross-correlation between the sequences of the first training sequence set and the target training sequence set to generate the second training sequence set,</u><u style="single"> Optimizing the cross-correlation between the sequences of the second training sequence set to generate a third training sequence set,</u><u style="single"> Optimizing the cross-correlation between the sequence of the third training sequence set and the corresponding sequence of the target training sequence set to generate a fourth training sequence set.</u><u style="single"> To output the fourth training sequence set for use in a multi-user transmission system</u><u style="single"> Including methods.</u><u style="single">(Item 2)</u><u style="single"> It s a computer implementation method.</u><u style="single"> Optimizing the cross-correlation between sequences within the first training sequence set to generate a second training sequence set,</u><u style="single"> To optimize the cross-correlation between the sequences of the second training sequence set and the target training sequence set in order to generate a third training sequence set.</u><u style="single"> Optimizing the cross-correlation between the sequence of the third training sequence set and the corresponding sequence of the target training sequence set to generate a fourth training sequence set.</u><u style="single"> To output the fourth training sequence set for use in a multi-user transmission system</u><u style="single"> Including methods.</u><u style="single">(Item 3)</u><u style="single"> The method of item 1 or 2, further comprising optimizing the autocorrelation for the candidate set of training sequences to generate the first training sequence set above.</u><u style="single">(Item 4)</u><u style="single"> Cross-correlation optimization</u><u style="single"> SNR deterioration,</u><u style="single"> Parameters related to the amplitude of the intercorrelation coefficient,</u><u style="single"> Simulation-based optimization,</u><u style="single"> The method according to any one of items 1 to 3, which comprises optimizing based on criteria selected from the group consisting of.</u><u style="single">(Item 5)</u><u style="single"> A computer-readable medium encoded by a data structure, the data structure of which is</u><chemistry num="10"><img file="JP5111664B2_D0011.tif" /></chemistry><u style="single">Consists of at least one training sequence from the first set of training sequences,</u><chemistry num="11-1"><img file="JP5111664B2_D0012.tif" /></chemistry><chemistry num="11-2"><img file="JP5111664B2_D0013.tif" /></chemistry><u style="single">Consists of at least one training sequence from a second set of training sequences</u><u style="single">A computer-readable medium that comprises.</u><u style="single">(Item 6)</u><u style="single"> The data structure further provides a one-to-one pairing between each of the at least one training sequence from the first set and the corresponding best pair of training sequences from the second set. The computer-readable medium described in item 5.</u><u style="single">(Item 7)</u><u style="single"> A computer program product that can be loaded directly into the internal memory of a computer, the software code portion for performing the step according to any one of items 1 to 4 when running on the computer. A computer program product.</u><u style="single">(Item 8)</u><u style="single"> It s a transmitter,</u><u style="single"> It comprises a signal generator configured to generate signals using carrier frequencies and time slots, at least some time slots include content for multiple receivers, content for each receiver and each. Slots contain at least each training sequence</u><u style="single"> The transmitter is</u><chemistry num="12"><img file="JP5111664B2_D0014.tif" /></chemistry><u style="single">A transmitter consisting of at least one training sequence from the first set of training sequences.</u><u style="single">(Item 9)</u><u style="single"> The signal generated by the signal generator is such that for at least some of the slots containing content for the plurality of receivers, each training sequence for at least one of the plurality of receivers is a training sequence. 8. The transmitter according to item 8, wherein the signal comprises the first training sequence from the first set above.</u><u style="single">(Item 10)</u><u style="single"> The signal generated by the signal generator is such that for at least some of the slots containing content for the plurality of receivers, each training sequence for at least one of the plurality of receivers</u><chemistry num="13"><img file="JP5111664B2_D0015.tif" /></chemistry><u style="single">9. The transmitter according to item 9, wherein the signal comprises a second training sequence from a second set of training sequences consisting of.</u><u style="single">(Item 11)</u><u style="single"> For at least some of the slots containing content for multiple receivers, the second training sequence is the sequence of the second set of training sequences that are the best pair with the first training sequence. , The transmitter according to item 10.</u><u style="single">(Item 12)</u><u style="single"> For time slots at carrier frequencies that include multi-user signals</u><u style="single"> Combining each training sequence for each receiver of at least two receivers with a respective payload for each receiver is to generate a multi-user signal, at least one of the plurality of receivers described above. Each training sequence for one</u><chemistry num="14"><img file="JP5111664B2_D0016.tif" /></chemistry><u style="single">Containing the first training sequence from the first set of training sequences, consisting of</u><u style="single"> To transmit the signal</u><u style="single"> Including methods.</u><u style="single">(Item 13)</u><u style="single"> Each of the above training sequences for at least one of the plurality of receivers</u><chemistry num="15"><img file="JP5111664B2_D0017.tif" /></chemistry><u style="single">The method of item 12, comprising a second training sequence from a second set of training sequences consisting of.</u><u style="single">(Item 14)</u><u style="single"> The method according to item 13, wherein the second training sequence is a training sequence of the second set of training sequences that is the best pair with the first training sequence.</u><u style="single">(Item 15)</u><u style="single"> If the first multi-user recognition receiver is shared with the second receiver,</u><u style="single"> a) In the step of assigning the first training sequence to the first receiver,</u><u style="single"> b) With the step of assigning the second training sequence to the second receiver</u><u style="single"> The method of item 13, which is applied to generate a two-user signal, further comprising.</u><u style="single">(Item 16)</u><u style="single"> When the first multi-user unrecognized receiver is shared with the second multi-user recognized receiver</u><u style="single"> a) In the step of assigning the second training sequence to the first receiver,</u><u style="single"> b) In the step of assigning the first training sequence to the second receiver,</u><u style="single"> The method of item 15, further comprising.</u><u style="single">(Item 17)</u><u style="single"> 12. The method of item 12, further comprising assigning a training sequence from the first set, by submitting an assignment to assign the training sequence from the first set.</u><u style="single">(Item 18)</u><u style="single"> Assigning a training sequence from the first set and assigning a training sequence from the first set by sending an assignment.</u><u style="single"> Assigning a training sequence from the second set above By sending an assignment to assign a training sequence from the second set above</u><u style="single"> The method of item 13, further comprising.</u><u style="single">(Item 19)</u><u style="single"> It s a receiver</u><u style="single"> Equipped with at least one antenna</u><u style="single"> The receiver</u><chemistry num="16"><img file="JP5111664B2_D0018.tif" /></chemistry><u style="single">Consisting of, encoded in at least one training sequence from the first set of training sequences,</u><u style="single"> The receiver further</u><chemistry num="17"><img file="JP5111664B2_D0019.tif" /></chemistry><u style="single">Encoded in at least one training sequence of the second set of training sequences, consisting of</u><u style="single"> In addition, the receiver uses a training sequence selected from the at least one training sequence from the first set of training sequences and the at least one training sequence from the second set of training sequences. A receiver that is configured to work with.</u><u style="single">(Item 20)</u><u style="single"> A mobile device with the receiver described in item 19.</u><u style="single">(Item 21)</u><u style="single"> The mobile device according to item 20, encoded in all training sequences of the first set above.</u><u style="single">(Item 22)</u><u style="single"> A base station with the receiver of item 19.</u><u style="single">(Item 23)</u><u style="single"> 20. The mobile device according to item 20, further configured to receive different training sequence assignments as the mobile station moves.</u><u style="single">(Item 24)</u><u style="single"> It s a method for mobile devices,</u><u style="single"> The mobile device is</u><chemistry num="18"><img file="JP5111664B2_D0020.tif" /></chemistry><u style="single">Having at least one training sequence from the first set of training sequences, consisting of</u><u style="single"> The mobile device also</u><chemistry num="19"><img file="JP5111664B2_D0021.tif" /></chemistry><u style="single">Has at least one training sequence of the second set of training sequences, consisting of</u><u style="single"> Including operating using a training sequence selected from the at least one training sequence from the first set of training sequences and the at least one training sequence from the second set of training sequences. ,Method.</u><u style="single">(Item 25)</u><u style="single"> 24. The method of item 24, further comprising receiving an assignment to assign the first set of training sequences above.</u><u style="single">(Item 26)</u><u style="single"> 24. The method of item 24, further comprising receiving an assignment to assign the second set of training sequences above.</u><u style="single">(Item 27)</u><chemistry num="20"><img file="JP5111664B2_D0022.tif" /></chemistry><u style="single">Use of a training sequence from a set of training sequences consisting of, as a training sequence in a cellular radiotelephone.</u></p>
<figref num="1">Figure 1 is a schematic diagram of GSM bandwidth allocation and TDMA frame definition.</figref><figref num="2">Figure 2 is a table listing legacy GSM training sequences.</figref><figref num="3">Figure 3 is a table containing a set of training sequences with optimized cross-correlation characteristics compared to legacy GSM training sequences.</figref><figref num="4A">FIG. 4A is a table containing a set of training sequences.</figref><figref num="4B">FIG. 4B is a schematic representation of a computer-readable medium containing the training sequence of FIG. 4A.</figref><figref num="5A">FIG. 5A is a table containing a set of training sequences.</figref><figref num="5B">FIG. 5B is a schematic representation of a computer-readable medium containing the training sequence of FIG. 5A.</figref><figref num="6A">FIG. 6A is a table containing a set of training sequences.</figref><figref num="6B">FIG. 6B is a schematic representation of a computer-readable medium containing the training sequence of FIG. 6A.</figref><figref num="7">FIG. 7 is a diagram showing some sets used to define a set of training sequences.</figref><figref num="8">FIG. 8 is a flowchart of the first method of determining the training sequence.</figref><figref num="9A">FIG. 9A is a flowchart of the first method of assigning a training sequence.</figref><figref num="9B">FIG. 9B is a flowchart of the second method of assigning a training sequence.</figref><figref num="10A">FIG. 10A is a block diagram of the transmitter for OSC downlink transmission.</figref><figref num="10B">FIG. 10B is a block diagram showing a pair of receivers for the OSC subchannel.</figref><figref num="11A">FIG. 11A is a block diagram of a co-TCH transmitter for downlink transmission.</figref><figref num="11B">FIG. 11B is a block diagram of a pair of transmitters for co-TCH downlink transmission.</figref><figref num="12A">FIG. 12A is a diagram showing a pair of OSC or co-TCH transmitters for uplink transmission.</figref><figref num="12B">FIG. 12B is a block diagram of a receiver consisting of two receivers for receiving each transmission from the transmitter pair of FIG. 12A.</figref>
Signal-to-noise ratio (SNR) degradation (B. Steiner and P. Jung, "Optimum and suboptimum channel estimation for the uplink CDMA mobile radio systems with joint detection", European Transactions on Telecommunications, vol.5, Jan.-Feb. , 1994, pp.39-50, and M. Pukkila and P. Ranta, "Channel estimator for multiple co-channel demodulation in TADM mobile systems", Proc. Of the 2nd EPMC, Germany) is used herein to evaluate the correlation characteristics of training sequences and / or to design new training sequences. In MUROS / VAMOS, interference arises from other subchannels of the same MUROS / VAMOS pair on the same cell phone and from the same channel signal on another cell phone.
The deterioration of SNR can be determined as follows. Training sequence of length N with S = {s<sub>1</sub>, s<sub>2</sub>, , s<sub>N</sub>}, S<sub>n</sub>Let ε {-1, + 1}, n = 1, ..., N. L-tap independent composite channel impulse response h<sub>m</sub>= (h<sub>m, 1</sub>, h<sub>m, 2</sub>, ..., h<sub>m, L</sub>), M = 1,2 Consider two synchronous identical channels or MUROS / VAMOS signals. The joint channel impulse response is h = (h<sub>1</sub>, h<sub>2</sub>). Receive signal sample, y = Sh<sup>t</sup>+ n (In the equation, the noise vector is n = (n)<sub>1</sub>, n<sub>2</sub>, , n<sub>N-L + 1</sub>)<sup>t</sup>And S = [S<sub>1</sub>, S<sub>2</sub>] Is a (N-L + 1) x2L matrix, S<sub>m</sub>(m = 1,2) is defined below).
<maths num="1"><img file="JP5111664B2_D0023.tif" /></maths>This is the training sequence (s<sub>m, 1</sub>, s<sub>m, 2</sub>, , s<sub>m, N</sub>) Corresponds to (S<sub>1</sub>And S<sub>2</sub>Note that can be constructed with two different training sequences, respectively, from the same training sequence set or different training sequence sets).
The minimum squared error estimator for the channel is as follows.
<maths num="2"><img file="JP5111664B2_D0024.tif" /></maths>The SNR degradation of the training sequence is defined as follows.
<maths num="3"><img file="JP5111664B2_D0025.tif" /></maths>In the equation, tr [X] is a trace of the matrix X, Q = [q<sub>ij</sub>]<sub>2Lx2L</sub>= S<sup>t</sup>S is S<sub>1</sub>And S<sub>2</sub>Autocorrelation, as well as S<sub>1</sub>And S<sub>2</sub>It is a correlation matrix including the cross-correlation between and, and the calculation of the entry is as follows.
<maths num="4"><img file="JP5111664B2_D0026.tif" /></maths> Based on the definitions (1) to (3), the pair SNR degradation values of the GSM training sequence are calculated and listed in Table 1.
<tables num="1"><img file="JP5111664B2_D0027.tif" /></tables> The mean, minimum and maximum pair SNR degradation values between different GSM training sequences are equal to 5.10 dB, 2.72 dB and 11.46 dB, respectively. Table 1 demonstrates that some GSM training sequence pairs yield reasonable SNR degradation values and some GSM training sequence pairs are strongly correlated. It seems inappropriate to apply all existing GSM training sequences to MUROS / VAMOS. For MUROS / VAMOS, it is desirable to have a new training sequence, each with very good autocorrelation characteristics and very good cross-correlation characteristics with the corresponding GSM training sequence. Also, with further optimization. It is desirable to reduce the effects of identical channel interference, the cross-correlation characteristics of any pair of new training sequences, and the cross-correlation characteristics of any pair of new training sequences and legacy GSM training sequences.
Tables 2 and 3 show the pair SNR degradation performance of these sequences between any pair of sequences in Figure 3 and GSM training sequences, and between any pair of these sequences themselves.
<tables num="2"><img file="JP5111664B2_D0028.tif" /></tables>
<tables num="3"><img file="JP5111664B2_D0029.tif" /></tables> In Table 2, the diagonal paired SNR degradation values in the table are the result of the sequence in Figure 3 and the corresponding GSM training sequence. As used herein, a corresponding sequence is defined as two sequences having the same training sequence number in two separate sequence tables. The average diagonal in Table 2 is equal to 2.11 dB. The mean, minimum and maximum SNR degradation values between the sequence in Figure 3 and any pair of GSM TSCs are 2.63 dB, 2.05 dB and 4.87 dB, respectively.
Table 3 shows that the mean, minimum, and maximum SNR degradation values between any pair of different sequences in Figure 3 are 3.19 dB, 2.32 dB, and 6.89 dB, respectively.
Both Tables 2 and 3 demonstrate good average pair SNR degradation performance between any pair of sequences in Table 2 and GSM training sequences, and between any pair of different sequences in Table 2. However, the peak pair SNR degradation values shown in Tables 2 and 3 can affect co-channel interference elimination with the introduction of MUROS / VAMOS.
New training sequence for MUROS / VAMOS Training sequence that is the best pair with the corresponding GSM TSC In one embodiment of the present disclosure, a computer search yields a set of eight sequences of length 26, each of which corresponds to a GSM training sequence for SNR degradation calculated using (1)-(3). And the best pair. FIG. 4A shows these best pair sequences, generally referred to as 120 in data structure 122 stored on computer readable medium 124, called training sequence set A. The search was performed as follows. 1) Start with the first GSM training sequence. 2) For the sequence with the lowest SNR degradation, search all sets of candidate sequences, add the found sequence to a new set, and remove the found sequence from the candidate set. 3) Repeat steps 1 and 2 for each of the 2nd to 8th GSM training sequences and the sequence that is the best pair.
FIG. 4B shows a computer-readable medium, generally represented by 128, in which the data structure 125 is stored. Data structure 125 includes a set of standard GSM training sequences 126 and also includes training sequence set A 127. There is a one-to-one correspondence between GSM training sequence 126 and training sequence set A 127.
<tables num="4"><img file="JP5111664B2_D0030.tif" /></tables> The mean, minimum and maximum SNR degradation values between any pair of sequences and GSM training sequences in Figure 4A are 2.52 dB, 2.04 dB and 4.10 dB, respectively. The average diagonal in Table 4 is equal to 2.07 dB. Based on the results shown in Table 4, the new training sequence in Figure 4A is well designed to pair with the corresponding GSM training sequence.
Table 5 shows the SNR degradation values between the sequences listed in Figure 4A. The mean, minimum and maximum pair SNR degradation values between the GSM training sequence and the best pair sequence are equal to 3.04 dB, 2.52 dB and 4.11 dB, respectively.
<tables num="5"><img file="JP5111664B2_D0031.tif" /></tables> B. Training sequence with cyclic structure with optimized autocorrelation and cross-correlation characteristics A set of training sequences with optimized autocorrelation and cross-correlation characteristics was determined by computer search using the methods described in detail below. A set of training sequences, called training sequence set B, is described in FIG. 5A and is generally represented by 130 in the data structure 132 stored on the computer readable medium 134.
FIG. 5B shows a computer-readable medium, generally represented by 138, in which the data structure 135 is stored. Data structure 135 includes a set of standard GSM training sequences 136 and also includes training sequence set B 137. There is a one-to-one correspondence between GSM training sequence 136 and training sequence set A 137.
The pair SNR degradation values between any pair of new training sequences and GSM training sequences in Figure 5A are shown in Table 6. The average, minimum and maximum SNR degradation values in Table 6 are 2.43 dB, 2.13 dB and 2.93 dB, respectively. The average diagonal in Table 6 is equal to 2.22 dB.
<tables num="6"><img file="JP5111664B2_D0032.tif" /></tables> Table 7 shows the paired SNR degradation values between the sequences listed in Figure 5A. The average, minimum and maximum pair SNR degradation values in Table 7 are 3.17 dB, 2.21 dB and 4.75 dB, respectively.
<tables num="7"><img file="JP5111664B2_D0033.tif" /></tables> C. Training sequence without circular structure Unlike training sequence set B, a third training sequence set, referred to herein as training sequence set C, is composed of sequences that do not retain a circular structure. In the generation of training sequence set C, only the optimization steps II-IV for training sequence set B outlined below are considered. To optimize SNR degradation between the new sequence and the GSM training sequence, the sequence set Ω1 is 2<sup>26</sup>It is obtained from the sequences by selecting the | Ω1 | sequence that has the least average SNR degradation between the sequence at | Ω1 | and all GSM training sequences. The training sequence set C is listed in FIG. 6A and is generally represented by 140 in the data structure 142 stored on the computer readable medium 144.
FIG. 6B shows a computer-readable medium, generally represented by 148, in which data structure 145 is stored. Data structure 145 includes a set of standard GSM training sequences 146 and also includes training sequence set C 147. There is a one-to-one correspondence between GSM training sequence 146 and training sequence set C 147.
The pair SNR degradation values between any pair of new training sequences and GSM training sequences in Figure 6A are shown in Table 8. The average, minimum and maximum SNR degradation values in Table 8 are 2.34 dB, 2.11 dB and 2.87 dB, respectively. The average diagonal in Table 8 is equal to 2.16 dB.
<tables num="8"><img file="JP5111664B2_D0034.tif" /></tables> Table 9 shows the paired SNR degradation values between the sequences listed in Figure 6A. The average, minimum and maximum pair SNR degradation values in Table 9 are 3.18 dB, 2.44 dB and 4.19 dB, respectively.
<tables num="9"><img file="JP5111664B2_D0035.tif" /></tables> Sequence search procedure-first method FIG. 7 shows the procedure for finding a second set of | Ω3 | training sequences Ω3 with autocorrelation and cross-correlation characteristics, taking into account the target set of the training sequence Ψ. This procedure consists of sequence sets Ω 152, Ω 1 154, Ω 2 156 and Ω 3 158 (Ω Ω 1 Ω 2 Ω 3 and | Ω 3 | = the number of sequences found, where | · | are the elements in one set. Includes the determination of). Ω 152 is a subset of all possible sequences 150 determined by the first optimization step. Ω 1 154 is a subset of Ω 152 determined by the second optimization step. Ω2 156 is a subset of Ω1 154 determined by the third optimization step. Ω 3 158 is a subset of Ω 156 determined by the fourth optimization step.
Although the method is implemented in a computer, it will be described with reference to the flowchart of FIG. The method begins in block 8-1 by optimizing the autocorrelation of the training sequence candidate sets to generate the first training sequence set. The method then optimizes the SNR degradation between the first training sequence set and the target set sequence of the training sequence Ψ in block 8-2 to generate a second training sequence set. The method then optimizes the SNR degradation between the second training sequence set sequences in blocks 8-3 to generate a third training sequence set. The method then optimizes the SNR degradation between the training sequence of the third set and the corresponding sequence of the target set of the training sequence Ψ in blocks 8-4. The output of block 8-4 is the fourth set of training sequences, which is the new training sequence set. This set is output for use in systems with multi-user transmission. Another embodiment provides a computer-readable medium containing instructions that, when executed by a computer, cause the method of FIG. 8 to be performed.
In some embodiments, steps 8-2 and 8-3 are performed in the reverse order shown and described above. This optimizes the intercorrelation between the sequences in the first training sequence set to generate the second training sequence set and the mutual between the sequences in the second training sequence set and the target training sequence set. A fourth training by optimizing the correlation and optimizing the intercorrelation between the step of generating the third training sequence set and the sequence of the third training sequence set and the corresponding sequence of the target training sequence set. A computer method is provided that includes a step of generating a sequence set and a step of outputting a fourth training sequence set for use in a multi-user transmission system.
First optimization step: Autocorrelation optimization: Consider all binary sequences of the desired length. Optionally, copy some of the last bits of the sequence to the beginning to create a somewhat circular sequence. Search for sequences that have zero autocorrelation values for the non-zero shift range.
To achieve zero autocorrelation, the sequence in which the autocorrelation is determined must have a uniform length. If odd-length sequences are required, additional bits are added to the set of sequences with zero autocorrelation values. This may be done, for example, by copying the first bit of the original sequence, or by adding -1 or +1 for further optimization of the cross-correlation properties.
The output of this step is a set of sequences Ω with optimized autocorrelation characteristics. Correlation matrix Q = S in (3) for the sequence in which additional bits are added<sup>t</sup>In S, the maximum magnitude of the autocorrelation coefficient is 1.
Second optimization step: Optimization of SNR degradation between the new sequence and the target set of the training sequence Ψ: A subset of Ω Ω1 from Ω to the sequence in | Ω1 | and the target set of the training sequence Ψ It is obtained by selecting the | Ω1 | sequence with the smallest mean SNR degradation between. The average SNR degradation for a given sequence from Ω is determined by calculating the degradation for each of that sequence and the target set training sequence and averaging the results.
Third optimization step: Optimization of SNR degradation between new sequences: Select Ω2, a subset of Ω1, that has the lowest average SNR degradation between sequences at | Ω2 |. The following is an example of how the third optimization step can be performed. 1) Select the first sequence from Ω2 and remove it from Ω2. 2) For the sequence with the lowest SNR degradation with the first sequence, inspect all remaining sequences in Ω2, select it as the second sequence, and remove it from Ω2. 3) For the sequence with the lowest average SNR degradation with the first and second sequences, inspect all remaining sequences in Ω2 and remove them from Ω2. 4) Continue until the desired number of sequences are identified. The average SNR degradation between the sequences identified in this way is calculated. 5) Repeat steps 1 through 4 using a different first sequence from Ω2 to produce each set of sequences and each average SNR degradation. 6) From all the sets of sequences generated in this way, select the set of sequences with the least average SNR degradation.
Fourth optimization step: Optimization of SNR degradation between the new training sequence and the corresponding sequence of the target set of the training sequence Ψ: Sequence sets Ω2 to | Ω3 | sequences are selected. This step is used to determine a pair of training sequences that includes one from the target set and one from the new set. The following is an exemplary method for performing this step. a) Select a training sequence from the target set. b) Find the training sequence in the new set with the lowest SNR degradation with the training sequence of the target set, pair that training sequence with the first training sequence from the target set, and pair that training sequence with the available training. Remove from the set of sequences. c) Repeat steps a) and b) until all sequences from the target set have been selected.
Applying the optimization procedure described above, we constructed a set of training sequences in Figure 5A with | Ω1 | = 120 and | Ω2 | = 12. More specifically, it is as follows.
First optimization step: Autocorrelation optimization: Consider all binary sequences of length 20 (set size is 2)<sup>20</sup>Is). Similar to GSM TSC, for each such sequence, copy the last 5 bits of the sequence, precede these 5 bits to the most significant, and generate a sequence of length 25; autocorrelation definition.
<maths num="5"><img file="JP5111664B2_D0036.tif" /></maths>Is used to find a sequence of length 25 with a zero autocorrelation value for a non-zero shift [-5,5]. There are a total of 5440 such available sequences.
To be compatible with the current TSC format, the new TSC length must be 26. The 26th bit of the full-length sequence (length 26) is -1 or +1 by copying the first bit of the corresponding sequence of length 20 or for further optimization of the cross-correlation characteristics. It can be obtained by adding. Therefore, a set Ω of sequences with optimized autocorrelation characteristics is generated. In both methods, the correlation matrix Q = S in (3)<sup>t</sup>In S, the maximum magnitude of the autocorrelation coefficient is limited to 1.
Second optimization step: Optimization of SNR degradation between new sequence and GSM TSC: Subset of Ω Ω1 is the average SNR degradation value between the sequence at | Ω1 | and all GSM TSCs from Ω. Is obtained by selecting the | Ω1 | sequence with the smallest. The average SNR degradation for a given sequence from Ω is determined by calculating the degradation for each of that sequence and the GSM sequence and averaging the results.
Third optimization step: Optimization of SNR degradation between new sequences: Select Ω2, a subset of Ω1, that has the lowest average SNR degradation between sequences at | Ω2 |.
Fourth optimization step: Optimization of SNR degradation between the new training sequence and the corresponding GSM TSC: Sequence set Ω2 to | Ω3 | = 8 Sequences are determined. The result is set B of the sequence in Figure 5A.
Sequence search procedure-second method In another method of sequence search, a search method similar to the above-mentioned "first method" in which the first optimization is omitted is provided. In this case, the method begins with the second optimization step of the first method, where the sequence set Ω1 is the average of all possible sequences, between the sequence at | Ω1 | and all sequences of the target set Ψ. Obtained by selecting the | Ω1 | sequence with the lowest SNR degradation value. Note that in this embodiment the sequence does not have to be circular. Using the words in the flowchart of FIG. 8, block 8-1 is omitted and the "first training sequence" is a candidate set for the training sequence.
When applied to the MUROS / VAMOS problem, in the second optimization step, the sequence set Ω1 is all 2 of length 26.<sup>26</sup>It is obtained by selecting the | Ω1 | sequence that has the smallest average SNR degradation value between the sequence at | Ω1 | and all GSM training sequences. The result is set C of the sequence in Figure 6A above.
Training sequence assignment For example, part of set A or set A defined in combination with a legacy GSM training sequence, or part of set B or set B defined in combination with a legacy GSM training sequence, or legacy GSM training. Once you have defined a new set of training sequences or part of a new set of training sequences for use with a target set of training sequences, such as set C or part of set C defined above with the sequence. , Various mechanisms are provided for assigning training sequences. It should be noted that these mechanisms are not specified in the examples described herein. Specific examples of multi-user operation are the MUROS / VAMOS operation described above, for example, the specific implementation thereof includes its implementation in OSC or co-TCH or adaptive code group.
Within mobile phones where multi-user transmission is implemented, there is interference from at least two sources in the interference limiting scenario. This includes interference from other users (s) on the same physical transmission resource in the mobile phone, and interference from mobile stations of the same physical transmission resource in other mobile phones. Traditional mobile stations already have the ability to cope with interference from mobile stations using the same physical transmission resources in other mobile phones.
A mobile station that specifically recognizes multi-user operation is called "multi-user recognition". In a specific example, a mobile station that recognizes the VAMOS recognition operation can be called, for example, a VAMOS recognition mobile station. Such mobile devices are configured to be able to use any training sequence in the target set and any training sequence in the new set. A mobile station that does not specifically recognize multi-user operation is called "multi-user non-recognition". Such mobile devices are configured to be able to use only target set training sequences. Multi-user unrecognized mobile stations can still function in multi-user situations, such mobile stations may interfere with other users (including multiple) on the same physical transmission resource on the same mobile phone. Note that it handles mobile stations in the same way that it handles mobile stations using the same physical transmission resources in other mobile phones.
Similarly, the network may or may not have multi-user capabilities. Networks with multi-user capabilities work with a target set and new sets of training sequences, and networks without multi-user capabilities use only the target set of training sequences.
In some embodiments, the allocation of training sequences to base stations is made during network configuration and does not change until reconfiguration is performed. A given multi-user recognition network element, such as a base station, comprises a training sequence from a target set and a training sequence from a new set. When a base station performs multicarrier transmission, the base station is configured with each training sequence from the target set and each training sequence from the new set for each carrier frequency it uses. The training sequence from the new set is the training sequence that is the best pair with the training sequence of the target set, and vice versa. In such cases, the training sequence assigned to the mobile station becomes a function of the previously executed network configuration. As mobile stations move between service areas, the assigned training sequence changes. In some embodiments, a given mobile station is assigned the same training sequence for both uplink and downlink transmissions. In another embodiment, different training sequences may be assigned.
Network behavior can be divided into two types: behavior when the time slot is used only for a single user, and behavior when the time slot is used for multiple users. be able to.
Behavior when the time slot is used for a single user: A) When a multi-user recognition MS is supported by a network without multi-user capabilities, one training sequence from the target set, ie, training assigned to the corresponding base station during or during network configuration. The sequence is assigned to this MS. Multi-user recognition mobile stations will be able to recognize and use both the target set of training sequences and the new set of training sequences. B) When a multi-user recognition MS is supported by a network with multi-user capabilities, this MS does not need to share a time slot with another MS if there is a free time slot. On average, the new training sequence is designed to have better correlation characteristics than the target set training sequence, so it corresponds to one new training sequence, ie, during or during network configuration. A new training sequence assigned to the base station will be assigned to this MS.
Behavior when the time slot is used for multiple users A) If the first multi-user recognition MS, MS-A, is supported by a network with multi-user capabilities as described above, it will be a new training sequence, i.e. the corresponding base station during or during network configuration. The new assigned training sequence is assigned to MS-A. If there is a request to share the same time slot as the second MS, MS-B, with the new training sequence used by MS-A, whether MS-B is a multi-user aware MS or not. The best pair of target set training sequences, i.e., target set training sequences assigned to the corresponding base stations during or during network configuration, is assigned to MS-B. B) If the first multi-user recognition MS, MS-A, is supported by a network with multi-user capabilities, it will be assigned to the training sequence from the target set, ie the corresponding base station during or during network configuration. The training sequence from the given target set is assigned to MS-A. In the same time slot, if there is a request to share MS-A with a second MS, MS-B, which is multi-user recognition, a new best pair with the training sequence of the target set used by MS-A. The training sequence, that is, the training sequence assigned to the corresponding base station during or during network configuration, is assigned to MS-B.
Two flowcharts of an exemplary method of training sequence assignment using multi-user slots are shown in Figures 9A and 9B. FIG. 9A relates to the case of A) described above, and FIG. 9B relates to the case of B) described above.
Referring here to FIG. 9A, when the first multi-user recognition mobile station is shared with the second mobile station, block 9A-1 assigns a new training sequence to the first mobile station. including. Block 9A-2 is the training sequence of the target set, which is the best pair with the new training sequence used by the first mobile station, regardless of whether the second mobile station is a multi-user recognized mobile station or not. Includes assigning to a second mobile station.
Referring here to FIG. 9B, when the first multi-user recognition mobile station shares with the second multi-user recognition mobile station, block 9B-1 sets the training sequence from the target set. Includes assigning to one mobile station. Block 9B-2 includes assigning a new set of training sequences, which is the best pair with the training sequence used by the first mobile station, to the second mobile station.
Illustrative transmitter and receiver implementation Here are various detailed examples of transmitter and receiver implementations. FIG. 10A shows an OSC (Orthogonal Subchannel) (or Adaptive Code Group) transmitter for downlink transmission. Most of the components are standard and will not be discussed in detail. The transmitter includes a training sequence repository 200 containing a targeted training sequence set and a new training sequence set generated using one of the methods described above. Typically, training sequences from the target set and training sequences from the new set are assigned to the transmitter during or during network configuration. In some embodiments, the training sequence thus assigned to a multi-user slot is used according to the method of FIG. 9A or 9B, to give some specific examples. The rest of the drawings are specific examples of signal generators for generating multi-user signals.
Figure 10B shows a pair of OSC (Orthogonal Subchannel) receivers for downlink transmission. Most of the components are standard and will not be discussed in detail. Each receiver contains a respective memory 210 containing a targeted training sequence set and a new training sequence set generated using one of the methods described above. One of these, called training sequence A, is used by the upper receiver to perform timing / channel estimation and DARP processing, and the other of these, called training sequence B, is timing / channel estimation. And used by the lower receiver to perform DARP processing. The training sequence used by the receiver must be consistent with the training sequence assigned and transmitted by the network. When a mobile station moves to a different service area to which a different training sequence is assigned, the mobile station changes the training sequence used accordingly.
Figure 11A shows a co-TCH (Simultaneous Traffic Channel) transmitter for downlink transmission. Most of the components are standard and will not be discussed in detail. The transmitter includes a training sequence repository 200 containing a targeted training sequence set and a new training sequence set generated using one of the methods described above. Typically, training sequences from the target set and training sequences from the new set are assigned to the transmitter during or during network configuration. In some embodiments, the training sequence thus assigned to a multi-user slot is used according to the method of FIG. 9A or 9B, to give some specific examples. The rest of the drawings are specific examples of signal generators for generating multi-user signals.
Figure 11B shows a pair of co-TCH (concurrent traffic channel) receivers for downlink transmission. Most of the components are standard and will not be discussed in detail. Each receiver contains a respective memory 210 containing a targeted training sequence set and a new training sequence set generated using one of the methods described above. One of these, called training sequence A, is used by the upper receiver to perform timing / channel estimation and DARP processing, and the other of these, called training sequence B, is timing / channel estimation. And used by the lower receiver to perform DARP processing. The training sequence used by the receiver must be consistent with the training sequence assigned and transmitted by the network. When a mobile station moves to a different service area to which a different training sequence is assigned, the mobile station changes the training sequence used accordingly.
Figure 12A shows a pair of OSC or co-TCH (concurrent traffic channel) transmitters for uplink transmission. Most of the components are standard and will not be discussed in detail. Each transmitter contains a training sequence repository 210 for a targeted training sequence set and a new training sequence set generated using one of the methods described above. The two transmitters use the same carrier frequency and the same time slot. This is similar to a network-generated multi-user signal, but in this case each component is generated at each mobile station rather than at a single transmitter. The training sequence used for uplink transmission is assigned by the network and changes as a given mobile station is passed to a different service area.
FIG. 12B shows a receiver consisting of two receivers for receiving each transmission from the mobile station pair of FIG. 12A in OSC or co-TCH uplink transmissions. Most of the components are standard and will not be discussed in detail. The receiver includes a training sequence repository 100 containing a targeted training sequence set and a new training sequence set generated using one of the methods described above. One of these, called training sequence A, is used by the upper receiver to perform timing estimation, joint channel estimation / detection, and the other of these, called training sequence B, is timing estimation, Used by the lower receiver to perform joint channel estimation / detection.
In some embodiments, the techniques described herein are used to generate training sequences for the GSM frame format described with reference to FIG. More generally, this technique can be applied to frame-format transmissions where the content for a given user contains at least each training sequence and payload (the payload simply contains any non-training sequence content). it can.
In all of the described embodiments, SNR degradation is used as an optimization criterion for optimizing the cross-correlation characteristics of the sequence. More generally, other optimization criteria can be used to optimize the cross-correlation characteristics of the sequence. Specific examples include: 1) Parameters related to the amplitude of the intercorrelation coefficient (maximum value, mean value, variance, etc.). 2) Simulation-based optimization. 3) Other correlation optimization criteria.
In some embodiments, each base station is encoded across a target training sequence set and a new training sequence set. For example, for transmission purposes, the base station training sequence repository 200 may be encoded across the target training sequence set and the new training sequence set.
In another embodiment, each base station, or more generally each transmitter, has at least one training sequence from the target training sequence set (eg, one of all of the training sequences, and a new training sequence. Encoded with at least one training sequence from a training sequence set (eg, one of all training sequences). A training sequence (s) from a new training sequence set is a targeted training sequence. A training sequence from a set (s) and a training sequence from a new training sequence set that is the best pair (s) may be included. In some embodiments, each base station is set up during network setup. Such a training sequence may be constructed.
Transmitters or receivers encoded with at least one training sequence from a set of target sets and at least one training sequence from a set of new training sequence sets also have a target training sequence set and new training. It may be encoded by one or more training sequences other than the sequence set.
In some embodiments, each receiver, eg, each mobile station, has at least one (eg, one or all) training sequence in the target training sequence set, and at least one of the training sequences in the new training sequence set. Encoded with one (eg one or all). For example, for transmission purposes, the mobile station training sequence repository 210 may be configured with at least one target training sequence and at least one new training sequence. Encoded in all of the training sequences of the target training sequence set and the new training sequence set, this allows the mobile station to include any training sequence (including multiple) from the target training sequence set and / or the new training sequence set. ) Can be transferred between the assigned base stations.
A transmitter or receiver encoded in a training sequence is a transmitter or receiver that is somehow stored by the transmitter or receiver and has a training sequence available to them. A transmitter or receiver, such as a base station or mobile station, that has a particular training sequence is a transmitter or receiver that can use a particular training sequence. This does not specify a valid step for storing the training sequence on the mobile station, but may be preceded by such a valid step. For example, it may be pre-stored during device configuration.
In light of the above teachings, a number of modifications and modifications of the present disclosure are possible. Therefore, within the scope of the appended claims, embodiments other than those specifically described herein can be practiced.
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Every citation, both waysCites: the store holds 0 of 1
| Reference | Relation |
|---|---|
| Research In Motion Ltd.,On Training Sequences for MUROS,3GPP TSG GERAN #39, Tdoc GP-081053,3GPP,2008年 8月25日,pp.1-7 | Non-patent |
| Motorola,MUROS Intra-Cell Interference and TSC Design,3GPP TSG GERAN #38, Tdoc GP-080602,3GPP,2008年 5月16日 | Non-patent |
| China Mobile,New series of training sequence codes for MUROS,3GPP TSG GERAN, GP-080785,3GPP,2008年 5月16日,pp.1-4 | Non-patent |
| China Mobile,New series of training sequence codes for MUROS,3GPP TSG GERAN, GP-080641,3GPP,2008年 5月16日,pp.1-3 | Non-patent |
| Xiang Chen et al.,GERAN Evolution: Multi-User Reusing One Slot to Improve Capacity,2009 International Conference on Communications and Mobile Computing,IEEE,2009年 1月 6日,Volume 1,pp.219-223 | Non-patent |
| Xiang Chen et al.,A scheme of Multi-User Reusing One Slot on Enhancing Capacity of GSM/EDGE Networks,International Conference on Communication Systems, 2008 (ICCS 2008),IEEE,2008年11月21日,pp.1574-1578 | Non-patent |
41 members in 17 offices
Priority claims9
| Document | Office | Kind | Date |
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| 61089712 | United States of America | – | |
| 8971208 | United States of America | P | |
| 8971208 | United States of America | P | |
| 2009001149 | Canada | W | |
| 2009001149 | Canada | W | |
| 2008089712 | – | – | – |
| 2009001149 | – | – | – |
| US20080089712P | – | – | – |
| WO2009CA01149 | – | – | – |
Members41
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| EP2157752A2 | European Patent Office (EPO) | A2 | |
| AU2009284653A1 | Australia | A1 | |
| CA2715286A1 | Canada | A1 | |
| WO2010020040A1 | World Intellectual Property Organization (WIPO) | A1 | |
| EP2157752A3 | European Patent Office (EPO) | A3 | |
| TW201021477A | Taiwan Province of China | A | |
| HK1141643A1 | Hong Kong, China | A1 | |
| MX2011001783A | Mexico | A | |
| KR20110039578A | Republic of Korea | A | |
| US7933355B2 | United States of America | B2 | |
| EP2319196A1 | European Patent Office (EPO) | A1 | |
| CN102132506A | China | A | |
| US2011176620A1 | United States of America | A1 | |
| JP2012500540A | Japan | A | |
| EP2157752B1 | European Patent Office (EPO) | B1 | |
| AT543311T | Austria | T | |
| ATE543311T1 | Austria | T1 | |
| DK2157752T3 | Denmark | T3 | |
| US8155226B2 | United States of America | B2 | |
| EP2442508A2 | European Patent Office (EPO) | A2 | |
| ES2381694T3 | Spain | T3 | |
| US2012163495A1 | United States of America | A1 | |
| ZA201101286B | South Africa | B | |
| PL2157752T3 | Poland | T3 | |
| EP2442508A3 | European Patent Office (EPO) | A3 | |
| JP5111664B2This record | Japan | B2 | |
| KR101237186B1 | Republic of Korea | B1 | |
| US8401101B2 | United States of America | B2 | |
| JP2013059039A | Japan | A | |
| TWI425796B | Taiwan Province of China | B | |
| AU2009284653B2 | Australia | B2 | |
| JP5422719B2 | Japan | B2 | |
| AU2009284653B8 | Australia | B8 | |
| CN102132506B | China | B | |
| CA2715286C | Canada | C | |
| BRPI0917300A2 | Brazil | A2 | |
| EP2319196A4 | European Patent Office (EPO) | A4 | |
| EP2442508B1 | European Patent Office (EPO) | B1 | |
| EP2319196B1 | European Patent Office (EPO) | B1 | |
| BRPI0917300B1 | Brazil | B1 |
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Numbers
- Publication
- 5111664
- Publication, DOCDB
- 5111664
- Publication, EPODOC
- JP5111664B
- Application
- 2011523277
- Application, DOCDB
- 2011523277
- Application, EPODOC
- JP20110523277
Titles2
- Japanese
- トレーニングシーケンス送信および受信のためのシステム、デバイスおよび方法
- English
- Systems, devices and methods for sending and receiving training sequences
Classification
- CPC, 4
- H04L25/0226
- H04B7/2643
- H04L25/0248
- H04L27/2089
- IPC, 1
- H04B1 76
