Method for detecting OTFS SCMA signal and receiver for implementing method

Abstract

A method of detecting a superimposed SCMA signal from a plurality of user equipments (UEs) in a receiver of an OTFS communication system includes initializing and performing an iterative loop in which a mean value of all posterior estimates of a transmission signal, determined so far, is calculated, these posterior estimates are based on OTFS demodulated received signals, corresponding channel matrices, and mean vectors and variances determined for each UE. The mean value of all posterior estimates so far determined is used to determine the vector and variance for each UE, further using the probability of non-zero elements in the respective UE codebook. Iterations are repeated until the termination standard is met.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and more particularly to wireless communications in systems using non-orthogonal sparse code multiple access (SCMA) and orthogonal time-frequency-space (OTFS) modulation, including wireless communications systems employing multiple-input multiple-output (MIMO) technology. More particularly, the present invention relates to detecting signals in such wireless communications systems in the presence of multi-dimensional interference, including inter-symbol interference (ISI), inter-Doppler interference (IDI), and inter-user interference (IUI), which are typically found in environments with high mobility settings having a large number of users.

[0002] Symbolic representation

[0003] Throughout this specification, boldface symbols denote vectors or matrices. Scalar values ​​are represented by italic lowercase letters, such as x. Superscripts T and H denote the transpose and complex conjugate transpose of a vector or matrix, respectively. Background Art

[0004] The sixth generation (6G) of wireless communications and subsequent technologies are expected to serve an increasing number of high-speed mobile users, such as vehicles, subways, highways, trains, drones, low-Earth orbit (LEO) satellites, etc.

[0005] Previous fourth- and fifth-generation (5G) wireless communications use orthogonal frequency division multiplexing (OFDM) technology, which offers high spectral efficiency, robustness against frequency-selective fading channels, and the use of low-complexity equalizers. However, high-speed mobile communications suffer from severe time and frequency dispersion due to speed-dependent Doppler shift or spread and rapidly changing multipath reception. Time and frequency dispersion can lead to inter-carrier interference (ICI) and signal fading at the receiver, hence the name doubly selective channel fading. Doubly selective channel fading can severely impair the performance of OFDM communications.

[0006] As an alternative to OFDM, OTFS modulation has been proposed as a solution to cope with doubly selective fading channels.

[0007] OTFS modulation is a 2D modulation scheme that multiplexes information-carrying QAM symbols onto a carrier waveform corresponding to localized pulses in a signal representation known as the Delay–Doppler representation. OTFS waveforms are spread in both time and frequency, but remain roughly orthogonal to each other under typical Delay–Doppler channel impairments. Theoretically, OTFS combines the reliability and robustness of spread spectrum with the high spectral efficiency and low complexity of narrowband transmission. OTFS exploits diversity from both channel delay and Doppler shift to achieve better performance. Due to the sparse nature of the channel in the Delay–Doppler domain, the pilot signals required for channel estimation and the receiver complexity can be significantly reduced, as described by P. Raviteja, K.T. Phan, and Y. Hong in “Embedded pilot-aided channel estimation for OTFS in delay–Doppler channels” (IEEE Transactions on Vehicular Technology, Vol. 68, No. 5, pp. 4906–4917, May 2019).

[0008] The OTFS waveform is coupled with the wireless channel in a way that directly captures the underlying physics, resulting in a high-resolution delay-Doppler radar image of the constituent reflectors. Thus, the time-frequency selective channel is transformed into an invariant, separable, and orthogonal interaction, where all received symbols experience the same local impairments and all delay-Doppler diversity branches are coherently combined.

[0009] This makes OTFS well-suited for wireless communications between a transmitter and receiver that are moving at high speed relative to each other, such as a receiver or transmitter located on a high-speed train, car, or even an airplane.

[0010] However, the equivalent transmission of OTFS in the delay-Doppler domain involves complex two-dimensional periodic convolution, which leads to severe inter-symbol interference. Therefore, an efficient yet simple signal detector is crucial for OTFS systems to maintain sufficient diversity in the wireless channel and achieve the required reception performance with reasonable effort.

[0011] In addition, to further improve spectral efficiency and transmission reliability in high-mobility scenarios, MIMO can be combined with OTFS. MIMO refers to a class of technologies that simultaneously send and receive multiple data signals on the same radio channel, using multiple transmit and receive antennas to exploit multipath propagation, thereby exponentially increasing the capacity of the radio link. In addition, modern MIMO usage typically refers to sending multiple data signals to different receivers with one or more receive antennas, although more accurately, this should be referred to as multi-user multiple input single output (MU-MISO).

[0012] Especially in vehicular communications, where a large number of users move rapidly at varying speeds and directions, requiring wireless access and communication, traditional access solutions such as Time Division Multiple Access (TDMA) can quickly become overloaded. This scenario is also known as massive multiple access.

[0013] To improve large-scale user connectivity, i.e., allowing a large number of users to access the same wireless resources simultaneously and improve spectrum utilization, non-orthogonal multiple access (NOMA) is considered a promising solution to achieve high spectral efficiency in traditional overloaded multi-user OFDM and MIMO systems. Existing NOMA methods are mainly divided into power-domain NOMA and code-domain NOMA to distinguish different users. As shown in A. Chatterjee, V. Rangamgari, S. Tiwari, and SSDas in “Nonorthogonal multiple access with orthogonal time-frequency space signal transmission” (IEEE Syst. J., Vol. 15, No. 1, pp. 383-394, March 2021) and Z. Ding, R. Schober, P. Fan, and H. V. Poor in “OTFS-NOMA: An efficient approach for exploiting heterogenous user mobility profiles” (IEEE Trans. Commun., Vol. 67, No. 11, pp. 7950-7965, November 2019), applying NOMA to OTFS can effectively improve spectrum utilization and support large-scale mobile connections. Specifically, sparse code multiple access (SCMA), a type of code-domain NOMA, can offer excellent performance and low receiver complexity, as discussed by K. Deka, A. Thomas, and S. Sharma in “OTFS-SCMA: A code-domain NOMA approach for orthogonal time-frequency-space modulation,” IEEE Trans. Commun., vol. 69, no. 8, pp. 5043–5058, August 2021. In OTFS-NOMA, multiple mobile users are allowed to simultaneously share the same delay-Doppler resource and are distinguished by different power levels or pass-through codes (e.g., sparse codewords).

[0014] The SCMA encoder maps log2(M) bits to a K-dimensional codeword of size M selected from a predefined codebook. The K dimensions correspond to K different orthogonal tones, such as OFDMA subcarriers. The K-dimensional codeword is a vector with only N < K non-zero entries. A user cannot transmit data through the subcarriers represented by the other N - K zero entries. In theory, each user can be assigned multiple codebooks, and each codebook can generally be used by multiple users. However, in this specification, it is assumed that each user uses only one SCMA layer.

[0015] Figure 1 An example of SCMA coding is shown, which has 6 codebooks CB1…CB6 (variable nodes) and 4 subcarriers SC1…SC4 (function nodes). Each row represents a dimension, and each column represents a 4-dimensional codeword. In each codebook, the constellation size is 4, which means there are 4 different codewords to choose from. White or empty entries represent zero elements in the codebook, and patterned entries represent non-zero elements. For example, in codebook 1, the entries in the first row are patterned and the entries in the third row are white, which means the first dimension is non-zero and the third dimension is zero. In each codebook, there are 2 non-zero dimensions with a patterned lattice. In an AWGN channel, the signal received at the base station is a superposition of codewords selected from the codebook, indicated by the combination pattern in each subcarrier.

[0016] Existing NOMA implementations perform poorly in managing the radio access of a group of mobile user equipment (UE) for wireless connections, especially in multi-user (MU) systems, including MU-MIMO systems. A major challenge faced by OTFS multi-user systems is the difficulty of data detection at the receiving end due to a significant increase in system dimensions. In addition, the additional inter-user interference burdens channel equalization, which requires high processing complexity and the processing at the receiving end is also very complex.

[0017] Figure 2 An exemplary scenario diagram of multiple wirelessly connected high-speed mobile users UE is shown. In the figure, multiple vehicles on the road are temporarily connected to a roadside unit (RSU) simultaneously, for example, through a wireless connection conforming to the IEEE802.11p / WAVE standard (shown by the zigzag lightning symbol). The RSU is connected to a central node and ultimately connected to the Internet through a suitable communication network (shown by the two-way arrow). The transmission requirements for modern vehicle safety communication and other uses are huge, and the mobility of the UE causes double-selective fading, that is, time selectivity caused by Doppler frequency shift and frequency selectivity caused by delay frequency shift. Although OTFS can at least partially solve the double-selective fading problem, in order to achieve the required performance in a MU system, at least an effective detector is required in the RSU. Summary of the Invention

[0018] Therefore, it is desirable to provide a method and a detector for effectively detecting signals from multiple users in an OTFS SCMA communication system in the presence of multi-dimensional interference including ISI, IDI, and IUI. Further, it is desirable to provide a method for receiving binary data sequences transmitted from multiple UEs in the form of OTFS SCMA signals in the presence of multi-dimensional interference including ISI, IDI, and IUI.

[0019] This need is addressed by the method for detecting superimposed OTFS SCMA signals from multiple UEs set forth in claim 1, the detector of claim 10, the method for receiving binary data sequences set forth in claim 11, the wireless receiver of claim 12, and the computer program product of claim 15. The corresponding computer-readable storage medium is set forth in claim 16. Embodiments and developments of the method and the device are provided in their respective dependent claims.

[0020] A MIMO OTFS SCMA system will be described below as an example, in which J independent mobile UEs transmit signals to a base station (BS) simultaneously. Figure 3 The corresponding exemplary schematic block diagram of the system is shown.

[0021] Without loss of generality, each UE 300 is equipped with one transmit antenna 312, and the BS is equipped with U receive antennas. Some different elements included in the UE 300 are shown in dashed boxes in the figure. For clarity, only one box is shown around the elements of multiple UEs.

[0022] In each transmission time slot, each log2Q information bit b from the j-th UE j is mapped in the SCMA mapper 302 to a K-dimensional sparse codeword selected from a user-specific SCMA codebook of size Q where j = {1, 2,..., J} and J > K usually results in an overload factor Assume that each UE uses only one SCMA layer, and also assume that only D (D < K) non-zero terms exist in the K-dimensional codeword c j The delay-Doppler plane of the j-th UE 300 is generated by non-overlappingly allocating the codeword c j along the delay axis or along the Doppler axis. M and N are the number of resource grids along the delay and Doppler dimensions, respectively. For simplicity, assume that M and N are integer multiples of K, i.e., [M] K = [N] K = 0, where [·] kThe transmission scheme of each UE 300 is based on OTFS to combat the double selective fading channel caused by channel delay and Doppler shift.

[0023] At the transmitter 300, the delayed Doppler symbols can be modulated in the OTFS modulator 306 by first Apply Inverse Symplectic Finite Fourier Transform (ISFFT) to convert it into time-frequency domain symbols for each UE 300 Thus, the time domain signal emitted in OTFS is obtained.

[0024]

[0025] in, and denote the normalized M-point and N-point fast Fourier transform (FFT) matrices, respectively.

[0026] Applying the Heisenberg transform and assuming a rectangular emission pulse g tx (t), the output of the Heisenberg transform can be expressed as

[0027]

[0028] in, and denote the normalized M-point and N-point fast Fourier transform (FFT) matrices, respectively.

[0029] Transmit time domain signal By S j Perform column vectorization to generate:

[0030]

[0031] Where T (seconds) and Δf = 1 / T (Hz) are selected to be larger than the maximum channel delay spread and the maximum Doppler shift, respectively. The system sampling interval is T s =1 / MΔf.

[0032] Then, in the CP adder 308, a cyclic prefix (CP) is added to the time domain signal generated for each UE 300. After passing through the transmit filter 310, the resulting time domain signal for each UE 300 is simultaneously transmitted through the dual selective fading channel. Figure 9 Also shown are the various modules or elements of the transmitter, which were referred to above by their reference numerals.

[0033] Refer again Figure 3, BS 400 receives signals from multiple UEs 300, filters the received signals in receive filter 404, removes the cyclic prefix added by the transmitter, and performs OTFS demodulation in OTFS demodulator 406. According to the present invention, the demodulated OTFS signal is provided to multi-user detection stage 408 to recover the signal of each UE 300. Finally, the SCMA codeword of each UE 300 is demapped in SCMA demapper 410, and the transmitted information is provided at output 412. The processing steps performed in the receiver or its components will now be described in more detail.

[0034] The impulse response channel characteristic between the jth UE 300 and the uth receiving antenna is

[0035]

[0036] Among them, L uj represents the number of multipaths between the jth UE 300 and the uth receiving antenna u, t j represents the timing offset experienced by the jth UE 300; h uj,i , τ uj,i and v uj,i Denote the channel gain, delay, and Doppler shift associated with the ith path, respectively. Doppler shift v uj,i It can be further expressed as v uj,i =(k uj,i +β uj,i ) / NT, where the integer k uj,i and real number β uj,i ∈(-0.5,0.5] represents v uj,i The exponential and decimal parts of .

[0037] In formula (3), P rc (·) is the equivalent total raised cosine (RC) roll-off filter when a typical root raised cosine (RRC) pulse shaping filter is applied at the transmitter and receiver. The maximum channel tap P uj Determined by the duration of the total filter response and the maximum channel delay spread. To overcome inter-frame interference, a CP is attached that is long enough to accommodate the maximum timing offset and maximum channel delay spread of all UEs 300.

[0038] As mentioned above, the received time domain signal first enters the receiving filter. After discarding the CP, the signal received from the jth UE 300 at the uth receiving antenna can be expressed as

[0039]

[0040] The signal obtained Devectorize to a matrix Then, by receiving the pulse g rx (t) Apply Wigner transform to generate time-frequency signal

[0041]

[0042] Finally, the delay-Doppler domain signal is obtained by applying the symplectic finite Fourier transform (SFFT),

[0043]

[0044] For simplicity, the above steps are for g tx (t) and g rx (t) uses a rectangular pulse, where the end-to-end delay from the jth UE 300 to the uth receiving antenna is modeled in the Doppler domain as follows:

[0045]

[0046] in

[0047]

[0048] The input-output model in formula (7) can be further expressed in vector form as

[0049]

[0050] in and is a sparse matrix.

[0051] Therefore, the signal received at the uth receiving antenna is

[0052]

[0053] Where u={1,2,…,U}, and

[0054] is the complex additive white Gaussian noise (AWGN) at the u-th receiving antenna.

[0055] It should be noted that is a sparse vector, and The number of non-zero entries in is only Indicates that the zero is removed and the The valid input vector after grouping every D non-zero entries of the same SCMA codeword in . Similar operations are applied to the corresponding columns in to obtain the effective matrix

[0056] Therefore, equation (10) can be rewritten as

[0057]

[0058] in and

[0059]

[0060] By stacking the receiving vectors in (11) as The input-output model of the MIMO-OTFS SCMA system is given as

[0061] y=Hx+ω, (12)

[0062] in and

[0063] For convenience, we define and

[0064] Signal detection can be performed using known linear receiver designs, such as the design proposed by P. Singh, A. Gupta, HBMishra, and R. Budhiraja in “Low-complexity ZF / MMSE MIMO-OTFS receivers for high-speed vehicular communication” (IEEE Open J. Commun. Soc., Vol. 3, pp. 209-227, 2022). It is also conceivable to use a more advanced receiver proposed by Y. Ge, Q. Deng, P. Ching, and Z. Ding in “OTFS signaling for uplink NOMA of heterogeneous mobility users” (IEEE Trans. Commun., Vol. 69, No. 5, pp. 3147-3161, May 2021). Other known receivers that can be used include low-complexity Gaussian approximate message passing (GMP) receivers with empirical damping for multi-user detection, such as proposed by P. Raviteja, KTPhan, Y. Hong and E. Viterbo in "Interference cancellation and iterative detection for orthogonal time frequency space modulation" (IEEE Trans. Wireless Commun., Vol. 17, No. 10, pp. 6501-6515, October 2018), or L. Xiang, Y. Liu, L.-L. Yang and L. Hanzo in "Gaussian approximate message passing detection of orthogonal time frequency space modulation" (IEEE Trans. Veh. Tech., Vol. 70, No. 10, pp. 10999-11004, October 2021).Another type of receiver that can be used includes an expectation propagation (EP) receiver, also with empirical damping for multiuser detection, as proposed by Y. Shan, F. Wang, and Y. Hao in "Orthogonal time frequency space detection via low-complexity expectation propagation" (IEEE Trans. Wireless Commun., 2022).

[0065] However, known receiver designs often have acceptable computational efficiency but low performance, or only provide acceptable performance at unacceptable computational complexity.

[0066] Therefore, according to the present invention, a low complexity memory approximate message passing (LCM-AMP) detector is proposed for recovering the signal of each UE from the signal received at the base station. The system model discussed above and finally developed into formula (12) can be represented by a factor graph, where each factor node y is connected to multiple variable nodes x c , where c = 1, 2,…, MNJ / K.

[0067] Figure 4 A schematic block diagram of the processing applied to the nodes of a factor graph is shown. At the factor nodes, operations including, for example, LMMSE estimation based on Taylor expansion approximation are performed. The operations at the factor nodes use information available at the BS 400, including intermediate results calculated by each UE 300 available in each iteration. The use of Taylor expansion approximation avoids the need to use matrix inversion, which has extremely high computational complexity, thereby improving the computational efficiency of the process. The operations at the factor nodes can be regarded as providing results similar to matched filters, but with low computational complexity. The results of the operations at the factor nodes are orthogonalized and provided to the variable nodes. At each variable node, operations including, for example, Bayesian denoising are performed, which use information related to the corresponding UE 300, including intermediate results calculated by the BS 400 available in each iteration. The results of the operations at the variable nodes are provided to the factor nodes after orthogonalization and damping processing, and the damping processing is used to improve the convergence of the process. According to the present invention, on the factor graph, the factor node y is parallel to the variable node x. c ,c=1,2,…,MNJ / K, the updated and transmitted messages are approximated as Gaussian distribution, which reduces the computational complexity compared to using exact messages.

[0068] Perform an iterative processing loop between factor node y and the corresponding variable node x c , until the termination criterion is met. The number of iterations is represented by the superscript (t).

[0069] From factor node y to variable node x c : At factor node y, determine the posterior estimate of the effective input vector The valid input vector is obtained after removing zeros from the same SCMA codeword and grouping all D non-zero elements. For example, the posterior estimate can be obtained by applying the linear minimum mean square error (LMMSE) criterion

[0070]

[0071] in and η t,t are the mean vector and variance received from the variable node in the (t-1)th iteration, respectively.

[0072] In order to avoid the complexity of the matrix inverse in Equation (13), the following lemma is introduced for simplicity.

[0073] Lemma 1. Assume that the matrix (IC) is invertible and the spectral radius C of C satisfies ρ(C)<1. Then

[0074] Lemma 2. From t = 1 and r (0) = 0, you can use the recursive process r (t) =Cr (t-1) +x to approximate

[0075] Based on Lemma 1 and Lemma 2, we define

[0076]

[0077] where θ t is the relaxation parameter used to ensure [I-θ t (ρ t I+HH H )] has a spectral radius less than 1. It has been verified that θ t =(λ + +ρ t ) -1 ,λ + =(λ max +λ min ) / 2 satisfies this condition, where λ max and λ min They are HH H In addition, the weight ξ is selected and optimized. t , to accelerate the convergence of the memory AMP detector. For convenience, define B = λ + I-HH H And get θt B=I-θ t (ρ t I+HH H ).

[0078] It should be noted that the calculation of λ max and λ min The complexity is as high as the complexity of the matrix inversion. L. Liu, S. Huang, and B.M. Kurkoski proposed a simple bound approximation for the maximum and minimum eigenvalues ​​that can be applied without losing performance in the 2022 IEEE Transactions on Information Theory, "Memory AMP."

[0079] From t=1 and Equation (13) can be approximately rewritten as

[0080]

[0081] Wherein, formula (15b) follows the recursive process of formula (14).

[0082] Definition A t =H H B t H and

[0083] From formula (15b), we can see that all previous messages {μ (t)} are both used for estimation. Therefore, the traditional orthogonality principle between the current input and output estimation errors applied in non-memory orthogonal approximate message passing (OAMP) and vector approximate message passing (VAMP), for example, proposed by T. Thaj and E. Viterbo in "Low-complexity linear diversity-combining detector for MIMO-OTFS" (IEEE Wireless Commun. Lett., Vol. 11, No. 2, pp. 288-292, February 2022), and expectation propagation (EP), for example, by Z. Ding, R. Schober, P. Fan and H. V. Poor in "OTFS-NOMA: An efficient approach for exploiting heterogenous user mobility profiles" (IEEE Trans. Commun., Vol. 67, No. 11, pp. 7950-7965, November 2019), it is not enough to guarantee the asymptotic independent and identically distributed (IID) Gaussianity of the estimation errors usually required in the memory AMP process. Therefore, a stricter orthogonality is required, that is, the current output estimation error must be orthogonal to the estimation errors of all previous inputs. According to the orthogonalization rule discussed in "Memory AMP" by L. Liu, S. Huang, and B.M. Kurkoski (IEEE Trans. Inf. Theory, 2022), the estimated external mean vector can be generated as follows

[0084]

[0085] in and

[0086] Its effectiveness can be demonstrated as follows:

[0087] According to the orthogonalization rule, for i <t,

[0088] Similarly, for i=t,

[0089] calculate Proof of completion.

[0090] Without loss of generality, the external mean vector r (t) It can be further expressed as

[0091]

[0092] in Next, the estimated external variance can be approximately calculated as

[0093]

[0094] in represents the expectation operation, and E t,i =φ t,i (a t-i IA t-i ). Further μ (i) The estimation error is defined as f (i) =μ (i) -x, where

[0095] Assume that the noise vector ω is (i)}, we can further simplify formula (17) to

[0096]

[0097] The following symbols are used:

[0098] and

[0099] Optimal parameter ξ t is achieved by minimizing τ t,t Obtained. Due to τ t,t (ξ t )About t Differentiable at point Except for Therefore, the optimal ξ t Either ±∞ or As a result, the optimal solution for can be set as And for t≥2,

[0100]

[0101] External mean and variance Finally, it is passed to the variable node x c ,c=1,2,…,MNJ / K。

[0102] From the variable node x c ,c=1,2,…,MNJ / K to factor node y: At each variable node, the posterior probability can be expressed as

[0103]

[0104] in and Indicates a round-up operation. is a set containing non-zero elements, and Is from D-dimensional code word. Represents the prior probability. If the receiver has no prior information, it assumes that all symbols have equal probability. The posterior probability is then projected onto a series of Gaussian distributions. Among them

[0105]

[0106] For simplicity, the simple mean of the variances is determined for further use, i.e. According to the Gaussian message combining rule adopted by Y. Ge, Q. Deng, P. Ching, and Z. Ding in “OTFS signaling for uplink NOMA of heterogeneous mobility users” (IEEE Trans. Commun., Vol. 69, No. 5, pp. 3147-3161, May 2021), the external variance and mean are updated and given by:

[0107]

[0108] therefore,

[0109] In order to ensure the convergence of the detector algorithm and improve the performance, The damping vector Λ is introduced under the constraint of t+1 =[Λ t+1,1 ,Λ t+1,2 ,…,Λ t+1,t+1 ] T Therefore, the variable node will be the mean vector μ (t+1) Further updated to

[0110]

[0111] Next, the external variance can be approximately updated by

[0112]

[0113] in V t =[η i,j ]t×t ,1≤i≤j≤t.

[0114] For 1≤t′≤t, we can calculate

[0115]

[0116] in equal The conjugation of .

[0117] Different from the heuristic damping method adopted in the prior art detector described above, the detector according to the present invention solves the optimization problem of equations (27a) and (27b) based on equation (24) to obtain the damping vector Λ t+1 The solution,

[0118]

[0119] st1 T Λ t+1 =1(27b)

[0120] Where 1 is an all-one vector. It is usually a semi-positive matrix. The problem expressed in formula (27) is a convex optimization problem and is easy to solve. It has been verified that the optimal solution is given by the following formula

[0121]

[0122] According to formula (28), the variance can be directly updated as follows

[0123] For 1≤t′≤t,

[0124]

[0125] Finally, μ (t+1) and η t+1,t′ ,1≤t′≤t+1 is passed back to the factor node.

[0126] It should be noted that the maximum damping length L (i.e. t+1 ), rather than being fully damped, where L = 3 or 2 is sufficient to achieve the desired performance.

[0127] When the expected convergence or the maximum number of iterations is reached The iteration in the memory AMP detector terminates when the posterior estimate of the transmitted signal x of the current iteration When the difference between the posterior variance and the immediately previous iteration is below a predetermined value, When the value is below a predetermined value, and / or when the a posteriori estimate of the transmitted signal x of the current iteration and the external mean of the current iteration When the difference between them is below a predetermined value, it can be considered that convergence is achieved.

[0128] Finally, a decision can be made on the transmitted symbols and SCMA demapping can be performed to recover the information bits transmitted by each UE.

[0129] The detection process between factor nodes and variable nodes can be simply summarized as

[0130] Input: y, H, λ min ,λ max , L and

[0131] initialization: λ + =(λ max +λ min ) / 2,

[0132] And the number of iterations t=1.

[0133] Repeat iteratively:

[0134] 1. Factor node y generates the external mean according to formula (16) Generate the external variance according to formula (18d) Then pass them to the variable node x c ,c=1,2,…,MNJ / K

[0135] 2. Variable node x c ,c=1,2,…,MNJ / K Calculate the mean vector μ according to formula (23) (t+1) , calculate the variance η according to formula (29) t+1,t′ ,1≤t′≤t+1, and pass them back to the factor node y

[0136] 3. t: = t + 1

[0137] until the termination criteria are met.

[0138] Then the decision result about each user's information bit is output.

[0139] Therefore, according to a first aspect of the present invention, a method for detecting superimposed SCMA signals from multiple UEs received wirelessly by a receiver of an OTFS communication system comprises: receiving an OTFS demodulated signal y representing the received signals from all UEs, and receiving a corresponding channel matrix H. If the SCMA signal is received by a multi-antenna receiver, the demodulated signal y represents the signals from all antennas of the multi-antenna receiver and all UEs. The method also includes performing an iterative loop after corresponding initialization to determine the signal transmitted by each UE. The iterative loop includes: determining a posterior estimate of the transmitted signal This process uses the demodulated signal y, the channel matrix H, and the mean vector μ determined for each UE in the immediately previous iteration. (t-1) and its associated variance η t,t-1 (or determined at initialization in the first iteration) as input. Next, based on all the posterior estimates determined so far (including the current iteration) Determine the external mean of the current iteration In addition, the external variance of the current iteration is determined Based on the external mean of the current iteration and the corresponding external variance of the current iteration (The average value can also be taken), which is the SCMA codebook for each UE and the corresponding UE Each non-zero element of determines the posterior probability of the current iteration Posterior probability Indicates the probability that an element in the SCMA codebook is one of the elements that are superimposed and form the received signal. and the corresponding variance Calculate the current iteration posterior Gaussian distribution for each UE and all non-zero elements in the SCMA codebook of the corresponding UE The current iteration posterior Gaussian distribution Then it is used to update the corresponding mean vector μ for each UE (t) and its corresponding external variance η t,t The iterative loop repeats until the termination criterion is met.

[0140] In one or more embodiments, initialization includes setting the first iteration mean vector μ (1) and the first iteration external mean Set to zero.

[0141] In one or more embodiments, initialization includes receiving a damping length L, a maximum number of iterations and / or the channel matrix and its complex conjugate transposed matrix (HH H )The minimum and maximum eigenvalues ​​λ of the product min ,λ maxDuring initialization, the number of iterations t can be set to 1, and / or λ + It can be set as the channel matrix and its complex conjugate transposed matrix HH H The smallest and largest eigenvalues ​​λ of the product min ,λ max The mean of .

[0142] In one or more embodiments, a posterior estimate of the transmitted signal x is determined This involves applying finite terms of matrix Taylor series to approximate the matrix inverse in linear minimum mean square error.

[0143] In one or more embodiments, the corresponding mean vector μ is updated (t) The elements and corresponding external variance η t,t Includes application of Gaussian message combining.

[0144] In one or more embodiments, the posterior probability of the current iteration The current iteration variance of Average to update the corresponding mean vector μ (t) The elements and corresponding external variance η t,t .

[0145] In one or more embodiments, the iteration is terminated after a predetermined number of times. Alternatively or additionally, when the a posteriori estimate of the transmitted signal x of the current iteration The iteration may be terminated when the difference between the value of the iteration and the immediately previous iteration is lower than a predetermined value. Another additional or alternative termination criterion may be that the posterior estimate of the transmitted signal x of the current iteration is and the external mean of the current iteration The difference between is below a predetermined value, or the posterior variance falls below a predetermined value.

[0146] In one or more embodiments, the damping length L is adjusted based on the SNR of the received signal. Specifically, the damping length can be reduced as the SNR decreases. This can reduce computational complexity when increasing the damping length does not result in an improvement in bit error rate (BER) within a certain number of iterations.

[0147] According to a second aspect of the present invention, a detector for a receiver is proposed, the receiver being configured to receive superimposed OTFS modulated SCMA signals from multiple UEs. The detector comprises one or more software and / or hardware blocks or modules configured to receive an OTFS demodulated signal y representing the received signals from all UEs, and a corresponding channel matrix H. The detector further comprises one or more software and / or hardware blocks or modules configured to initialize and execute an iterative loop that implements the repeated iterative steps of the method according to the first aspect of the present invention, for detecting the SCMA signal of each UE in the received superimposed OTFS modulated SCMA signal, and outputting the detected SCMA signal to an SCMA demapper.

[0148] According to a third aspect of the present invention, a method for receiving a binary data sequence simultaneously transmitted by multiple UEs as OTFS modulated SCMA signals includes: performing OTFS demodulation on the received OTFS signal. Channel estimation is then performed on the demodulated signal. The demodulated signal and the channel information obtained by the channel estimation are provided to a detection process, which detects superimposed SCMA signals from multiple UEs in the received signal according to the method of the first aspect of the present invention. The output of the detection process (i.e., the detected SCMA signal) is demapped, and binary data is reconstructed from the demapped detected SCMA signal.

[0149] The corresponding wireless receiver 400 of the OTFS communication system according to the fourth aspect of the present invention includes one or more antennas 402 for receiving superimposed OTFS modulated SCMA signals from multiple UEs via an OTFS communication channel. The received signal is provided to an OTFS demodulator module 404, which is configured to output a received signal (y) representing the signals received from all UEs. The demodulated OTFS signal is provided to a channel estimator module 406, which is configured to output an estimate of the channel coefficient to a signal detector module 408. The signal detector module 408 is configured to detect the SCMA signal from each UE and output the detected SCMA signal to an SCMA demapper module 410. The SCMA demapper module 410 provides the demapped signal to a channel decoder module 414, the output of which is a reconstructed version of the binary data sequence transmitted by each UE. Each module can be implemented in hardware and / or software. It is worth noting that the demodulator module 404, the channel estimator module 406, the signal detector module 408 and the SCMA demapper module 410 can be implemented as software executed by a microprocessor, hardware blocks or modules (e.g., dedicated computing hardware controlled by a microprocessor), or a combination thereof. Figure 9 The various modules referenced above are shown with their reference numbers.

[0150] The wireless receiver may include one or more microprocessors 450, volatile memory 452, and non-volatile memory 454, which are physically or logically interconnected with software or hardware or a combination thereof that implements the OTFS demodulator module 404, the channel estimator module 406, the signal detector module 408, the SCMA demapper module 410, and the channel decoder module 414. The non-volatile memory 454 stores computer program instructions that, when executed by the one or more microprocessors 450, configure the one or more microprocessors 450 to control the software or hardware blocks or modules or a combination thereof to perform the method according to the first or third aspect of the present invention. Figure 12 The various elements referred to above by their reference numerals are shown in FIG.

[0151] The methods described above can be represented by computer program instructions. Therefore, a computer program product includes computer program instructions that, when executed by a microprocessor of a receiver, cause the microprocessor to perform the methods according to the first or third aspects of the present invention and accordingly control the hardware and / or software modules of a receiver of an OTFS communication system according to the first or third aspects of the present invention as set forth above.

[0152] Computer program instructions can be stored or transferred in a retrievable manner on a computer-readable medium or data carrier. The medium or data carrier can be implemented in physical form, such as a hard disk, solid-state drive, flash memory device, etc. However, the medium or data carrier can also contain a modulated electromagnetic, electrical, or optical signal, which is received by the computer with the aid of a corresponding receiver and transferred to the computer's memory and stored there.

[0153] Since the equivalent channel matrix H is a sparse matrix and the memory AMP detector proposed in this paper only involves matrix-vector multiplication, the complexity of the detector is low and it can be called a low-complexity memory AMP (LCM-AMP) detector. Specifically, the complexity of the proposed LCM-AMP in each iteration is mainly determined by formulas (14), (15a), (20), (21), (22) and (26), which require complexity orders of and S B and S H denote the average number of non-zero entries in each row of B and H, respectively. The overall computational complexity of the proposed LCM-AMP is The computational complexity can be further reduced by replacing the large-scale matrix inversion with a finite number of terms in the matrix Taylor series.

[0154] In summary, Table 1 provides a detailed comparison of the complexity of known GMP, EP, OAMP / VAMP, and the proposed LCM-AMP detector. Clearly, the proposed LCM-AMP detector achieves comparable complexity to the GMP and EP detectors, though the performance of the GMP and EP detectors is slightly inferior. While the OAMP / VAMP detectors offer comparable performance, the LCM-AMP detector exhibits significantly lower computational complexity.

[0155]

[0156] The next section discusses the performance of the LCM-AMP detector in the proposed MIMO-OTFS SCMA system. Assume that the carrier frequency is centered at 4 GHz and the subcarrier spacing is Δf = 15 kHz. The RRC (root raised cosine) roll-off factor is set to 0.4 for both the transmitter and the receiver. Unless otherwise specified, the size of the delay-Doppler plane is M = 32 and N = 16. Further assume that J = 6 users simultaneously share K = 4 orthogonal resources and the base station (BS) has U = 4 receive antennas. In the simulation, the SCMA codebook was designed according to the method proposed in K. Xiao, B. Xia, Z. Chen, B. Xiao, D. Chen and S. Ma in "On capacity-based codebook design and advanced decoding for sparse code multiple access systems" (IEEE Trans. Wireless Commun., Vol. 17, No. 6, pp. 3834-3849, June 2018), with the size of each codeword being Q = 4 and the non-zero term D = 2. The typical urban channel model proposed in the 2017 3GPP TR 38.901 standard "Study on Channel Model for Frequencies From 0.5 to 100 GHz" was adopted, which has an exponential power delay distribution. The speed of each user was set to 300 km / h, corresponding to a maximum Doppler shift v max=1111 Hz. In addition, the channel Doppler shift is generated using the Jakes formula, as described in Y. Ge, Q. Deng, P. Ching, and Z. Ding, “Receiver design for OTFS with afractionally spaced sampling approach” (IEEE Trans. Wireless Commun., Vol. 20, No. 7, pp. 4072-4086, July 2021) or P. Raviteja, K. T. Phan, Y. Hong, and E. Viterbo, “Interference cancellation and iterative detection for orthogonal time frequency space modulation” (IEEE Trans. Wireless Commun., Vol. 17, No. 10, pp. 6501-6515, October 2018), i.e., in Uniformly distributed on [-π,π].

[0157] Firstly, the convergence of the proposed LCM-AMP detector and the influence of damping length on its performance are studied. Figure 5 The figure shows the bit error rate (BER) performance of the proposed LCM-AMP detector as a function of the number of iterations for different damping lengths L. It can be observed that the BER decreases monotonically and converges within a certain number of iterations. It can also be noted that there is no significant performance improvement for damping lengths L>3. Therefore, in the following simulations, for simplicity, we assume L=3 and

[0158] Figure 6 The figure further illustrates the impact of the eigenvalue bound approximation on the performance of the proposed LCM-AMP detector with varying numbers of BS antennas. Clearly, the proposed LCM-AMP with approximate eigenvalue bounds achieves similar performance to the LCM-AMP using exact eigenvalues—the diamonds and crosses represent approximate and exact eigenvalues, respectively, whose positions overlap and are difficult to distinguish. This strongly demonstrates the effectiveness of this approximation in memory AMP. It should also be noted that the BER performance improves with increasing the number of BS antennas, which is attributed to the additional spatial diversity.

[0159] Figure 7A comparison of the BER performance of different detectors for an exemplary MIMO-OTFS SCMA system is shown. The results show that the performance of all detectors improves with increasing signal-to-noise ratio (SNR). It can also be seen that the performance of traditional GMP and EP detectors is very sensitive to the damping parameter and can even lead to significant error flooring if no damping is applied. However, even with a low-complexity matched filter, the proposed LCM-AMP detector (referred to as Memory AMP in the figure) can achieve similar performance to the OAMP / VAMP detector—the diamonds and crosses of the two detectors in the figure overlap and are difficult to distinguish—and outperform the GMP and EP detectors. This analysis further confirms that the proposed LCM-AMP detector can achieve good performance with low complexity, thus bringing practical implementation advantages.

[0160] Figure 8 Figure 2 shows a comparison of the BER performance of the proposed LCM-AMP for different user speeds under different M and N settings. As user speed increases, BER performance initially improves slightly, then reaches saturation at speeds exceeding 300 km / h. This is because OTFS modulation can resolve more distinct paths in the Doppler domain to accommodate higher speeds, resulting in performance gains. It can also be observed that BER performance degrades with decreasing M and N, especially at higher SNRs. This is due to the loss of diversity caused by the reduced resolution of the OTFS delay-Doppler grid.

[0161] The proposed LCM-AMP is particularly well-suited for massive MIMO-OTFS SCMA systems with inherent channel sparsity. Replacing the traditional large-scale matrix inversion with a finite number of terms in the matrix Taylor series and restricting matrix-vector multiplications helps reduce complexity and improve computational tractability. Utilizing all preceding messages during the iteration process ensures that the estimation errors in the LCM-AMP are asymptotically independent and identically distributed (IID), thus maintaining orthogonality during the LCM-AMP detection process and helping to offset the performance degradation that may be caused by positive reinforcement during the iteration phase.

[0162] Unlike previous detector designs, the multi-user memory AMP detector proposed in this paper achieves a good balance between computational efficiency and receiver performance. This can reduce equipment costs and promote the application of MIMO-OTFS systems using SCMA. SCMA exhibits excellent characteristics in large-scale synchronous high-mobility communication environments and demonstrates superior spectral efficiency. In addition to the reduced detector complexity of this paper, the proposed optimized damping (fixed or dynamically variable) can further improve receiver performance.

[0163] The multi-user memory AMP detector proposed in this paper fully exploits the diversity in delay-Doppler and spatial domains to improve the performance.

[0164] The methods and devices proposed in this article can be widely used in mobile communications (such as vehicle-to-everything (V2X)), high-speed railway communication systems, low-Earth orbit (LEO) satellite communications, unmanned aerial vehicle (UAV) communications (especially in swarm settings), massive machine-type Internet of Things (IoT) communications (such as in wireless factories and other industrial environments), very large-scale MIMO and OFDM systems, and even underwater acoustic communications.

[0165] Although the present invention has been described herein using an exemplary MIMO OTFS SCMA system, it will be apparent to those skilled in the art who read and understand this specification that the principles and methods developed and proposed above are also applicable to a general MU system using a single antenna at the receiver and / or transmitter. BRIEF DESCRIPTION OF THE DRAWINGS

[0166] The figures in the accompanying drawings are used to illustrate various aspects of the present invention in detail.

[0167] Figure 1 An example of SCMA coding with a 6-layer codebook and 4 subcarriers is shown,

[0168] Figure 2 This is an exemplary schematic scenario of multiple high-speed mobile UEs with wireless connections.

[0169] Figure 3 FIG. 4 shows an exemplary schematic block diagram of a MIMO OTFS SCMA system,

[0170] Figure 4 1 shows a schematic block diagram of an iterative process applied in a Low Complexity Memory Approximate Message Passing (LCM-AMP) detector according to one aspect of the present invention,

[0171] Figure 5 The figure shows the relationship between the bit error rate performance of the proposed LCM-AMP detector and the number of iterations under different damping lengths.

[0172] Figure 6 The effect of eigenvalue bound approximation on the performance of the proposed LCM-AMP detector with different numbers of BS antennas is shown.

[0173] Figure 7 shows the BER performance comparison of different detectors for an exemplary MIMO-OTFS SCMA system,

[0174] Figure 8 shows the BER performance comparison of the proposed LCM-AMP for different user speeds under different M and N settings,

[0175] Figure 9A further representation of an exemplary schematic block diagram of a MIMO OTFS SCMA system is shown,

[0176] Figure 10 FIG2 shows a flow chart of a method for detecting superimposed SCMA signals from multiple UEs in a receiver of an OTFS communication system according to the first aspect of the present invention.

[0177] Figure 12 An exemplary block diagram of a receiver according to a fourth aspect of the present invention is shown.

[0178] In the drawings, the same or similar elements may be denoted by the same reference numerals. DETAILED DESCRIPTION

[0179] Figures 1 to 9 This has been described in detail above and will not be repeated here.

[0180] Figure 10 An exemplary flow chart of a method 100 for detecting superimposed SCMA signals received wirelessly from a plurality of UEs 300 by a receiver 400 of an OTFS communication system according to the first aspect of the present invention is shown. After receiving an OTFS demodulated signal y representing the signals received from all UEs 300 in step 110 and receiving the corresponding channel matrix H in step 112, an initialization step 120 is performed. After initialization is complete, steps 130 to 170 are iteratively repeated. In step 130, the demodulated signal y, the channel matrix H, and the corresponding mean vector μ determined for each UE 300 in the immediately preceding iteration are used. (t-1) and associated variance η t,t-1 (or determined for each UE at initialization in the first iteration t=1) as input, determine the a posteriori estimate of the transmitted signal x Next, in step 140, based on all the posterior estimates determined so far and the corresponding external variance of the current iteration Determine the external mean of the current iteration In step 150, the SCMA codebook of each UE 300 and the corresponding UE 300 is determined. The current iteration posterior probability of each non-zero element of This determination is based on the external mean of the current iteration and the corresponding external variance of the current iteration Next, in step 160, for each UE 300 and its SCMA codebook Calculate the posterior probability of the current iteration on all non-zero elements The current iterative posterior Gaussian distribution of and the corresponding variance Finally, in step 170, based on the posterior Gaussian distribution, the corresponding mean vector μ is updated for each UE 300. (t) The elements and corresponding external variance η t,t In step 180 , a check is performed to determine whether the termination criteria are met. In the negative “No” branch of step 180 , step 130 is iteratively repeated. In the affirmative “Yes” branch of step 180 , the detected SCMA signal is output to the receiver 400 for further processing, e.g., for SCMA demapping and subsequent channel decoding.

[0181] Figure 11 An exemplary flow chart of a method 200 according to the third aspect of the present invention is shown. An OTFS-modulated SCMA signal is received in step 202 and OTFS demodulated in step 204. After channel estimation in step 206, the process 100 of detecting superimposed SCMA signals from multiple UEs 300 is performed. Once the process 100 terminates, the detected SCMA signal is demapped in step 208, and the binary data carried therein is reconstructed in step 210.

[0182] Figure 12 An exemplary block diagram of a receiver 400 according to the present invention is shown. The receiver 400 includes one or more microprocessors 450, volatile memory 452, non-volatile memory 454, and wireless interface circuitry 456 configured to communicate with the transmitter 300 by receiving electromagnetic signals via the plurality of antennas 402. The aforementioned elements are communicatively coupled via one or more signal or data connections or buses 458. The non-volatile memory 454 stores computer program instructions that, when executed by the microprocessor 450, cause the receiver 400 to perform the method according to the first or third aspect of the present invention as set forth herein.

[0183] List of Reference Signs (Part of the Description)

[0184] 100 Detection Method 204 OTFS Demodulation

[0185] 110 Receive OTFS demodulated signal 206 Channel estimation

[0186] 112 Receive channel matrix 208 Demapping

[0187] 114 Receiving further input 210 Reconstruction

[0188] 120 Initialize and execute iteration 300 Transmitter / UE

[0189] Ring 302 SCMA Mapper

[0190] 124 The mean of the first iteration is sent to the 304 SCMA codeword distributor

[0191] The amount and external mean are set to zero 306 OTFS modulator

[0192] 130 Determine the a posteriori 308 CP adder of the transmitted signal

[0193] Estimated value 310 Transmit filter

[0194] 140 Generates receive vector for external 312 antenna

[0195] Average 400 Receiver / Base Station

[0196] 150 Determine the posterior probability 402 antenna

[0197] 160 Calculate the posterior height of each UE 404 receiving filter

[0198] OTFS Demodulator

[0199] 170 Update mean 408 LCM-AMP detector for each UE

[0200] Vector Elements 410 SCMA Demapper

[0201] 180 Does it meet the termination criteria? 412 Output

[0202] 190 Output detected SCMA 450 Microprocessor

[0203] Signal 452 Volatile Memory

[0204] 200 Receiving Method 454 Non-volatile Memory

[0205] 202 Receive OTFS modulation SCMA 456 Wireless interface circuit

[0206] Signal 458 Signal / Data Connection / Bus.

Claims

1. A method (100) for detecting superimposed sparse code multiple access signals from a plurality of user equipments (300), the signals being wirelessly received by a receiver (400) of an orthogonal time-frequency space communication system, the method comprising: - receiving (110) an orthogonal time-frequency-spatial demodulated signal (y) representing the received signals from all user equipments (300), - receiving (112) the corresponding channel matrix (H), - Initialize (120) and execute an iterative loop, said iterative loop comprising: a) using the demodulated signal (y), the channel matrix (H) and the corresponding mean vector (μ) determined for each user equipment (300) in the corresponding immediately preceding iteration (t-1) ) and its associated variance (η t,t-1 ), or in a first iteration (t=1) taking as input the corresponding mean vector and its associated variance determined at initialization (120) for each user equipment (300), determining (130) a posterior estimate of the transmitted signal (x) b) Based on all the posterior estimates determined so far and the corresponding external variance of the current iteration Generate (140) the current iteration external mean c) Based on the external mean of the current iteration and the corresponding external variance of the current iteration A sparse code multiple access codebook for each user equipment (300) and a corresponding user equipment (300) Each non-zero element of determines (150) the posterior probability of the current iteration d) a sparse code multiple access codebook for each user equipment (300) and the corresponding user equipment (300) Calculate the posterior probability of the current iteration (160) on all non-zero elements in The current iterative posterior Gaussian distribution of and the corresponding variance e) updating (170) the corresponding mean vector (μ) for each user device (300) based on the posterior Gaussian distribution (t) ) and the corresponding external variance (η t,t ), and - Repeat steps a) to e) until the termination criteria are met.

2. The method according to claim 1, wherein Initialization (120) involves converting the first iteration mean vector (μ (0) ) and the first iteration external mean Set to zero, and / or set the external variance (η 0,0 ) is set to 3. The method (100) according to claim 1 or 2, wherein: Initialization (120) includes receiving the damping length (L), the maximum number of iterations and / or the channel matrix and its complex conjugate transposed matrix (HH H ) of the product of the minimum and maximum eigenvalues ​​(λ min ,λ max ) as further input.

4. The method (100) according to claim 3, wherein: Initialization (120) includes setting the iteration count (t) to 1, and / or setting the positive relaxation eigenvalue parameter (λ + ) is set to the channel matrix and its complex conjugate transposed matrix (HH H ) of the product of the minimum and maximum eigenvalues ​​(λ min ,λ max )’s average value.

5. The method (100) according to any one of the preceding claims, wherein Determine (130) a posterior estimate of the transmitted signal (x) This involves applying finite terms of the matrix Taylor series to approximate the matrix inverse in linear minimum mean square error.

6. The method (100) according to any one of the preceding claims, wherein: Updating (170) includes applying Gaussian message combining.

7. The method (100) according to any one of the preceding claims, wherein: In order to update the corresponding mean vector (μ (t) ) and the corresponding external variance (η t,t ), the posterior probability of the current iteration The current iteration variance of Perform averaging.

8. The method (100) according to any one of the preceding claims, wherein: After a predetermined number of iterations, when the posterior estimate of the transmitted signal (x) of the current iteration is When the difference between the iteration and the immediately previous iteration is below a predetermined value, when the posterior variance falls below a predetermined value, and / or when the a posteriori estimate of the transmitted signal (x) of the current iteration External average with current iteration The iteration is terminated when the difference between is below a predetermined value.

9. The method (100) according to any one of the preceding claims, further comprising dynamically adjusting the damping length (L) based on a signal-to-noise ratio of the received signal.

10. A detector (408) of a receiver (400), the receiver being arranged to receive superimposed orthogonal time-frequency-space modulated sparse code multiple access signals from a plurality of user devices (300), the detector (408) comprising one or more software and / or hardware blocks or modules, the one or more software and / or hardware blocks or modules being configured to: receive an orthogonal time-frequency-space demodulated signal (y) representing the received signals from all user devices (300), receive a corresponding channel matrix (H), initialize and execute an iterative loop implementing steps a) to e) of the method according to one or more of claims 1 to 9, detect the sparse code multiple access signal of each user device (300) in the received superimposed orthogonal time-frequency-space modulated sparse code multiple access signal, and output the detected sparse code multiple access signal to a sparse code multiple access demapper (410).

11. A method (200) for receiving a binary data sequence transmitted simultaneously by a plurality of user equipments (300) as orthogonal time-frequency-space modulated sparse code multiple access signals, the method comprising: a) subjecting the received orthogonal time-frequency-space signal to (204) orthogonal time-frequency-space demodulation, b) performing (206) channel estimation on the demodulated orthogonal time-frequency-space signals, c) detecting (100) the superimposed sparse code multiple access signal by applying the method according to any one or more of claims 1 to 9, d) demapping the detected sparse code multiple access signal (208), and e) reconstructing (210) binary data based on the demapped detected sparse code multiple access signal.

12. A wireless receiver (400) of an orthogonal time-frequency space communication system, the wireless receiver comprising one or more antennas (402) for receiving superimposed orthogonal time-frequency space modulated sparse code multiple access signals from a plurality of user devices (300) via an orthogonal time-frequency space communication channel, the received signals being provided to an orthogonal time-frequency space demodulator (404), the orthogonal time-frequency space demodulator being configured to output a received signal (y) representing the received signals from all the user devices (300) to a channel estimator (406), the channel estimator The device (406) is configured to output an estimate of the channel coefficient to a signal detector (408) according to claim 10, the signal detector (408) being configured to detect a sparse code multiple access signal from each user equipment (300), and to output the detected sparse code multiple access signal to a sparse code multiple access demapper (410), the sparse code multiple access demapper (410) providing the demapped signal to a channel decoder (414), the output of the channel decoder being a reconstructed version of the binary data sequence transmitted by the corresponding user equipment (300).

13. The wireless receiver (400) of claim 12, wherein: The orthogonal time-frequency-space demodulator (404), the channel estimator (406), the signal detector (408), the sparse code multiple access demapper (410) and / or the channel decoder (414) are implemented as software or hardware blocks or modules, or a combination thereof.

14. The wireless receiver (400) of claim 12 or 13, further comprising one or more microprocessors (450), volatile memory (452), and non-volatile memory (454) physically or logically interconnected with software or hardware blocks or modules or combinations thereof implementing the orthogonal time-frequency-space demodulator (404), the channel estimator (406), the signal detector (408), the sparse code multiple access demapper (410), and the channel decoder (414), wherein: The non-volatile memory (454) stores computer program instructions which, when executed by the one or more microprocessors (450), configure the one or more microprocessors (450) to control the software or hardware blocks or modules or a combination thereof to perform the method according to one or more of claims 1 to 9 or according to claim 11.

15. A computer program product comprising computer program instructions which, when executed by a microprocessor, cause a computer and / or control hardware block, module or component of a detector (408) according to claim 10 or a receiver (400) according to one or more of claims 12 to 14 of an orthogonal time-frequency space transmission system to perform a method (100, 200) according to one or more of claims 1 to 9 or according to claim 11, respectively.

16. A computer-readable medium or data carrier for retrievably transmitting or storing the computer program product according to claim 15.

17. A wireless communication device (400) comprising one or more microprocessors (450), volatile memory (452) and non-volatile memory (454), and wireless interface circuitry (456) configured to transmit and / or receive electromagnetic signals via one or more antennas (402), wherein: The non-volatile memory (454) stores computer program instructions which, when executed by the microprocessor (452), configure the wireless device (400) to perform a method according to one or more of claims 1 to 9 or according to claim 11.

Images