We consider the turbo frequency domain equalization (FDE) approach based on expectation propagation (EP) to the single-carrier with index modulation (SCIM) transmission over frequency-selective channels. The SCIM-FDE system implements vector-by-vector signal transmission in the time domain, in which elements within each transmitted symbol vector display correlation. In this paper, we propose a novel vector-wise EP (VW-EP) equalization, where the discrete prior distribution of each symbol vector is approximated with a multivariate complex Gaussian distribution. Hence the extrinsic information in the EP feedback is extended to be in terms of the mean vector and covariance matrix. The effectiveness of the proposed scheme can be validated through the error-correction performance and extrinsic information transfer (EXIT) analysis. Numerical results show that the SCIM-FDE transmission with the VW-EP turbo equalization demonstrates remarkable performance.

With the widespread adoption of massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) communication techniques, the overhead of beam measurement and the complexity of beam management become even more severe issues due to the dramatic increase of the number of beams. Traditional methods often overlook the selection of optimal probing beams, thus limiting beam prediction performance. Additionally, existing solutions based on deep learning exhibit high complexity, which often hinders their practical deployment. In this work, we propose a lightweight, integrated neural network approach tailored for joint probing-beam pattern selection and beam prediction. Specifically, our solution includes two main components. First, formulating the selection of probing beams as a sampling operation, we envision a sampling network where, to enable gradient back-propagation of network parameters in spite the non-differentiable nature of sampling, the standard sampling function is approximated with a fitting function. Then, drawing inspiration from the physical structure of antenna arrays and 3D beam formation, we develop a beam-prediction network based on convolutional neural networks and self-attention mechanisms. Experimental results demonstrate that, thanks to the learned pattern, our proposed scheme achieves very good prediction performance (exceeding the state of the art by over 15% in top-1 accuracy), using a neural network with almost 90% less parameters than existing machine learning-based solutions.

The multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) has emerged as a promising scheme for high-mobility wireless communications. The unitary approximate message passing (UAMP) detection has demonstrated outstanding performance in the single-input single-output (SISO)-OTFS system. However, We found that directly employing the UAMP detection for the MIMO-OTFS system results in performance degradation and the emergence of an error floor. Therefore, we propose a minimum mean square error (MMSE)-aided UAMP scheme, incorporating MMSE to assist UAMP by providing the prior information. Notably, we reduce the complexity by setting a signal-to-noise ratio (SNR) threshold, only when SNR exceeds the threshold shall we start the MMSE-aided mechanism. Simultaneously, we analyze the impact of fractional delay and consider it into system model, aiming to optimize the practicality of the system. Simulation results under fast-fading Rayleigh channels demonstrate that the proposed MMSE-aided UAMP significantly outperforms the original UAMP in the medium to high SNR range.

In this paper, we introduce a deep learning-based beamspace channel estimation approach that better exploits the inherent sparsity of the mmWave MIMO channel. On one hand, we replace conventional complicated phase shifter networks with switch-based selection networks, whose sparse connectivity is more adapted to the sparsity of mm Wave channels. On the other hand, we propose an attention-Unet model for accurate beamspace channel estimation. The architecture comprises an encoder-decoder structure with attention mechanism. By selectively focusing on the dominant part, the attention mechanism can further capture the sparsity of the beamspace channel. Simulation results demonstrate that the proposed approach outperforms the existing phase shifter-based techniques under both the widely used Saleh-Valenzuela channel model and the open-source DeepMIMO dataset based on ray-tracing.

This letter explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing. Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that embed into GEC-SR a quick inversion of the quasi-banded matrices, thus reducing the algorithm's complexity from cubic order to linear order, while keeping the performance at almost the same level.

In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.

A novel coexisting passive reconfigurable intelligent surface (RIS) and active decode-and-forward (DF) relay assisted non-orthogonal multiple access (NOMA) transmission framework is proposed. In particular, two communication protocols are conceived, namely Hybrid NOMA (H-NOMA) and Full NOMA (F-NOMA). Based on the proposed two protocols, both the sum rate maximization and max-min rate fairness problems are formulated for jointly optimizing the power allocation at the access point and relay as well as the passive beamforming design at the RIS. To tackle the non-convex problems, an alternating optimization (AO) based algorithm is first developed, where the transmit power and the RIS phase-shift are alternatingly optimized by leveraging the two-dimensional search and rank-relaxed difference-of-convex (DC) programming, respectively. Then, a two-layer penalty based joint optimization (JO) algorithm is developed to jointly optimize the resource allocation coefficients within each iteration. Finally, numerical results demonstrate that: i) the proposed coexisting RIS and relay assisted transmission framework is capable of achieving a significant user performance improvement than conventional schemes without RIS or relay; ii) compared with the AO algorithm, the JO algorithm requires less execution time at the cost of a slight performance loss; and iii) the H-NOMA and F-NOMA protocols are generally preferable for ensuring user rate fairness and enhancing user sum rate, respectively.

This paper investigates an integrated passive reconfigurable intelligent surface (RIS) and active relay assisted non-orthogonal multiple access (NOMA) system. To unleash the potential of distant user based on the downlink NOMA protocol, we identify a two-stage transmission strategy. In both stages, RISs provide coverage for paired users by actively modifying the channel response, and in the second stage, we introduce a dedicated active relay to realize the communication between the base station (BS) and distant user. Our goal is to maximize the system sum rate by jointly optimizing the power allocation at the BS and the passive beamforming at the RIS. To tackle the formulated non-convex problem, we propose an alternating penalty-based based algorithm. In particular, for the RIS phase-shift reconfiguration, a difference-of-convex (DC) approach is utilized to accurately detect the feasibility of the rank-one constraint. Numerical results demonstrate that: i) the integrated transmission scheme outperforms other baseline schemes; ii) the proposed scheme is capable of significantly enhancing the performance of distant NOMA users.

At the present stage, most of the packet header detection algorithms in massive access are to solve the problem of multi-user collision in the additive white gaussian noise (AWGN) channel. On the other hand, many algorithms do not consider that the number of user collisions is unknown and varies randomly. First, we propose an adaptive algorithm based on correlation. Secondly, based on the coordinate ascent variational inference (CAVI) algorithms, we present an improved header detection method in fading channels. Compared with the traditional packet head detection method based on compressive sensing reconstruction algorithm, this improved method solves the problem that the number of users who collide is unknown, and it also has good performance in fading channels. In addition, the method can also cope with the random change of the number of users in collision.

This paper proposes an improved version of concatenated code scheme in massive random access, with performance enhancement and low-complexity implementation. Under theoretical idealization, most existing solutions only consider improving energy efficiency, ignoring the cost of implementation complexity. However, both of them are vital when it comes to practical realization. Although the concatenated code system is simple to implement, its performance is relatively weak, and the equivalent mapping detection method adopted at the receiving end causes performance loss. Therefore, based on concatenated code system, this paper makes performance improvements in the following three aspects: (i) using multi-level decisions to avoid loss of information; (ii) a priori information aided detection utilizing the distribution of multi-level symbols to minimize the error probability of detection; (iii) soft information output to enhance soft decoders at the next stage. Meanwhile, in decoding complexity, the improved algorithm retains the low complexity feature of the concatenated code system and achieves linear implementation. This scheme successfully enhances energy efficiency at a lower complexity cost by improving the detection algorithm. We prove that the proposed method is an implementation-oriented massive random access with advantages in energy efficiency and performance by theory and simulation.

Cell-free (CF) millimeter-wave (mmWave) Massive multiple-input multiple-output (MIMO) will be a promising technology in the next-generation mobile communication system. Traditionally, beam selection is widely studied in the single-cell scenario for analog beamforming under hybrid beamforming architectures. Meanwhile, there is also a great necessity to study beam selection in CF systems featuring multiple access points (APs). In this paper, we propose two beam selection schemes for the hybrid CF mmWave massive MIMO systems. First, by suppressing both the intra-AP and inter-AP interference of beams, the proposed capacity maximization-based selection scheme (CM-S) aims to maximize the system sum rate. Second, utilizing the proportional fairness (PF) scheduling algorithm, our proposed scheme of joint user scheduling and beam selection (JDSBS) iteratively performs user selection and beam selection in several continuous slots, which reduces the user's outage probability by more than 10 times with little performance loss. Finally, the simulation results show the advantages of the proposed schemes and indicate that our proposed schemes can achieve better performance when users are dense.

Outdated channel information degrade capacity performance in limited feedback precoded MIMO-OFDM system. Channel prediction is a flexible way to combat channel aging without the cost of scarce time-frequency resources. Long short term memory (LSTM) is a class of neural networks, capable of processing data that has long-term dependence in time series. In this paper, an LSTM-based channel predictor is designed to achieve a longer range of predictions. Precoding matrix indicator (PMI) and rank indicator (RI) are two indicators related to precoding and the number of precoding matrices forming the codebook increased with the number of antenna ports. Furthermore, we try to calculate PMI/RI with the aid of the neural network. Through the simulation experiment, we test the performance of the LSTM channel predictor and the PMI/RI selector. The result illustrates that our predictor achieves considerable channel capacity gain and our selector shows high accuracy. Moreover, the generalization ability of the LSTM predictor for different antenna configurations is studied.

Due to the mobility and flexibility of unmanned aerial vehicle (UAV), it has been widely used in data collection. This article considers multiple UAVs to collect data from multiple sensor nodes (SNs) with the interference. In order to ensure the timeliness of the collected data, we jointly optimize the trajectory of the UAVs and the wake-up time allocation as well as the transmit power of the SNs for minimization of the mission completion time. The formulated problem is non-convex with continuous variables which is difficult to solve. In order to solve this problem, we consider time discretization directly, then use bisection search to turn it into a finite variable problem and use the successive convex approximation and alternating optimization techniques to solve it. In our research, we initialized the UAV's trajectory with tangent circles to speed up the convergence of algorithm. The simulation results show that the proposed method complete the missions of data collection better than benchmark schemes.

Recently, the indoor positioning system based on the angle of arrival (AOA) has been widely concerned. The positioning system often contains two steps. The first step is the AOA estimation algorithm and the second step is the positioning algorithm using the estimated AOAs. However, due to the indoor signal multipath propagation, the AOA estimation errors can lower the positioning performance. To solve this problem, this paper proposes a novel angle of arrival (AOA) positioning algorithm which selects multiple candidate AOAs and selects the most reliable one from them. When multiple candidate AOAs are selected and passed into the positioning algorithm, multiple estimated locations can be obtained. To select the most reliable AOA, the location reliability prior information related to these estimated locations is introduced. The AOA with the maximum location reliability prior information is the final AOA estimation result and its corresponding estimated location is the final positioning result. The simulation results show that compared with the positioning algorithm which directly uses one AOA for positioning, the proposed positioning algorithm provides better positioning performance.

In this paper, a multi-scale convolutional neural network-based (MSN) method is proposed for robust automatic modulation classification (AMC). The classifier directly utilizes in-phase and quadrature (I/Q) samples to identify the modulation type of received signal without any data preprocessing, thereby reducing the computational complexity. Further, the network architecture employs one-dimensional convolution (Conv1D) to extract multi-scale feature maps due to its merits of low computational complexity. Then these multi-scale feature maps are merged together by repeated multi-scale fusions, in order to improve the classification accuracy performance and the robustness to varying SNR environment. Repeated multi-scale fusions can make better use of amplitude-phase information because it can learn the local changes brought by modulation as well as the timing characteristics of the samples. Simulation results show that proposed MSN achieves classification rate of 97.38% classification accuracy at high SNR regimes for 24 different modulation types on the public well-known over-the-air (OTA) dataset. Moreover, MSN still can recognize the modulation types of received signals with the accuracy rates of about 95% under varying SNR scenarios. Compared to the methods proposed in other papers, our classifier not only shows a better performance in terms of classification accuracy, but also is the most robust in varying SNR environment.

A good quantization scheme plays a tremendously important role in the hardware implementation of the decoder for polar codes. In this paper, a non-uniform quantization scheme for cyclic redundancy check aided successive-cancellation list (CA-SCL) decoder of polar codes is proposed to save memory resources. In the proposed non-uniform quantization scheme, we introduce a simple compression function to decrease the truncation threshold so that the soft information can be quantized with fewer quantization bits. The scaling factor in the compression function is a negative integer power of two, which can be efficiently implemented by shift operations. Then a compressed-domain CA-SCL decoder is derived, alleviating the need to decompress samples one-by-one. Simulation results show that on the condition of approaching floating-point performance, the internal log-likelihood ratio (LLR) memory resource consumption can be saved by 20% compared with uniform quantization.

The positioning algorithm currently in wireless networks is a separate estimation problem. The first step is to estimate the delay of each link to obtain the time-of-arrival (TOA) information. The second step is to pass the TOA into the positioning algorithm to get position estimation. However, the transmission of the signal between the user equipment (UE) and the base station (BS) is inevitably affected by non-line-of-sight (NLOS) transmission. At this time, the estimation of delay may not be the true TOA, resulting in the positioning performance is greatly reduced. To solve this problem, this paper proposes a Joint Approximate Maximum Likelihood (JAML) positioning algorithm, which combines delay estimation and position estimation, in 5G New Radio (NR) Systems. When the delay estimation is inaccurate, JAML reselects the correct delay estimation as TOA information for position estimation. We use a channel model of 5G NR in the simulation, and simulation results show that the JAML positioning algorithm improves the positioning performance.

In millimeter-wave (mmWave) communication systems, hybrid beamforming is regarded as an effective way to increase the spectral efficiency of the massive multiple-input multiple-output (MIMO) system. Assuming perfect channel state information (CSI) is known at the transmitter, we focus on a downlink massive multi-user MIMO system which supports multi-stream per user. In the above scenario, we investigate the hybrid beamforming problem with strong correlation between users' channels, where the existing schemes have performance loss. To tackle this problem, this paper proposes the channel correlation cancelation-based hybrid beamforming (CCCHB) algorithm which considers the correlation between channels and decomposes the optimization of overall spectrum efficiency of the users to a series of sub-rate optimization problems. And the block diagonalization (BD) technique is used in the equivalent channel to eliminate inter-user interference. Simulation results illustrate that the performance of the proposed scheme outperforms the existing algorithm, especially significant when there exists high correlation between users' channels.

This paper investigates ground-aerial uplink non- orthogonal multiple access (NOMA) cellular networks. A unmanned aerial vehicle (UAV) user and ground users (GUEs) are served by ground base stations (GBSs) by utilizing uplink NOMA protocol. The goal is to minimize the UAV mission completion time by jointly designing the UAV trajectory and UAV-GBS association vectors while considering the interference of UAV to other non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite number of variables, which is difficult to be directly solved. To tackle this challenge, we first prove the optimal UAV trajectory satisfies \emph{fly-hover-fly} communication policy. With this insight, we propose an efficient algorithm to solve the original problem based on a properly constructed graph by invoking graph theory and convex optimization techniques. Numerical results show that the UAV mission completion time is significantly minimized with proposed NOMA scheme compared with conventional orthogonal multiple access (OMA) communication and reveal a tradeoff between the UAV mission completion time and GUEs' quality-of-service (QoS) requirements.

Orthogonal time frequency space modulation (OTFS) which is a novel modulation scheme, aims at offering reliable communications to users under high mobility conditions. Since each symbol in OTFS experiences the same fading over the doubly selective channel, OTFS is able to exploit full time-frequency (TF) diversity when coupled with a proper equalizer. In this paper, we study the low complexity iterative equalizer design for the coded OTFS system. In order to jointly combat the two-dimensional interference, we adopt linear minimum mean squared error based parallel interference cancellation (LMMSE-PIC) as the equalizer for OTFS. The first order Neumann series is used to approximate the matrix inversion involved in LMMSE-PIC for OTFS, which reduces the equalizer complexity to quasi-linear to the total number of transmitted symbols. Extrinsic information transfer (EXIT) charts and coded bit error rate (BER) simulation results show that our proposed LMMSE-PIC equalizer outperforms the recently proposed PIC approach for OTFS and also achieves considerable gain compared to orthogonal frequency division multiplexing (OFDM).

In massive multiple-input multiple-output (MIMO) scenario, in order to adapt to channel features and ensure the high-reliability and high-rate of communication, the channel state information (CSI) should be sent back to the base station (BS). As the number of antennas increases, the amount of CSI feedback increases dramatically. Ensuring communication performance and reducing CSI feedback is a challenging research direction. Some deep learning (DL) based models have been proposed to solve this problem. This paper aims to improve recovery performance and decrease time complexity. First, in encoder network, long short-term memory (LSTM) network have been introduced; second, in decoder network, attention mechanism has been added; third, early stopping have been used in training step. The simulation results show that the structure proposed in this paper is not only better than the performance of the traditional compressed sensing algorithm, but also better than the existing DL-based feedback network and achieve state-of-the-art.

For short to moderate finite block-lengths, due to incompletely polarized bit-channels, polar codes fail to deliver competitive performance under successive cancellation (SC) decoding. In this paper, we first define ω-order medium-level bit-channels (MBC) sets, which are used to quantitatively identify the incompletely polarized bit-channels of polar codes. And then, using the reliability ordering of bit-channels, which is calculated and used for encoding and decoding of polar codes, an algorithm for finding the MBC sets is given. Finally, we propose a multi-bit-flipping SC decoding algorithm, which builds a list of candidate bit-flips with 1 up to ω flipping positions within the ω-order MBC sets. The iteration trajectory of bit-flipping positions is a fixed subsequence of reliability ordering, which avoids the sorting operation of log likelihood rates in each codeword frame. Simulation results show that, with a code rate 0.5, the search scope and decoding latency of the proposed algorithm can be reduced about 1/3 to 1/6 without loss of performance compared with the standard SCFlip decoding.

In the conventional bit interleaved polar coded modulation (BIPCM), there exist some mutual information losses due to neglecting the correlations between the bit levels by using the bit interleaver. Based on this, the delayed BIPCM (DBIPCM) architecture is proposed to reduce the information losses in this paper. In the DBIPCM scheme, only one encoder is adopted and the transmitted symbols convey bits from the codewords encoded at different time for each bit level determined by the preset delays, which is different from the BIPCM. As we know, the variance of the bit channel capacities is used to measure the polarization effect under the multilevel polar coded modulation (MLC-PCM) scheme. It is found that the DBIPCM can achieve a more significant polarization effect compared to the MLC-PCM with the same set partitioning (SP) mapping way due to an extra log2m polarization transform process for 2m-ary constellations. From the simulation results by using the 16PAM over the additive white Gaussian noise channels, it is shown that the DBIPCM with Gray mapping can achieve a better performance compared to the conventional BIPCM. In addition to this, the DBIPCM with SP mapping outperforms the MLC-PCM by up to more than 0.6dB.

An efficient codebook optimization algorithm is proposed to maximize mutual information in sparse code multiple access (SCMA). At first, SCMA signal model is given according to superposition modulation structure, in which the channel matrix is column‐extended. The superposition model can well describe the relationship between the codebook matrix and received signal. Based on the above model, an iterative codebook optimization algorithm is proposed to maximize mutual information between discrete input and continuous output. This algorithm can efficiently adapt to multiuser channels with arbitrary channel coefficients. The simulation results show that the proposed algorithm has good performance in both AWGN and non‐AWGN channels. In addition, message passing algorithm (MPA) works well with the codebook optimized according to the proposed algorithm.

In this paper, a three‐dimensional polar code (3D‐PC) scheme is proposed to improve the error floor performance of parallel concatenated systematic polar code (PCSPC). The proposed 3D‐PC is constructed by serially concatenating the PCSPC with a rate‐1 third dimension, where only a fraction λ of parity bits of PCSPC are extracted to participate in the subsequent encoding. It takes full advantage of the characteristics of parallel concatenation and serial concatenation. In addition, the convergence behavior of 3D‐PC is analyzed by the extrinsic information transfer (EXIT) chart. The convergence loss between PCSPC (λ = 0) and different λ provides the reference for choosing the value of λ for 3D‐PC. Finally, the simulation results confirm that the proposed 3D‐PC scheme lowers the error floor.

An iterative intercell interference cancellation algorithm is introduced to improve the receiver performance of uplink transmission in multicell networks. At first, the uplink signal detection is performed independently in each cell according to minimum mean squared error (MMSE) criterion. Subsequently, the detection results are applied to reconstruct the transmit signals of different users and cancel their interference to neighboring cells. With the help of reconstruction results, the MMSE detection matrix of each cell is updated. The channel responses of both efficient and interference links are estimated with the help of pilots. The pilot allocation parameter is introduced to indicate the quality of channel estimation. The simulation results indicate that intercell interference can be greatly mitigated by the proposed algorithm with a moderate number of receiver antennas at the base station.

Recently, security problems in massive multiple-input multiple-output (MIMO) network draw more and more attention. In this paper, a security problem for a multi-pair massive MIMO two-way amplify-and-forward (AF) relay network is considered, where multiple users exchange confidential messages via a common relay equipped with large number of antennas. We propose an optimal power allocation scheme that maximizes the secrecy sum rate (SSR) under power constraints, when the multi-pair sources act as both potential eavesdroppers who intend to wiretap the information of other pairs and conventional users who exchange their information with pairings. Simulation results show that the proposed scheme outperforms the equal power allocation especially under the circumstance of large number of relay antennas. Furthermore, the number of user-pairs, the Signal to Noise Ratio (SNR) and the number of relay antennas are all factors which influence the performance of SSR in this network.

In the time-division-duplex (TDD) system, the channel state information (CSI) is mainly obtained through channel estimation. The active eavesdropper is able to attack the channel estimation phase by sending the same pilot signals as the legitimate users send to the base station without being detected, which can destroy the CSI acquired through channel estimation and greatly decrease the communication quality, so it is very necessary to detect the active eavesdropper before the data transmission. In this paper, we propose to design a mechanism to detect active eavesdropper in the massive MIMO network. Specifically, the sender will intentionally add noise to the pilot sequence so that it becomes harder for the bad guys to learn the pilot sequence. At the same time, if the real sender and the attacker signals arrive at the receiver simultaneously, the received signals will demonstrate special properties in the rank of the matrix. we design a mechanism to estimate the ranks to detect active eavesdropper. Comparison between the approach and MDL-based detector is conducted.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access scheme for 5G systems. In this paper, based on the sphere decoding (SD), we design an optimal receiver for SCMA, which provides much lower complexity than the maximum likelihood (ML) detection without sacrifice of the optimal performance. Regarding the coded SCMA system, we propose a list sphere decoding (LSD) method to output each user's bit soft decisions to the corresponding channel decoders. In addition, the existing lattice points in the list are employed to set the original radius so as to further reduce the complexity of LSD. The performance and complexity analyses indicate that the proposed optimal receiver provides notable gain compared to the most popular SCMA receiver equipped with the message passing algorithm.

In this paper, polar coding is combined with the generalized frequency division multiplexing (GFDM) and the channel polarization idea is extended to the GFDM system, which is a flexible multicarrier modulation scheme proposed for future waveform. First, the framework of polar coded GFDM (PC-GFDM) is constructed based on a two-stage channel polarization transform. In the first stage, on the basis of bit-interleaved code modulation, GFDM channel is divided into a set of parallel binary-input GFDM synthesized channels. In the second stage, by using the binary channel polarization, the GFDM synthesized channels are further polarized into a set of bit polarized channels. The polar codes are efficiently constructed by calculating the reliabilities of the two-stage polarized channels. Then, an empirically optimal interleaver is designed for PC-GFDM. Furthermore, regarding the features of GFDM synthesized channels, a simplified block interleaving scheme is proposed to approach the optimal interleaver and reduce the implementation complexity. Compared with the conventional turbo coded GFDM (TC-GFDM), simulation results indicate that our proposed PC-GFDM system can outperform the TC-GFDM system by at least 1dB for different scenarios.

Sparse code multiple access (SCMA) has recently emerged as one of the most favorable multiple access schemes for 5G networks, which allows overloading with a large number of users so as to enable massive connectivity. In this paper, we design a hardware framework of a uplink system for SCMA. First, we propose a unified quantization scheme based on density evolution optimization which is independent of signal-to-noise ratios (SNR). Second, we apply a fast convergence message passing algorithm (FC-MPA) in SCMA multiuser detection, in which the function nodes updating and variable nodes updating are processed synchronously so as to make the FC-MPA converge about 2 times faster than standard MPA. Finally, based on FC- MPA, we design a pipelined decoding structure for SCMA so as to increase throughput. FPGA results demonstrate that the fix- point performance achieve a near floating-point performance for different SNR and the pipelined hardware structure we design is feasible.

With the development of wireless communication, one of the critical demands is secure transmission especially in downlink communication. In this paper, downlink secure transmission in two-cell base station cooperation multiuser network is studied. The two base stations are fully cooperated and an artificial noise (AN) is added to degrade the eavesdropper(EVE)'s channel. Perfect channel state information (CSI) of all users is known by the base stations and regularized channel inversion (RCI) precoding is used. The close form expression of secrecy sum rate is derived in the large system regime. The regularization parameter and the power allocation ratio are optimized based on larger system regime results. From analysis, base station cooperation network could serve more users per cell in secure transmission than single cell network. The numerical results show large system regime results are accurate even in finite case. In the simulation figures, the analytical optimal results can well approximate to the simulation results.

In this study, zero‐forcing matrix decomposition polynomial expansion update (ZF‐MDPE‐update) and zero‐forcing successive over relaxation update (ZF‐SOR‐update) algorithms are proposed to update a zero‐forcing detector quickly without requiring complicated matrix inversion recomputations when massive multiple‐input multi‐output systems channel estimates contain a small perturbation. To further accelerate the convergence rate and to maximise date throughput, a new method of calculating the optimal coefficients for the matrix polynomial that can significantly improve the accuracy of the initial input inverse matrix approximation is considered by the ZF‐MDPE‐update algorithm. On the other hand, the ZF‐SOR‐update algorithm with an optimal iterative initial solution and an optimal relaxation parameter is devised, which achieves excellent detection performance. Results demonstrate that when the ratio of base station (BS) antennas to user terminal (UT) antennas, , is small, the proposed update detection algorithms, with only a few operations, achieve a significant improvement in the average achievable rate of the UTs compared to the recently proposed update algorithm. Therefore, more UTs can be served in a cell with a fixed number of BS antennas. At the same time, the authors' algorithms are shown to facilitate easy memory transfer and are low cost.

A non-reused pilot (NRP) design scheme is presented for Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems. As the users at the cell edge suffer from severe pilot contamination (PC), the pilot design based on Chu sequences for two kinds of users is considered. The users in each cell are divided into center users and edge users according to the large-scale fading coefficients. The proposed pilot design scheme can reduce inter-cell interference of center users and improve the performance of edge users, whereby assigning phase shifted pilot groups to center users in adjacent cells and orthogonal pilot group to edge users in all cells.

In this paper, an architecture of constellation shaping for bit-interleaved polar coded modulation (BIPCM) is proposed. Theoretically, the probability distribution of constellation points should be selected according to a continuous Gaussian distribution over Additive White Gaussian Noise (AWGN) channels, which will help to obtain shaping gain. Based on this probabilistic shaping idea, unequiprobable constellations are designed to increase the mutual information. Under the proposed BIPCM architecture, the mapping method has a larger diversity of bit channels that can help to improve the system performance. The mapping way is performed based on the prefix code, which can meet the above property. Simulation results of 16-ary pulse amplitude modulation (16-PAM) over AWGN channels show that the nonequiprobable constellations provide 0.5dB shaping gain compared to the traditional equiprobable constellations.

This paper is concerned with a simplified hard decision feedback equalizer (S-HDFE) for single carrier modulation with cyclic prefix. In this paper, we focus on the coded system in which the feedback symbols are reproduced from hard decisions of channel decoding results. Cholesky decomposition is the key technique for simplifying the feedback filter calculation of S-HDFE. With the same matched filter bound (MFB), the maximum number of the feedback filter taps in S-HDFE is smaller than that in the existing hard decision feedback equalizer with noise prediction (DFE-NP). Therefore, the computational complexity of S-HDFE is lower than DFE-NP with the same MFB. Furthermore, the simulation result shows that if the feedback filter tap number is the same for both equalizers, the BER performance of S-HDFE is better than DFE-NP.