Low Complexity Multiuser Detection with Recursively Successive Zero-Forcing and SIC Based on Nullspace for Multiuser MIMO-OFDM System

To improve the spectrum efficiency, this paper considers the multiuser detection with the MU-MIMO technology for multiuser MIMO-OFDM system uplink with the same subcarrier shared by multiple users. A low complexity multiuser detection algorithm with recursively successive zero-forcing and successive interference cancellation(RSZF-SIC) based on nullspace is proposed. The RSZF process based on the block diagonalization(BD) technique eliminates the co-channel interference(CCI) by a recursive method based on the nullspace orthogonal theorem. The SIC process detects the user signals respectively with the reasonable user detection sequence based on the results of the RSZF process. The computational complexity of the proposed algorithm is effectively reduced by reducing the total number of singular value decomposition(SVD) operations and the dimension of the SVD matrix in the recursive procedure. The performance of the proposed algorithm is improved in terms of bit error rate and sum capacity of the system, especially in the highSNR regime.

Efficient Bandwidth and Power Allocation Algorithms for Multiuser MIMO-OFDM Systems

This paper studies the problem of finding an effective subcarrier and power allocation strategy for downlink communication to multiple users in a MIMO-OFDM system with zero-forcing beamforming. The problem of minimizing total power consumption with constraint on transmission rate for users is formulated. The problem of joint allocation is divided into two stages. In the first stage, the number of subcarriers that each user will get is determined based on the users’ average signal-to-noise ratio. In the second stage, it finds the best assignment of subcarriers to users. The optimal method is a complex combinatorial problem which can only be assuredly solved through an Exhaustive Search (ES). Since the ES method has high computational com-plexity, the normalized user selection algorithm and the simplified-normalized user selection algorithm are proposed to reduce the computational complexity. Simulation results show that the proposed low complexity algorithms offer better performance compared with an existing algorithm.

基于混沌序列的MIMO-OFDM雷达通信一体化波形设计

为了解决多输入多输出的正交频分复用(multiple input multiple output orthogonal frequency division multiplexing,MIMO-OFDM)雷达通信一体化信号波形由于通信信息的不确定性导致模糊函数旁瓣水平较高以及系统雷达探测能力较差的问题,提出利用混沌序列对通信信息进行直接扩频。但普通单级混沌序列具有保密性低,可用数量少,容易被破解的缺点。因此采用一种基于加权算法优化的混合型级联混沌序列对通信信息进行扩频调制,然后再通过MIMO-OFDM一体化系统进行传输。由仿真结果可知,加权后的级联混沌序列相关性和误码率性能较m序列和Gold序列更好,经此序列扩频后的一体化信号模糊函数旁瓣水平会更低,故该一体化波形雷达探测性能更佳。且混沌序列越长,信号波形雷达探测能力越好。

基于深度学习的多用户MIMO-OFDM-IM系统符号检测技术

MIMO-OFDM-IM是将MIMO-OFDM(多输入多输出正交频分复用)和IM(索引调制)结合的一种新颖的多载波调制技术。为解决目前多用户MIMO-OFDM索引调制的符号检测技术仍存在的复杂度高和误码性能受用户数量影响严重的问题,提出一种基于深度学习的符号检测框架,在该框架中,编码器与解码器都由DNN(深度神经网络)构造,采用监督式学习训练数据。实验结果表明,该框架有效地解决了以上问题。

Resource allocation with CCI suppression for multiuser MIMO-OFDM downlink in correlated channels

To minimize the overall transmit power while maintaining a constant data rate and target BER, a downlink adaptive resource allocation algorithm with jointing the exclusive manner and the shared manner is proposed for multiuser MIMO-OFDM system in correlated channels. The algorithm allocates all the subcarriers to different users according to their spatial correlations. The users with high spatial correlation are allocated in the same group and the exclusive manner is applied. The shared manner with an improved null broadening method, which improves the performance of co-channel interference （CCI） suppression and decreases the number of transmit antennas required, is applied between the different group users. As the user＇s direction of departure （DOD） changes very slowly, a looking up table method is used to reduce the computational complexity. The simulation results show that despite the angle spread of DOD, when compared with the exclusive manner, the proposed algorithm improves the spectral efficiency, and when compared with the TDMA-ZF （zero forcing） shared manner, the proposed algorithm decreases the total transmit power by at least 1 dB.

Low-complexity soft-output signal detector based on adaptive pre-conditioned gradient descent method for uplink multiuser massive MIMO systems

In multiuser massive Multiple Input Multiple Output(MIMO)systems,a large amount of antennas are deployed at the Base Station(BS).In this case,the Minimum Mean Square Error(MMSE)detector with soft-output can achieve the near-optimal performance at the cost of a large-scale matrix inversion operation.The optimization algorithms such as Gradient Descent(GD)method have received a lot of attention to realize the MMSE detection efficiently without a large scale matrix inversion operation.However,they converge slowly when the condition number of the MMSE filtering matrix(the coefficient matrix)increases,which can compromise the efficiency of their implementation.Moreover,their soft information computation also involves a large-scale matrix-matrix multiplication operation.In this paper,a low-complexity soft-output signal detector based on Adaptive Pre-conditioned Gradient Descent(APGD-SOD)method is proposed to realize the MMSE detection with soft-output for uplink multiuser massive MIMO systems.In the proposed detector,an Adaptive Pre-conditioner(AP)matrix obtained through the Quasi-Newton Symmetric Rank One(QN-SR1)update in each iteration is used to accelerate the convergence of the GD method.The QN-SR1 update supports the intuitive notion that for the quadractic problem one should strive to make the pre-conditioner matrix close to the inverse of the coefficient matrix,since then the condition number would be close to unity and the convergence would be rapid.By expanding the signal model of the massive MIMO system and exploiting the channel hardening property of massive MIMO systems,the computational complexity of the soft information is simplified.The proposed AP matrix is applied to the GD method as a showcase.However,it also can be used by Conjugate Gradient(CG)method due to its generality.It is demonstrated that the proposed detector is robust and its convergence rate is superlinear.Simulation results show that the proposed detector converges at most four iterations.Simulation results also show that the proposed approach achieves a better trade-off between the complexity and the performance than several existing detectors and achieves a near-optimal performance of the MMSE detector with soft-output at four iterations without a complicated large scale matrix inversion operation,which entails a big challenge for the efficient implementation.

A novel differential multiuser detection algorithm for multiuser MIMO-OFDM systems

We propose an efficient low bit error rate(BER) and low complexity multiple-input multiple-output(MIMO) multiuser detection(MUD) method for use with multiuser MIMO orthogonal frequency division multiplexing(OFDM) systems.It is a hybrid method combining a multiuser-interference-cancellation-based decision feedback equalizer using error feedback filter(MIMO MIC DFE-EFF) and a differential algorithm.The proposed method,termed 'MIMO MIC DFE-EFF with a differential algorithm' for short,has a multiuser feedback structure.We describe the schemes of MIMO MIC DFE-EFF and MIMO MIC DFE-EFF with a differential algorithm,and compare their minimum mean square error(MMSE) performance and computational complexity.Simulation results show that a significant performance gain can be achieved by employing the MIMO MIC DFE-EFF detection algorithm in the context of a multiuser MIMO-OFDM system over frequency selective Rayleigh channel.MIMO MIC DFE-EFF with the differential algorithm improves both computational efficiency and BER performance in a multistage structure relative to conventional DFE-EFF,though there is a small reduction in system performance compared with MIMO MIC DFE-EFF without the differential algorithm.

Resource allocation for multiuser STBC-BF MIMO-OFDM downlink with imperfect channel information

To improve the performance of a multiuser MIMO-OFDM system with imperfect channel status information, a downlink adaptive resource allocation algorithm which combines space-time block coding and beam forming （STBC-BF） is proposed. The algorithm allocates the subcarriers with a shared manner. A zero forcing processing with joint Rx-Tx is used to suppress the co-channel interference （CCI） and to construct uncorrelated channels for STBC. An adaptive power allocation for the STBC equivalent channels can increase signal to interference and noise ratio at the receiver. Simulation results show that under the condition of an imperfect CSI, the proposed algorithm improves the system performance and reduces the number of BS transmit antennas required.

SINR-BASED ANTENNA COMBINING FOR MULTIUSER MIMO-OFDM WITH LIMITED FEEDBACK

A multiuser Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) broadcast channel is considered where both transmitter and receivers are equipped with multiple antennas. The Channel State Information (CSI) is quantized and provided through limited feedback links. In the first part of this work, the Maximum Expected SINR Combining (MESC) strategy for feedback decrease is investigated. Then a combination of MESC with subcarrier assigning is presented to further reduce the feedback load. The basic idea is the subcarriers are partitioned into resource blocks. For each block, only one quantized channel vector is feedback. The simulation results show MESC can obtain the performance gain over the relevant combiner strategies in MIMO-OFDM system. Moreover the effectiveness on the reduction of feedback overhead by the use of partitioning blocks in the studied system is evaluated.

低频部署下MIMO-OFDM通信系统的频谱利用效率分析

主要深入研究低频部署下多进多出-正交频分复用(Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing,MIMO-OFDM)通信系统的频谱利用效率,探讨低频部署对系统性能的影响,并提出相应的优化策略。详细分析低频部署对信号传输性能和频谱利用效率的影响,评估频谱利用效率,提出针对低频部署的MIMO-OFDM系统的优化策略,最后探讨低频部署下MIMO-OFDM系统的适用场景,为实际应用提供一些建议。

Comparison of Two Neural Networks in MC-CDMA Multiuser Detection

ＭＣＣＤＭＡ如同ＤＳＣＤＭＡ一样，也存在多用户干扰．ＭＬ准则是最佳的多用户检测准则，但其计算复杂度随用户数目增多而呈指数增长．神经网络利用其大规模并行的特点，可应用于此问题．本文论述了均场退火和混沌神经网络在此问题中的解决方案，并作了比较．

Cross-Layer Framework for Capacity Analysis in Multiuser Ultra-Dense Networks with Cell DTx

Cell discontinuous transmission(Cell DTx)is a key technology to mitigate inter-cell interference(ICI)in ultra-dense networks(UDNs).The aim of this work is to understand the impact of Cell DTx on physical-layer sum rates of SBSs and link-layer quality-of-service(QoS)performance in multiuser UDNs.In this work,we develop a cross-layer framework for capacity analysis in multiuser UDNs with Cell DTx.In particular,we first extend the traditional one-dimensional effective capacity model to a new multidimensional effective capacity model to derive the sum rate and the effective capacity.Moreover,we propose a new iterative bisection search algorithm that is capable of approximating QoS performance.The convergence of this new algorithm to a unique QoS exponent vector is later proved.Finally,we apply this framework to the round-robin and the max-C/I scheduling policies.Simulation results show that our framework is accurate in approximating 1)queue length distribution,2)delay distribution and 3)sum rates under the above two scheduling policies,and further show that with the Cell DTx,systems have approximately 30% higher sum rate and 35% smaller average delay than those in full-buffer scenarios.

Performance Enhancement of Multiuser Passive Time Reversal Communication Based on Space-Time Block Coding

Reliable, with high data rate, acoustic communication in time-valTing, multipath shallow water environment is a hot research topic recently. Passive time reversal communication has shown promising results in improvement of the system performance. In multiuser environment, the system performance is significantly degraded due to the interference among different users. Passive time reversal can reduce such interference by minimizing the cross-correlated version of channel impulse response among users, which can be realized by the well-separated users in depth. But this method also has its shortcomings, even with the absence of relative motion, the minimization sometimes may be impossible because of the time-varying environment. Therefore in order to avoid the limitation of minimizing the cross-correlated channel function, an approach of passive time reversal based on space-time block coding （STBC） is presented in this paper. In addition, a single channel equalizer is used as a pest processing technique to reduce the residual symbol interference. Experimental results at 13 kHz with 2 kHz bandwidth demonstrate that this method has better performance to decrease bit error rate and improve signal to noise ratio, compared with passive time reversal alone or passive time reversal combined with equalization.

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.

Performance of Block Diagonalization Broadcasting Scheme for Multiuser MIMO System Operating in Presence of Spatial Correlation and Mutual Coupling

In this paper, the capacity of a multiuser Multiple Input Multiple Output (MIMO) system employing the block diagonalization broadcasting scheme in presence of spatial correlation and mutual coupling is investigated. It is shown by computer simulations that, in general, the presence of spatial correlation decreases the capacity of a multiuser MIMO system. However, for some particular antenna element spacing mutual coupling decreases the spatial correlation rendering an increased capacity. The optimized diagonalization broadcasting technique with a two-stage power allocation scheme is proposed and verified. The presented simulations results confirm the advantage of the proposed broadcasting scheme.

Secure Transmissions in Wireless Multiuser Networks Using Message Correlation

Due to the openness of wireless multiuser networks,the private information transmitted in uplink or downlink is vulnerable to eavesdropping.Especially,when the downlink transmissions use nonorthogonal multiple access(NOMA)techniques,the system further encounters interior eavesdropping.In order to address these security problems,we study the secret communication in multiuser networks with both uplink and downlink transmissions.Specifically,in uplink transmissions,the private messages transmitted in each slot are correlated,so any loss of the private information at the eavesdropper will prevent the eavesdropper from decoding the private information in later time slots.In downlink transmissions,the messages are correlated to the uplink information.In this way,any unexpected users who lose the expected user’s uplink information cannot decode its downlink information.The intercept probability is used to measure security performance and we analyze it in theory.Finally,simulation results are provided to corroborate our theoretical analysis.

Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing

Mobile Edge Computing(MEC)has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices.In this paper,we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization.Specifically,we first focus on a static multiuser computation offloading scenario and jointly optimize users'offloading decisions,transmission durations,and Edge Servers'(ESs)resource allocations.Owing to the nonconvexity of our joint optimization problem,we identify its layered structure and decompose it into two problems:a subproblem and a top problem.For the subproblem,we propose a bisection search-based algorithm to efficiently find the optimal users'offloading decisions and ESs’resource allocations under a given transmission duration.Second,we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem.Further,after solving the static scenario,we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload.To efficiently address this dynamic scenario,we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner.Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios.We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.

NOVEL ADAPTIVE MULTIUSER DETECTIONALGORITHM BASED ON WAVELET TRANSFORM

The wavelet transform-based adaptive multiuser detection algorithm is presented. The novel adaptive multiuser detection algorithm uses the wavelet transform for the preprocessing, and wavelet-transformed signal uses LMS algorithm to implement the adaptive multiuser detection. The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence. White noise can be wiped off under the wavelet transform according to different characteristics of signal and white noise under the wavelet transform. Theoretical analyses and simulations demonstrate that the algorithm converges faster than the conventional adaptive multiuser detection algorithm, and has the better performance. Simulation results reveal that the algorithm convergence relates to the wavelet base, and show that the algorithm convergence gets better with the increasing of regularity for the same series of the wavelet base. Finally the algorithm shows that it can be easily implemented.

Fair subcarrier-power allocation scheme for multiuser multicarrier systems

The main objective of multiuser orthogonal frequency division multiple access(MU-OFDM) is to maximize the total system capacity in wireless communication systems. Thus, the problem in MU-OFDM system is the adaptive allocation of the resources(subcarriers, bits and power) to different users subject to several restrictions to maximize the total system capacity. In this work, a proposed subcarrier allocation algorithm was presented to assign the subcarriers with highest channel gain to the users. After the subcarrier allocation, subcarrier gain-based power allocation(SGPA) was employed for power and bit loading. The simulation results show that the proposed subcarrier-power allocation scheme can achieve high total system capacity and good fairness in allocating the resources to the users with slightly high computational complexity compared to the existing subcarrier allocation algorithms.

ANALYSIS OF GROUP MULTIUSER DETECTION BASED ON COALITION GAME THEORY

Coalition game theory is introduced to investigate the performance,fairness and stability of decorrelating group multiuser detection receiver,not only from the perspective of individual nodes,but also various coalitions and the whole system as well. Firstly,to derive how the system scale with coalition size,a stochastic model with transferable payoffs (stochastic TU-model) is provided. Secondly,to find the most preferred coalition structures from the view point of individual nodes,a model with Non-Transferable payoffs (NTU-model) is presented. Theoretical analysis and simulation results suggest that stochasticaly the grand coalition is payoff maximizing for the system as a whole,while individual nodes with good-conditioned channels may prefer local "win-win coalitions".