基于非理想CSI的下行MU-MIMO鲁棒波束成形

在下行多用户多入多出（MU-MIMO）系统中,基站（BS）所获得的非理想信道状态信息（CSI）会导致频分双工（FDD）系统预编码性能变差.现有的MU-MIMO鲁棒预编码算法虽然可以对抗非理想CSI所导致的系统性能损失,但其只考虑其中一种或两种信道误差的鲁棒性,因此系统性能提升有限.本文通过建立包含信道估计误差、量化误差和延时误差的联合信道误差模型,推导出具有集中式特性的基于最小均方误差（MMSE）的鲁棒波束成形矩阵的闭式解;随后将这种信道条件应用到分布式通信系统,并推导出具有分布式特性的基于信号泄露的MMSE的鲁棒波束成形矩阵的闭式解.数值分析表明,本文所提的集中式和分布式MU-MIMO波束成形算法,与只考虑量化误差的鲁棒MMSE算法相比,具有更优的系统和速率与误码率,补偿了上述三种信道误差所导致的预编码性能损失.     

User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) – non-orthogonal multiple access (NOMA) downlink system for 5G wireless communications. This work consider the imperfect channel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as non-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beamforming design which establishes on majorization minimization (MM) technique to find the optimal transmit beamforming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.     

Robust Interference Management via Linear Precoding and Linear/Non-Linear Equalization

This work studies the robust design of linear precoding and linear/ non-linear equalization for multi-cell MIMO systems in the presence of imperfect channel state information (CSI). A worst-case design approach is adopted whereby the CSI error is assumed to lie within spherical sets of known radius. First, the optimal robust design of linear precoders is tackled for a MIMO interference broadcast channel (MIMO-IBC) with general unicast/multicast messages in each cell and operating over multiple time-frequency resources. This problem is formulated as the maximization of the worst-case sum-rate assuming optimal detection at the mobile stations (MSs). Then, symbol-by-symbol non-linear equalization at the MSs is considered. In this case, the problem of jointly optimizing linear precoding and decision-feedback (DF) equalization is investigated for a MIMO interference channel (MIMO-IC) with the goal of minimizing the worst-case sum-mean squared error (MSE). Both problems are addressed by proposing iterative algorithms with descent properties. The algorithms are also shown to be implementable in a distributed fashion on processors that have only local and partial CSI by means of the Alternating Direction Method of Multipliers (ADMM). From numerical results, it is shown that the proposed robust solutions significantly improve over conventional non-robust schemes in terms of sum-rate or symbol error rate. Moreover, it is seen that the proposed joint design of linear precoding and DF equalization outperforms existing separate solutions.     

Differentiated rate scheduling for the down-link of cellular systems

We consider the problem of differentiated rate scheduling for the downlink (i.e., multi-antenna broadcast channel), in the sense that the rates required by different users must satisfy certain constraints on their ratios. When full channel state information (CSI) is available at the transmitter and receivers, the problem can be readily solved using dirty paper coding (DPC) and the application of convex optimization techniques on the dual problem which is the multiple access channel (MAC). Since in many practical application full CSI may not be feasible and computational complexity prohibitive when the number of users is large, we focus on other simple schemes that require very little CSI: time-division opportunistic (TO) beamforming where in different time slots (of different lengths) the transmitter performs opportunistic beamforming to the users requiring the same rate, and weighted opportunistic (WO) beamforming where the random beams are assigned to those users having the largest weighted SINR. For single antenna systems we also look at the capacity-achieving superposition coding (SC) scheme. In all cases, we determine explicit schedules to guarantee the rate constraints and show that, in the limit of large number of users, the throughput loss compared to the unconstrained throughput (sum-rate capacity) tends to zero. We further provide bounds on the rate of convergence of the sum-rates of these schemes to the sum-rate capacity. Finally, we provide simulation results of the performance of different scheduling schemes considered in the paper.     

Adaptive minimum bit feedback for sum-rate analysis in multi-user distributed antenna systems

The paper mainly studies the sum-rate performance of limited feedback (LFB) block-diagonalization (BD) in multi-user distributed antenna system (DAS). As the channel state information (CSI) fed back to base station (BS) is limited, multi-user interference (MUI) is caused inevitably because of the quantization error. Considering the influence of the MUI to the capacity of DAS, we propose a parameter of effective sum-rate ratio (ESRR) to denote the capacity offset under the condition of the BS can get perfect CSI and limited CSI first, then we confirm that the approximated ESRR is very close to actual ESRR got through simulations. After that, based on the approximated ESRR, an adaptive minimum bit feedback scheme which can effectively reduce the overhead of feedback channel and the complexity of the system is proposed. Simulation results show the effectiveness of the proposed scheme.     

Residual energy based multi-relay selection and power allocation scheme in two-way relay network

In this paper, we have proposed a multi-relay selection and power allocation scheme for two-way relay network which aims to maximize the sum-rate of two-way relay system. First, to prolong network lifetime, a multi-relay selection strategy is proposed in which both channel state information （CSI） and remaining energy （RE） are considered. Next, a multi-relay power allocation algorithm based on convex optimization （MRPA-CO） is presented. To reduce the computational complexity, it can be divided into two steps： terminal nodes power allocation （TNPA） and relay nodes power allocation （RNPA）. Simulation results indicate that the proposed relay selection strategy can significantly prolong network lifetime compared to other relay selection strategies which consider CSI only, and the MRPA-CO algorithm has great advantage over equal power allocation （EPA） on sum-rate in two-way relay network.     

MMSE based joint Tomlinson-Harashima source and linear relay precoder design in non-regenerative MIMO relay system

This paper proposes a joint nonlinear transceiver design scheme based on minimum mean square error(MMSE) criterion for non-regenerative multiple input multiple output(MIMO) relay system.The proposed scheme decomposes the error covariance matrix,reformulates the original joint design problem as two separate optimization problems,and then provides a closed-form solution with only local channel state information(CSI) available at the source and destination.Performance evaluation shows that the proposed scheme significantly outperforms linear schemes,and has a competitive performance compared with existing global CSI based nonlinear schemes,both iterative and non-iterative.     

Closed-Form Solutions to the Pareto Boundary and Distributed Beamforming Strategy for the Two-User MISO Interference Channel

The outer boundary of the achievable rate region for multiple-input single-output (MISO) interference channel (IC) is Pareto boundary, and all points on the Pareto boundary can be obtained by solving weighted sum rate maximization problem. Unfortunately, since the optimization problem is non-convex, it is generally very difficult to obtain the solutions without performing an exhaustive search. In this paper, the achievable rate region of the two-user MISO IC is considered. Firstly, by minimizing the interference power leaked to the other receiver for fixed useful signal power received at the intended receiver, the non-convex optimization problem is converted into a family of convex optimization problems. Secondly, after some conversions, the closed-form solutions to all Pareto optimal points are derived using the Lagrange duality theory, and the only computation involved is to solve a basic quadratic equation. Then, the antenna reduction is performed to further simplify the process of derivation. In order to avoid the exchange of channel state information (CSI) between base stations, a distributed iterative beamforming strategy which can achieve a approximate Pareto optimal outcome with only a few iterations is also proposed. Finally, the results are validated via numerical simulations.     

Sum-Rate Analysis of Downlink Channels with 1-Bit Feedback

The sum-rate capacity of a single-input single-output (SISO) downlink with Rayleigh flat fading channels and K users, grows as log log K when optimal scheduling is employed. However, the optimal scheduling requires that the full channel state information (CSI) for all users be available to the transmitter. In this work it is shown that the same rate growth holds even if the feedback rate from the users to the transmitter is reduced to 1-bit per fading block. A simple analysis for this setup is presented, resulting in a closed form expression for the achievable ergodic sum-rate. The mechanism of setting a sub-optimal threshold is elucidated by simple lower and upper bounds to the sum-rate. Among the insights afforded by the sum-rate expression and the bounds, is that application of the sub-optimal threshold demonstrates the same scaling law as the optimal full CSI scheme, asymptotically with the number of users K     

UDN中基于上下行对偶性的分布式干扰协调

针对超密集网络（UDN,Ultra-Dense Networks）中的小区间干扰问题,提出一种基于上下行对偶性的分布式干扰协调方法。首先设计了新的TDD帧格式,在原有LTE标准的基础上加入了信道估计部分与上下行迭代部分;接着利用网络的对偶原理,设计了新的迭代干扰处理算法,将最大化信干噪比和最小化残留干扰相综合,实现最大全网和数据的优化目标。理论分析和仿真结果表明,基于最大信漏噪比准则选取迭代初值时,可在一次迭代后达到较好的和数据率性能;并且该方法能在较短的时间内完成干扰协调,只需要已知局部信道信息,在UDN中易于实现,性能上优于其它分布式迭代干扰处理算法。      

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

The capacity of a massive MIMO cellular network depends on user and antenna selection algorithms, and also on the acquisition of perfect Channel State Information (CSI). Low computational cost algorithms for user and antenna selection significantly may enhance the system capacity, as it would consume a smaller bandwidth out of the total bandwidth for downlink transmission. The objective of this paper is to maximize the system sum-rate capacity with efficient user and antenna selection algorithms and linear precoding. We consider in this paper, a slowly fading Rayleigh channel with perfect acquisition of CSI to explore the system sum-rate capacity of a massive MIMO network. For user selection, we apply three algorithms, namely Semi-orthogonal user selection (SUS), Descending Order of SNR-based User Scheduling (DOSUS), and Random User Selection (RUS) algorithm. In all the user selection algorithms, the selection of Base Station (BS) antenna is based on the maximum Signal-to-Noise Ratio (SNR) to the selected users. Hence users are characterized by having both Small Scale Fading (SSF) due to slowly fading Rayleigh channel and Large-Scale Fading (LSF) due to distances from the base station. Further, we use linear precoding techniques, such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Ratio Transmission (MRT) to reduce interferences, thereby improving average system sum-rate capacity. Results using SUS, DOSUS, and RUS user selection algorithms with ZF, MMSE, and MRT precoding techniques are compared. We also analyzed and compared the computational complexity of all the three user selection algorithms. The computational complexities of the three algorithms that we achieved in this paper are O(1) for RUS and DOSUS, and O(M²N) for SUS which are less than the other conventional user selection methods.     

Optimization of Training and Scheduling in the Non-Coherent SIMO Multiple Access Channel

Channel state information (CSI) is important for achieving large rates in MIMO channels. However, in time-varying MIMO channels, there is a tradeoff between the time/energy spent acquiring channel state information (CSI) and the time/energy remaining for data transmission. This tradeoff is accentuated in the MIMO multiple access channel (MAC), since the number of channel vectors to be estimated increases with the number of users. Furthermore, the problem of acquiring CSI is tightly coupled with the problem of exploiting CSI through multiuser scheduling. This paper considers a block-fading MAC with coherence time T, n uncoordinated users-each with one transmit antenna and the same average power constraint, and a base station with M receive antennas and no a priori CSI. For this scenario, a training-based communication scheme is proposed and the training and multiuser-scheduling aspects of the scheme are jointly optimized. In the high-SNR regime, the sum capacity of the non-coherent SIMO MAC is characterized and used to establish the SNR-scaling-law optimality of the proposed scheme. In the low-SNR regime, the sum-rate of the proposed scheme is found to decay linearly with vanishing SNR when flash signaling is incorporated. Furthermore, this linear decay is shown to be order-optimal through comparison to the low-SNR sum capacity of the non-coherent SIMO MAC. A by product of these SNR-asymptotic analyses is the observation that non-trivial scheduling (i.e., scheduling a strict subset of trained users) is advantageous at low SNR, but not at high SNR. The sum-rate and per-user throughput are also explored in the large-n and large-M regimes. Non-coherent capacity, training, multiple access channel, multiuser scheduling, opportunistic scheduling.     

基于深度强化学习的无蜂窝系统无线接入点选择算法

面向以用户为中心的无蜂窝分布式多输入多输出（Multiple睮nput Multiple睴utput,MIMO）架构,研究利用不完备信道状态信息（Channel State Information,CSI）下实现无线接入点（Access Point,AP）与用户（User Equipment,UE）之间的选择,提出基于深度强化学习（Deep Reinforcement Learning,DRL）的高效分配算法,通过使用不完备CSI快速生成以用户为中心的AP集合,减少了对前馈链路容量的占用。仿真结果表明,与其他传统选择算法相比,所提出的DRL接入点选择算法可以获得至少22.48%的总遍历频谱效率增益;与深度Q网络 (Deep睶睳etwork,DQN)算法相比,可以获得约14.17%的总频谱效率增益。     

Novel transmit antenna selection strategy for massive MIMO downlink channel

In this paper, we deal with the problem of acquiring the channel state information (CSI) at the transmitter in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, obtaining CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a point-to-multipoint transmission in massive MIMO systems. The proposed strategy provides high sum-rate with limited CSI feedback and limited computational complexity. Innovatively, the antenna selection in our strategy is performed in a decentralized fashion successively at the receiving users. Two schemes are proposed in this work to perform the antenna selection at each user. Next, taking into consideration that the large-scale MIMO transmitter suffers from imperfect knowledge of CSI, we design a new performance criterion. Computer simulations validate that, when the CSI is perfectly known, the proposed strategy is able to achieve high performance in terms of system sum-rate while a significant reduction in both CSI feedback overhead and computational complexity is observed. Moreover, assuming imperfect CSI, the new proposed criterion achieves higher performance when the estimation accuracy is low and at high SNR regime.     

Power Adaptation for Energy Efficient Bi-directional Relaying with Quality-of-Service Requirement and Individual Peak-Power Limit

This paper addresses an optimal power adaptation (PA) problem of a two-time-slot bi-directional relaying network with a half-duplex amplify-and-forward relay. Unlike the existing studies, our goal is to develop effective PA strategies that can dynamically adjust the transmit-power levels of all the terminals to achieve energy efficiency, while satisfying the individual peak-power limit on each terminal and the quality-of-service (QoS) requirement of the network. By using the instantaneous channel state information (ICSI) and the statistical CSI (SCSI) knowledge, respectively, and with the aid of traffic information, the PA problem is analytically solved, leading to the so-called ICSI and SCSI based PA strategies with closed-form PA solutions for individual transmit-powers at the relay and the two end-terminals. Simulation results have verified the correctness of the derived expressions and confirmed the efficiency of our proposed strategies. It is shown that the proposed PA strategies can significantly reduce the total transmit-power of the network with guaranteed network QoS.

     

Performance Analysis and Enhancement for Opportunistic Analog Network Coding with Imperfect CSI

Imperfect channel state information (CSI) is among the main factors that affect system performance in wireless networks. In this paper, we investigate the impact of imperfect CSI on the performance of analog network coding (ANC) for a two-way relaying system based on opportunistic relay selection (ORS). An exact and generalized closed-form expression for system outage probability is presented in a Rayleigh flat-fading environment. To provide more insights, the closed-form asymptotic expression is then obtained. It is shown that the presence of channel estimation error causes outage probability maintain a fixed level even when a noiseless channel is adopted. Therefore, to mitigate the negative impact of imperfect CSI, we deduce the power allocation to minimize the system outage probability based on the knowledge of instantaneous channel information. Numerical results validate the accuracy of the derived expressions and highlight the effect of proposed power allocation algorithm compared with conventional uniform power allocation.     

Average power allocation based sum-rate optimization in massive MIMO systems

This paper exploits variations in the average channel gains in multi-cell multi-user massive multiple input multiple output (MIMO) systems. An average transmit power-control-based sum-rate optimization scheme is presented for the uplink of the system. The matched filtering (MF) and the zero forcing (ZF) processors are considered with perfect and imperfect channel state information at receiver (CSIR) under frequency flat Rayleigh fading channel. An average power-control-based system model is constructed for analyzing the sum-rate and formulating an optimization problem. A discrete level combinatorial optimization is performed for MF and ZF sum-rate under perfect and imperfect CSIR. The numerical results show a significant improvement in the sum-rate and power consumption. A low complexity algorithm for numerical optimization of the sum-rate is proposed. The performance of algorithm is quantified with different scenarios including different number of users, macro cells, and micro cells with low and high inter-cell interference powers. The evaluation results show that the improvement in sum-rate and energy efficiency increases with inter-cell interference power and the number of MTs.     

Capacity of practical wireless multihop broadcast networks

In a wireless multihop broadcasting scenario, a number of relay nodes may cooperate the source node in order to improve the capacity of the network. However, the imposition of total energy and maximum hop constraints to this system in a practical setting. In this paper, we study an ad-hoc network with infinitely many nodes and analytically find the number and positions of rebroadcasting relay nodes to achieve the optimal broadcast capacity. The interference due to multiple transmissions in the same geographical area is taken into account. According to the results of this theoretical model, we propose two heuristics, one distributed and one centralized, as suboptimal but practical solutions to the relay selection problem in wireless multihop broadcasting. We discuss the broadcast capacity performances and CSI (channel state information) requirements of these algorithms. The results illustrate that the benefits of peer-assisted broadcasting are more pronounced in the centralized relay selection algorithm when compared to the fully randomized and distributed selection under a realistic system model.     

双向中继信道的模拟网络编码最优功率分配

在双向信息非对称条件下,研究了基于模拟网络编码的双向中继信道中的最优功率分配问题。分别给出了中断概率最小化、和速率最大化意义下的最优功率分配闭式数学表达式,并证明了两种约束下最优功率分配问题的统一性。分析表明:现有的基于模拟网络编码的双向中继信道中的最优功率分配方法是本文提出方法在某些条件下的特例。计算机仿真分析证明了提出的最优功率分配方法在中断概率和和速率性能方面均优于平均功率分配方法。      

On co-channel and adjacent channel interference mitigation in cognitive radio networks

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation–linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.