RIS-Assisted Federated Learning in Multi-Cell Wireless Networks

Over-the-air computation(AirComp)based federated learning(FL)has been a promising technique for distilling artificial intelligence(AI)at the network edge.However,the performance of AirComp-based FL is decided by the device with the lowest channel gain due to the signal alignment property.More importantly,most existing work focuses on a single-cell scenario,where inter-cell interference is ignored.To overcome these shortages,a reconfigurable intelligent surface(RIS)-assisted AirComp-based FL system is proposed for multi-cell networks,where a RIS is used for enhancing the poor user signal caused by channel fading,especially for the device at the cell edge,and reducing inter-cell interference.The convergence of FL in the proposed system is first analyzed and the optimality gap for FL is derived.To minimize the optimality gap,we formulate a joint uplink and downlink optimization problem.The formulated problem is then divided into two separable nonconvex subproblems.Following the successive convex approximation(SCA)method,we first approximate the nonconvex term to a linear form,and then alternately optimize the beamforming vector and phase-shift matrix for each cell.Simulation results demonstrate the advantages of deploying a RIS in multi-cell networks and our proposed system significantly improves the performance of FL.

Resource provisioning for computation and communication in multi-cell wireless networks

The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning problem for edge nodes has to be investigated to provide prior information for future system configurations.This work focuses on how to quantify the computation capabilities of access points at network edges when provisioning resources of computation and communication in multi-cell wireless networks.The problem is formulated as a discrete and non-convex minimization problem,where practical constraints including delay requirements,the inter-cell interference,and resource allocation strategies are considered.An iterative algorithm is also developed based on decomposition theory and fractional programming to solve this problem.The analysis shows that the necessary computation capability needed for certain delay guarantee depends on resource allocation strategies for delay-critical tasks.For delay-tolerant tasks,it can be approximately estimated by a derived lower bound which ignores the scheduling strategy.The efficiency of the proposed algorithm is demonstrated using numerical results.

Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

智能反射面辅助OFDMA云接入网的资源分配方法

本文研究了智能反射面(IRS)辅助OFDMA(Orthogonal Frequency Division Multiple Access,正交频分多址接入)云无线接入网(C-RAN)的下行链路传输系统,其中基带处理单元(BBU)池通过多个远端射频头(RRH)与多个用户进行通信.RRH到用户的接入链路采用OFDMA接入技术.对于BBU池到RRH,采用无线前传链路,并且部署多个IRS以增强链路传输能力.在BBU池和每个RRH发射功率约束下,本文提出通过联合优化前传链路和接入链路资源配置使下行用户和速率最大化.由于该资源配置问题是非凸的,首先采用连续凸逼近(SCA)对目标以及约束条件进行转换.其次,将转换后的问题拆分成三个子问题来交替性求解.最后,计算机仿真结果显示了所提出的联合资源分配方法与其他基准方案相比具有显著的传输性能增益.

Resource Allocation for Channel Estimation in Reconfigurable Intelligent Surface-Aided Multi-Cell Networks

Reconfigurable intelligent surface(RIS)is a promising solution to deal with the blockage-sensitivity of millimeter wave band and reduce the high energy consumption caused by network densification. However, deploying large scale RISs may not bring expected performance gain due to significant channel estimation overhead and non-negligible reflected interference.In this paper,we derive the analytical expressions of the coverage probability, area spectrum efficiency(ASE)and energy efficiency (EE)of a downlink RIS-aided multi-cell network.In order to optimize the network performance, we investigate the conditions for the optimal number of training symbols of each antenna-to-antenna and antenna-to-element path (referred to as the optimal unit training overhead) in channel estimation.Our study shows that:1)RIS deployment is not“the more, the better”, only when blockage objects are dense should one deploy more RISs;2) the coverage probability is maximized when the unit training overhead is designed as large as possible;3)however,the ASE-and-EE-optimal unit training overhead exists. It is a monotonically increasing function of the frame length and a monotonically decreasing function of the average signal-to-noise-ratio (in the high signal-to-noise-ratio region). Additionally,the optimal unit training overhead is smaller when communication nodes deploy particularly few or many antennas.

Resource Allocation based on Shared Criterion in OFDMA Distributed Radio Access Network

Distributed radio access network (DRAN) is a novel wireless access architecture and can solve the problem of the available spectrum scarcity in wireless communications. In this paper, we investigate resource allocation for the downlink of OFDMA DRAN. Unlike previous exclusive criterion based algorithms that allocate each subcarrier to only one user in the system, the proposed algorithms are based on shared criterion that allow each subcarrier to be allocated to multiple users through different antennas and to only one user through same antenna. First, an adaptive resource allocation algorithm based on shared criterion is proposed to maximize total system rate under each user's minimal rate and each antenna's maximal power constraints. Then we improve the above algorithm by considering the influence of the resource allocation scheme on single user. The simulation results show that the shared criterion based algorithm provide much higher total system rate than that of the exclusive criterion based algorithm at the expense of the outage performance and the fairness, while the improved algorithm based on shared criterion can achieve a good tradeoff performance.

Noisy Chaotic Neural Network for Resource Allocation in High-Speed Train OFDMA System

High-speed train communication system is a typical high-mobility wireless communication network. Resource allocation problem has a great impact on the system performance. However, conventional resource allocation approaches in cellular network cannot be directly applied to this kind of special communication environment. A multidomain resource allocation strategy was proposed in the orthogonal frequency-division multiple access(OFDMA) of high-speed. By analyzing the effect of Doppler shift, sub-channels, antennas, time slots and power were jointly considered to maximize the energy efficiency under the constraint of total transmission power. For the purpose of reducing the computational complexity, noisy chaotic neural network algorithm was used to solve the above optimization problem. Simulation results showed that the proposed resource allocation method had a better performance than the traditional strategy.

Multi Multi-Cell Uplink Interference Management Cell Uplink Interference Management: A Distributed Power Control Method

This paper investigates a multi-cell uplink network,where the orthogonal frequency division multiplexing(OFDM)protocol is considered to mitigate the intra-cell interference.An optimization problem is formulated to maximize the user sup-porting ratio for the uplink multi-cell system by optimizing the transmit power.This paper adopts the user supporting ratio as the main performance metric.Our goal is to improve the user supporting ratio of each cell.Since the formulated optimization problem is non-convex,it cannot be solved by using traditional convex-based optimi-zation methods.Thus,a distributed method with low complexity and a small amount of multi-cell interaction is proposed.Numerical results show that a notable perfor-mance gain achieved by our proposed scheme compared with the traditional one is without inter-cell interaction.

A time domain multiple-CFOs and CIRs estimation algorithm over wireless multimedia sensor networks

Channel parameters estimation in an orthogonal frequency division multiple access(OFDMA)systemfor the receiver station is a multi-dimensional(MD)optimization problem,because every user node has aseparate local oscillator and every transmitter to receiver link has individual carrier frequency offset(CFO)and channel impulse response(CIR)parameters .In order to reduce the computational complexityfor MD optimization,a time domain CFOs and CIRs estimation algorithm over the OFDMA based wirelessmultimedia sensor networks(WMSN)is proposed in this paper.In this algorithm,the receiver stationcan decouple the signal from every node by correlation based on specially designed training sequences,sothat the MD optimization problem is simplified to an 1-D optimal problem.It is proved that the multipleCFOs can be identified from the correlation result using the phase shift of the consecutive training se-quences .Based on the CFOs estimation result,the CIRs can then be estimated according to the minimummean square error(MMSE)criterion.The theoretic analysis and simulation results show that the proposedalgorithm can effectively decouple the signal from different user nodes and the bit error rate(BER)per-formance curves are close to the ideal estimation when the user number is not large.

OFDMA中继网络变时域节能资源分配策略

现有正交频分多址接入(OFDMA)中继网络资源分配的研究均采用固定时域配置下的频域分配和功率分配,不能很好适应用户业务在时域上的变化。该文针对OFDMA中继网络提出一种可最优节能的资源分配策略和一种简化的节能资源分配策略,为资源分配问题建立一般化的模型,即动态分配时域资源、频域资源和功率资源,所建模型具有很强的灵活性和适应性,不仅适用于固定时域分配系统,也适用于非固定时域分配系统。此外针对非满负荷业务,在保证用户服务质量的情况下,以节能为目标对无线非协作中继网络的资源分配进行能效最大化建模,使用拉格朗日乘数法对模型求解。考虑到算法复杂度,应用指派问题中的匈牙利算法设计出一种简化的资源分配策略。理论和仿真结果表明,最优节能资源分配算法能够得到能效的最大化,而简化节能资源分配算法与最优节能资源分配算法在能效上的差距不足5%,但算法复杂度得到了显著降低。而且,动态分配时域资源比固定时域分配对用户分布不均或链路分布不均有更强的适应性。

多小区OFDMA网络中用于有效无线资源分配的ICI管理技术(英文)

Two Inter-cell Interference (ICI) management algorithms: Primary Interference Balancing (PIB) algorithm and Interfering Bits Loading Avoidance (IBLA) algorithm are proposed for canceling the ICI effects which the existing efficient radio resource allocation algorithms do not consider. The efficient radio resource allocation algorithm, i.e., Pre-assignment and Reassignment (PR) algorithm, obtains the lowest complexity and achieves good throughput performance in single cell OFDMA system. However, in multi-cell multi-sector OFDMA networks, PR algorithm is not applicable because it does not take ICI into consideration. The proposed PIB algorithm balances the number of loading bits for the desired User Equipment (UE) and the major interfering UE, as well as optimizes the SINR performance; meanwhile, IBLA avoids loading certain number of interfering bits which would make SINR unqualified. Simulations confirm the ICI management effectiveness and feasibility of both the proposals.

基于非合作博弈的OFDMA无线多跳中继网络上行链路资源分配算法

研究了基于OFDMA多址技术的无线多跳中继网络上行链路资源分配问题。首先,在最大发射功率等约束条件下,建立了多小区OFDMA无线多跳中继网络上行链路的资源分配优化模型。将非合作博弈论和定价机制引入后,该优化问题可转化为在每个子信道上独立地进行功率分配。基于非合作博弈的功率分配模型中的纳什均衡点的存在性和唯一性得到了证明,并给出了具体的分布式求解算法。仿真结果表明,所提算法能在大幅减少系统总发射功率的情况下,有效地提升系统吞吐量,达到较高的能效比。

多小区OFDMA系统的分布式资源分配方案

该文研究了多小区OFDMA系统中下行链路的资源分配问题,主要考虑同信道干扰和频率选择性衰落对资源分配的影响,提出一种基于基站的分布式分配方案,同时考虑用户的服务质量要求(QoS)及公平性,完成子载波和功率的联合分配。算法只要求基站间简单的子载波使用计数的信息交换,不会给系统带来很大的信令负载。仿真表明,通过调整参数,该算法可以取得不同性能间的折衷。

OFDMA毫微微小区双层网络中基于分组的资源分配

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享的正交频分多址(OFDMA)毫微微小区网络中,毫微微小区之间的同层干扰以及毫微微小区与宏小区(Macrocell)之间的跨层干扰严重限制了系统的性能。针对这两种干扰,该文提出一种基于分组的资源分配算法。该算法包括两部分:一部分是宏基站先利用改进的匈牙利算法为宏小区用户分配信道,再用注水算法分配功率,保证宏小区用户的正常传输;另一部分是在避免干扰宏小区用户的基础上,先采用模拟退火算法对毫微微小区进行分组,再进行信道和功率分配,满足毫微微小区用户的数据速率需求,最大化频谱效率。仿真结果表明,该算法有效地抑制了这两种干扰,既能保证用户的数据速率需求,又能有效提升网络频谱效率。

OFDMA多跳中继蜂窝网络的干扰协调策略

多跳中继蜂窝网络由于具有吞吐率高和覆盖性强的优点受到广泛关注。但是,中继站的引入改变了传统蜂窝网络的频率规划策略,因此如何在策略设计中对中继链路进行高效的频率资源分配成为极具挑战的问题;其次,小区频率复用越紧密,同频干扰越严重,对系统的整体性能产生影响。本文基于IEEE 802.16j/m标准,以下行链路的无线资源管理为基本手段,设计合理的资源复用策略及其对应的多跳中继下行子帧结构,在保证网络的资源利用率的同时,降低干扰给相邻小区边缘用户带来的影响;再将高效的比例公平调度算法扩展到多跳中继蜂窝网络,旨在兼顾系统整体性能和用户公平性。仿真和分析结果表明,干扰协调与资源调度策略相结合能显著提升系统吞吐率并改善用户公平性。

OFDMA无线多跳蜂窝网中的节能型资源分配策略

为在基于正交频分多址接入技术的无线多跳蜂窝网中实现节能型资源分配,将无线多跳蜂窝网描述为树形分层拓扑结构,规定了该结构中的奇数层和偶数层节点在系统奇时隙和偶时隙中的发送-接收关系,建立了可同时适用于奇时隙和偶时隙的基于系统总发射功率的优化目标函数,并确定了包括链路比例公平因子等限制条件.考虑到难以得到实际系统最优解,在尽量减少计算复杂度的前提下,提出了一种次优化分配策略,使系统总发射功率最小化.仿真结果表明,与基于正交频分复用的时分多址接入等传统固定资源分配方法相比,运用本文提出的资源分配策略可使系统的总发射功率减少约1.5 dB,并能保持各条链路的比例公平性.

宽带电力线认知通信系统OFDMA子载波分配研究

随着配电设备数量不断增加,宽带电力线载波频谱资源渐显匮乏。文章认为,引入认知频谱的概念组成宽带电力线认知通信系统,可在电力线衰落的信道上侦测其他配电设备不用或没有充分利用的频率,采用正交频分多址技术进行通信,并提出一种基于分布式宽带电力线认知通信系统的子载波分配方案,仿真结果表明了所提算法的有效性和优势。

基于两跳匹配的OFDMA中继网络联合资源分配算法

针对OFDMA(正交频分复用多址)中继网络的两跳特性,提出一种基于两跳匹配的中继网络联合资源分配算法。首先根据中继网络的两跳性建立两跳速率匹配模型,然后利用对偶分解理论将中继网络资源分配的主问题分解为中继选择、子载波分配和功率分配3个主要的子问题并进行联合优化,同时基于中继网络两跳性在子载波分配的过程中考虑两跳子载波配对,以逼近系统最优解。最后为了保证算法的公平性,考虑子载波分配因子约束以优化子载波分配。仿真结果表明,所提算法将中继选择、两跳子载波配对与功率分配联合优化以进一步提升系统吞吐量,同时引入子载波分配因子约束,保证了算法的公平性。

认知OFDMA混合网络模型中基于QoE的资源分配协议(英文)

提出了一种应用于基于正交频分多址接入(OFDMA)的认知网络模型中的资源分配协议,其认知网络采用了融合overlay和underlay的混合网络模型.在不影响主用户通信质量的前提下,overlay模型允许认知用户利用主用户未使用的空闲子信道;underlay模型允许认知用户和主用户共同占用子信道.该协议旨在最大化认知用户的体验质量(Qo E),动态地在overlay和underlay模型之间切换,并通过平均意见分数(MOS)函数进行量化,而非简单地满足各个用户在物理层和MAC层的限制条件.从对认知用户的文件传输和视频服务的仿真结果可看出,所提出的资源分配协议具有较高的频谱效率.

基于OFDMA-WLAN系统的上行MAC协议研究

基于OFDMA-WLAN系统提出了一种改进的上行MAC协议,用以提高IEEE802.11无线局域网(Wireless Local Area Network,WLAN)在大量站点(stations,STAs)存在时的信道利用率和吞吐量。在OFDMA-WLAN系统存在多个子信道时,该协议使多个STA同时在时域与频域上按照CSMA/CA方式发送信道接入的请求帧,在不需要额外增加信道与射频的基础上,解决了随机接入带来的静默问题。数值分析结果表明,与现有的基于OFDMA的上行MAC协议FICA相比,本协议较好地提高了系统的信道利用率和吞吐量。