多小区蜂窝网络波束成形优化策略

为了克服多小区的邻小区干扰问题,该文在多小区蜂窝网络中,研究了多小区多用户下行协同传输技术。该文提出一种联合优化基站和中继的波束成形权重的迭代算法,在基站和中继总功率限制下最大化最差用户接收信干噪比(Signal-to-Interference-and-Noise Ratio, SINR)。该文提出的联合优化波束成形策略,可以应用半正定松弛技术(Semi-Definite Relaxation, SDR)得到有效的解决。仿真表明,在多小区通信系统中,该文提出的迭代算法只需要少量的迭代次数就可以达到近似最优性能,并且在性能和资源消耗之间给出一种折中的传输策略。     

基于公平性对偶理论的多小区下行协同波束成形算法

为了实现最大化最差用户速率准则下的多小区下行协同波束成形优化,首先基于拉格朗日对偶理论推导出了多小区下行波束成形最差用户信干噪比最大化优化问题的虚拟上行对偶问题,进而提出了一种联合利用二分法和几何规划有效迭代解决该虚拟上行对偶问题的算法。通过把对偶上行链路优化问题的解转换到原始下行链路优化问题,从而获得多小区下行链路波束成形的波束矢量和发射功率。仿真结果表明,相比已有的多小区下行协同波束成形算法,所提算法在最差用户速率性能以及能效归一化速率性能指标方面都具有明显优势。     

一种分布式多点协同波束成形和用户调度算法

为了高效实现多点协同下行链路的协同传输,该文基于最大化最小信干噪比准则提出了一种单层迭代分布式协同波束成形算法,并且证明了该算法的全局收敛性;利用该算法中优化波束的特征,进而提出了一种分布式协同用户调度算法。仿真结果表明,所提分布式协同波束成形和用户调度算法可获得明显优于已有算法的最小信干噪比性能,且只需要很少量的基站间信息交互。     

基于对偶理论的多点协同联合发送波束成形算法

该文针对单基站功率约束的多点协同联合发送多输入单输出干扰下行链路系统,利用拉格朗日对偶理论研究了下行链路最大化最小信干噪优化问题与虚拟上行链路最小化最大信干噪比优化问题间的对偶关系。基于对偶关系和次梯度理论,提出了一种求解虚拟上行链路最小化最大信干噪比优化问题的内外层交替迭代优化波束成形算法;同时,给出了所提算法的收敛性证明;利用实数浮点运算理论分析了所提算法的复杂度;数值仿真验证了所提算法的有效性。     

基于干扰和功率控制的多小区分布式波束成形

在多小区干扰信道中,基于分布式竞争设计,提出两种优化基站发射波束成形矩阵的设计,使每个小区的最差信干噪比最大.一种是基于干扰控制的设计,利用差异化的干扰系数,抑制小区间的干扰,提高系统性能.另一种是基于功率控制的设计,通过调整各基站的发射功率,提高系统性能.这两种设计,都能保持算法的分布式特性,又能有效地提高用户信干噪比.仿真分析表明,基于功率控制的设计在功率大的情况下,性能优于基于干扰控制的设计.     

全双工MIMO中继系统高性能的波束成型算法

为了更好地抑制全双工多入多出技术(MIMO)中继系统的自干扰,提高信息传输速率,提出了一种新型的波束成型组合算法。该组合在源节点和目的节点采用奇异值分解的波束成型向量,而在中继站采用基于最大化信干噪比接收波束成型来抑制中继端的自干扰,以及最大化信泄噪比发射波束成型矩阵来减少中继发送端泄漏到中继接收机的信号组合波束成型算法。为了降低复杂度,随后引入了交替迭代结构来进一步优化中继接收和发送波束成型矩阵,并比较了不同组合波束成型方案的和速率性能。仿真结果表明,与现存的波束成型组合相比,当干噪比较低时,提出的组合算法能够提供更优的和速率性能。     

TD-SCDMA下行波束成形技术研究

讨论了TD-SCDMA系统的下行信号模型,并根据TD-SCDMA系统上行信道估计可以直接用于下行波束赋形的特点,研究了特征值分解方法下的下行波束成形算法的性能并仿真比较了其与基于最大径准则等下行波束形成算法在不同环境下的性能。     

一种基于内惩罚函数的多小区波束成形算法

本文提出一种利用惩罚函数的分布式波束成形迭代算法,该算法利用当效用函数的梯度为零时的局部最大值的性质。建议算法的收敛性已被证明。仿真结果表明,对比传统算法,建议算法在抑制小区内和小区间干扰方面具有较好的性能,特别是当用户数增加时建议算法具有更高的速率。     

能量有效的认知多小区协同波束赋形算法

针对认知多小区多用户下行传输链路,提出了一种基于能量效率最大化准则的协同波束赋形优化方法。该方法采用迫零消除小区内用户间干扰,在保证用户最小速率需求及认知干扰约束的同时,实现了能量效率和频谱效率的同步改善。为了分布式求解优化问题,通过约束泄露干扰并利用半定松弛,将其转换为凸问题,在此基础上,采用部分对偶分解方法将多小区联合优化问题分解为一组单小区优化问题,从而实现了分布式求解。仿真结果表明,该方法不仅实现了能量效率和频谱效率的有效折中,而且达到了集中式算法的性能。     

窃听信道下认知多小区中的协同波束赋形算法

小区之间优化变量耦合使多小区联合优化问题为非线性非凸问题,通过约束泄露干扰提出了基于半正定松弛的中心式协同波束成形算法,在此基础上,采用部分对偶分解方法将中心式联合优化问题分解为一组单小区优化问题,提出了一种分布式迭代求解算法。结果表明,所提算法在保证系统安全速率需求和认知干扰约束的条件下实现了系统功耗的降低,此外,分布式算法不仅能降低实现复杂度,而且能达到中心式算法的性能。     

Non-iterative Coordinated Beamforming for Multicell MIMO Heterogeneous Networks

Wireless Personal Communications - This paper presents ultra-wideband channel characteristics in the presence of float glass slab as obstacle of varied thickness (4 mm, 8 mm,...     

Joint Beamforming and Power Control for Downlink Multi-cell Massive MIMO System

Wireless Personal Communications - Because of versatility and portability brought by remote system made it conceivable in numerous applications. Among all the contemporary remote systems, mobile ad...     

大规模MIMO系统中基于溢出概率的鲁棒协作波束设计

大规模多输入多输出(Massive MIMO)技术通过在基站端配置大规模天线能有效提升5G蜂窝系统容量。考虑信道估计误差对系统性能的影响,该文在多小区大规模MIMO系统中形成了用户信干噪比的非溢出概率约束下最小化系统功率的优化问题。针对非凸概率约束中下行波束难于求解的问题,该文根据矩阵迹的性质将优化问题中的非凸约束缩放,进而提出上下行对偶算法求解波束矢量。为进一步减少多小区系统中信令开销,基于大系统分析,提出了仅采用大尺度信息的分布式算法。仿真结果表明,所提的分布式算法与对偶算法相比,在保证用户信干噪比的概率约束时,降低了大规模MIMO系统中传输瞬时信道状态信息的开销,同时具有良好的鲁棒性。     

Pilot Allocation and Power Control in Cell-Free Massive MIMO Systems

In this paper we consider a cell-free massive multiple-input multiple-output (MIMO) system which is composed of a large number of access points (APs) and a much smaller number of users. Each AP obtains the channel estimate from the uplink pilot and apply maximum ratio transmission in the downlink communication. Under the assumptions that the channels between the APs and users are Rician fading and the number of users is larger than that of orthogonal pilots, we derive the closed-form expression for the downlink achievable rate. Then, in order to maximize the achievable rate, we propose pilot allocation and power control algorithms for cell-free massive MIMO. Simulation results show that the proposed algorithms can achieve good system performance.

     

Grant-Free Random Access in Pilot-Reused Multicell Massive MIMO Systems with Backoff Mechanisms

Grant-free random access(RA) is attractive for future network due to the minimized access delay. In this paper, we investigate the grantfree RA in multicell massive multiple-input multipleoutput(MIMO) systems with pilot reuse. With backoff mechanism, user equipments(UEs) in each cell are randomly activated, and active UEs randomly select orthogonal pilots from a predefined pilot pool, which results in a random pilot contamination among cells.With the help of indicators that capture the uncertain...     

Joint Pilot Design and Beamforming Optimization in Massive MIMO Surveillance Systems

This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output(MIMO)frequency-division duplexing(FDD) wireless legitimate surveillance system. With the proposed scheme,the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total ...     

User Selection Algorithms for MU‐MIMO Systems with Coordinated Beamforming

In this paper, we propose two novel user selection algorithms for multiuser multiple‐input and multiple‐output downlink wireless systems, in which both a base station (BS) and mobile stations (MSs) are equipped with multiple antennas. Linear transmit beamforming at the BS and receive combining at the MSs are used to avoid interference between users and find a better sum‐rate capacity performance. An optimal technique for selecting users would entail an exhaustive search, which in practice becomes computationally complex for a realistic number of users. Suboptimal algorithms with low complexity are proposed for a coordinated beamforming scheme. Simulation results show that the performance of the proposed algorithms is better than that provided by previous algorithms and is very close to an optimal approach with reduced complexity.