基于鱼群算法的OFDMA自适应资源分配

针对多用户正交频分多址系统自适应资源分配问题, 提出了一种新的子载波和基于鱼群算法的功率自适应分配算法. 该算法首先对总功率在子载波间均等分布的条件下进行子载波分配,然后引入鱼群算法并根据给出的兼顾用户公平性与系统容量的适应度函数,通过全局搜索实现用户间的功率分配. 仿真结果表明,新算法在保证用户公平性的同时, 还实现了系统总的传输速率最大化. 关键词： 多用户正交频分多址 资源分配 鱼群算法 速率最大化     

一种面向中继协作频谱感知系统的自适应全局最优化算法

该文研究了冗余中继, 次用户及中继用户数目, 检测门限, 信道传输错误率等因素对中继协作频谱感知系统性能的影响, 并提出一种新的自适应全局最优化算法.该算法基于获得最大无干扰功率的自适应中继选择方法, 确定备选认知中继集合;单个次用户以信道传输错误率最小为准则, 从备选认知中继集合中自适应选择最佳中继, 使总体检测率最大;在给定目标检测率的条件下, 以系统吞吐量最大为准则, 给出了自适应全局最优化算法.仿真实验结果表明新算法信道传输精度高, 信道吞吐量大, 节约带宽资源.     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

一种裕量最大的高速载波比特功率分配算法

在国家电网公司Q/GDW 1379.4-2013技术规范对智能电能表载波通信单元功率消耗的限定要求下,为保障低压远程电力集抄台区本地通信链路的传输速率稳定可靠,给智能用电实时交互平台提供带宽支持,提出了一种裕量最大的高速载波比特功率分配算法。该算法以兼并窄带载波和宽带载波优势的G3-PLC技术为应用对象,使用约束注水法限定各子载波的传输比特数,根据时变的电力信道增益自适应调整比特功率分配方案,并响应通信服务质量QoS对系统误码率和目标传输速率的要求,结合信噪比门限算法对约束注水法进行界定和优化,得到了功率裕量最大化的表达式。仿真结果表明：提出的算法优于低反馈开销算法,能够适应低压电力线信道。在系统目标传输速率为200kbps时,达到了最大裕量值20,极大程度地降低了通信单元调制解调芯片实际的功率消耗。     

采用mQAM和孔径平均的水下弱湍流MIMO系统性能分析

采用Log-normal分布模拟海洋弱湍流信道,接收机采用等增益合并方式,在考虑雪崩光电二极管(APD)散粒噪声、信道衰减、几何损耗、Log-normal湍流和孔径平均因子等链路条件下,从理论上推导了水下无线光通信多输入多输出系统平均误码率和信道平均容量的上界表达式,并定量分析讨论了不同链路参数对系统平均误码率和信道平均容量的影响.数值结果表明,采用调制指数合适的正交幅度调制方式以及较大的天线数、接收孔径、发射光功率,均有助于提升系统性能.     

一种多用户上行放大转发中继系统中快速收敛的信道估计方法

针对传统交替最小二乘算法存在的收敛缓慢问题,本文在多用户上行放大转发中继系统中基于Levenberg Marquardt(LM)算法,提出了一种能够快速收敛的信道估计方法,实现了用户-中继信道和中继-基站信道的独立估计.在基站,通过对中继多次放大转发的信号进行建模,构造出具有平行因子结构的三维信号张量模型,并采用LM算法对该模型进行拟合,从而得到系统中两跳链路的信道状态信息.理论分析与仿真结果表明,与已有二线性交替最小二乘方法相比,所提方法具有近乎相同的估计精度;当中继放大因子矩阵为随机矩阵或者包含近似共线性相关列时,所提方法具有更快的收敛速度.     

自适应功率分配可见光MIMO信道容量研究

可见光通信（VLC）系统通常采用多阵列光源布局方式来兼顾照明与通信双重功能，因此需要使用多输入多输出（MIMO）技术进行多天线协同传输来实现高速率通信。然而传统MIMO系统中采用平均功率分配来实现空间复用，无法充分体现MIMO多天线协调传输的优势。根据每组收发天线信道状态的差异，设计了一种低计算复杂度的快速迭代注水算法，可实现依据信道特征自适应的分配信息，从而提高系统的信道容量。仿真结果表明:在相同信噪比情况下注水算法自适应功率分配系统比等功率分配系统的信道容量提高了1.25 bit/Hz左右。     

基于非正交多址接入的室内可见光通信系统EI北大核心CSCD

白光发光二极管(LED)的窄调制带宽限制了可见光通信(VLC)的系统容量。非正交多址接入(NOMA)技术通过功率复用可提高系统通信容量。结合直流偏置光正交频分复用(DCO-OFDM)和NOMA技术,设计了NOMA-DCO-OFDM系统。基于递归法给出了单个LED时VLC多径信道建模方法。在考虑限幅噪声影响时,推导了用户的信干噪比。采用分数阶功率分配、增益比功率分配和静态功率分配方法,研究系统平均和速率随LED半功率角、光电检测器的视场角(FOV)和功率分配因子的变化规律。仿真结果表明,系统平均和速率随着半功率角、FOV和功率分配因子的变化而变化,可以通过优化半功率角、FOV和功率分配因子达到系统平均和速率最大化。     

多跳噪声量子纠缠信道特性及最佳中继协议

在量子通信网络中, 最佳中继路径的计算与选择策略是影响网络性能的关键因素. 针对噪声背景下量子隐形传态网络中的中继路径选择问题, 本文首先研究了相位阻尼信道及振幅阻尼信道上的纠缠交换过程, 通过理论推导给出了两种多跳纠缠交换信道上的纠缠保真度与路径等效阻尼系数. 在此基础上提出以路径等效阻尼系数为准则的隐形传态网络最佳中继协议, 并给出了邻居发现、量子链路噪声参数测量、量子链路状态信息传递、中继路径计算与纠缠资源预留等工作的具体过程. 理论分析与性能仿真结果表明, 相比于现有的量子网络路径选择策略, 本文方法能获得更小的路径平均等效阻尼系数及更高的隐形传态保真度. 此外, 通过分析链路纠缠资源数量对协议性能的影响, 说明在进行量子通信网设计时, 可以根据网络的规模及用户的需求合理配置链路纠缠资源.     

灰霾背景下基于最优链路的低轨道量子卫星星间切换策略

为了应对灰霾对星地量子通信信道所带来的突发性干扰,根据灰霾天气下量子信号在自由空间中的衰减指数,本文提出了一种基于信号功率衰减最低的最优链路量子卫星切换策略。当目前的星地链路参数满足切换条件时,地面用户通过对不同信道中量子信号功率衰减系数的比对,测试到最小衰减卫星链路,基于对光子态Bell基的测量,完成卫星间的量子纠缠交换,建立新的纠缠信道,保证通信在切换过程中的连续性。仿真结果表明,当系统呼损率为5%,能见度为1km,地面用户数分别为50、100、300,轨道高度为300km和1400km时,量子卫星的切换成功率分别为95%、93.6%、91.8%和92%、90%、87%。由此可见,本策略能够在保证通信可靠性的前提下,实现星地间量子信道的平稳切换,提高量子卫星通信系统在灰霾背景下链路的有效性。     

Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems

This paper proposes a resource allocation scheme for hybrid multiple access involving both orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The proposed resource allocation scheme employs multi-agent deep reinforcement learning (MA-DRL) to maximize the sum-rate for all users. More specifically, the MA-DRL-based scheme jointly allocates subcarrier and power resources for users by utilizing deep Q networks and multi-agent deep deterministic policy gradient networks. Meanwhile, an adaptive learning determiner mechanism is introduced into our allocation scheme to achieve better sum-rate performance. However, the above deep reinforcement learning adopted by our scheme cannot optimize parameters quickly in the new communication model. In order to better adapt to the new environment and make the resource allocation strategy more robust, we propose a transfer learning scheme based on deep reinforcement learning (T-DRL). The T-DRL-based scheme allows us to transfer the subcarrier allocation network and the power allocation network collectively or independently. Simulation results show that the proposed MA-DRL-based resource allocation scheme can achieve better sum-rate performance. Furthermore, the T-DRL-based scheme can effectively improve the convergence speed of the deep resource allocation network.     

Cooperative diversity using per-user power control in the MAC channel

We consider a multiple access MAC fading channel with two users communicating with a common destination, where each user mutually acts as a relay for the other one as well as wishes to transmit its own information as opposed to having dedicated relays. We wish to evaluate the usefulness of relaying from the point of view of the system’s throughput (sum rate) rather than from the sole point of view of the user benefiting from the cooperation as is typically done. We do this by allowing a trade-off between relaying and fresh data transmission through a resource allocation framework. Specifically, We propose a cooperative transmission scheme allowing each user to allocate a certain amount of power for its own transmitted data while the rest is devoted to relaying. The underlying protocol is based on a modification of the so-called non-orthogonal amplify-and-forward (NAF) protocol Azarian et al. [18]. We develop capacity expressions for our scheme and derive the rate-optimum power allocation, in closed form for centralized and distributed frameworks. In the distributed scenario, partially statistical and partially instantaneous channel information is exploited.The centralized power allocation algorithm indicates that even in a mutual cooperation setting like ours, on any given realization of the channel, cooperation is never truly mutual, i.e. one of the users will always allocate zero power to relaying the data of the other one, and thus act selfishly. But in a distributed framework, our results indicate that the sum rate is maximized when both mobiles act selfishly.     

Adaptive scheduling algorithms for multimedia traffic in wireless OFDMA systems

This paper deals with the problem of finding an optimal subcarrier allocation strategy for uplink and downlink communications in an OFDMA metropolitan wireless system. Three different resource allocation algorithms are proposed by taking into account the Quality of Service (QoS) constraints and the user’s channel conditions. Moreover, a simple strategy for choosing the most appropriate Modulation and Coding Scheme (MCS) according to the channel conditions of the assigned subcarriers is also considered. Finally, this paper studies the possibility of applying an adaptive Time Division Duplexing (TDD) separation of the uplink and downlink frame, based on the instantaneous load of the network. The performance of the proposed algorithms have been derived and compared in terms of error rate and throughput. All proposed methods show a significant improvement of the overall system performance in comparison with the static subcarrier allocation in which channel information has not taken into account. Moreover, additional improvements have been obtained by applying to these schemes the proposed adaptive TDD frame separation strategy.     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission

The goal of this paper is to evaluate the performance of a proposed resource management approach that mitigates the co-channel interference (CCI) in non-orthogonal multiple access (NOMA) scenarios and maintains their enhanced spectral efficiency in distributed massive multiple input multiple output (d-massive MIMO) configurations as well. In this context, dedicated resource scheduling algorithms (RSAs) in power domain (PD) and frequency domain (FD) are studied in terms of resources’ orthogonality. Specifically, in PD case, adjusted power levels (denoted as PD-NOMA) per subcarrier and mobile terminal (MT) are assigned, while in FD case, the subcarriers are either orthogonal (FD orthogonal multiple access, FD-OMA) or non-orthogonal (FD-NOMA) to each other. The response of the d-massive MIMO system is evaluated statistically via independent Monte Carlo (MC) simulations considering a multicellular multi-user network topology and compared to typical multicellular MIMO configurations. In this framework, a simulation platform is implemented that integrates both the PD and FD RSAs. In PD, both intercell co-channel interference (Inter-CCI) and intracell co-channel interference (Intra-CCI) are modelled analytically in order to estimate the assigned power per subcarrier and MT. In the latter case (Intra-CCI), the worst-case scenario is assumed: a subcarrier can be assigned to multiple MTs (full spectral overlapping) leading to intense Intra-CCI. In FD, two subcarrier allocation approaches are considered: Pseudo-Random or maximum signal-to-noise ratio (MSNR). The simulations in both FD implementations (FD-NOMA, FD-OMA) show that, thanks to the proposed PD-NOMA scheme, each MT requires 1/4 of the maximum available power for downlink transmission. Moreover, in any of the investigated NOMA schemes, despite the intense Intra-CCI, roughly the same number of MTs as in the OMA case can access the network. Therefore, it is straightforward that, even in worst-case scenarios, the NOMA RSAs (i) are wisely exploiting the available resources; (ii) can inherently combat intense Intra-CCI and, in this way, maintain the system’s performance (number of MTs, power savings, resilience against CCI, computation complexity). Finally, it is worth noting that in contrary to typical MIMO configurations, the d-massive MIMO architecture alone can lead up to a 13.63% increase in the system’s capacity (10% maximum allowed blocking probability, 1 tier, 1 subcarrier per MT). In this case, the increased spatial separation that is achieved, along with the exploitation of NOMA RSAs, lead to a decreased CCI (both Intra- and Inter-); hence, SNR is improved and consequently the number of accepted MTs as well.     

Social-aware relay selection and energy-efficient resource allocation for relay-aided D2D communication

How to improve the flexibility of limited communication resources to meet the increasing requirements of data services has become one of the research hotspots of the modern wireless communication network. In this paper, a novel social-aware motivated relay selection method is put forward to allocate the energy efficiency (EE) resources for the device-to-device (D2D) communication. To improve system flexibility, a D2D user is selected to act as a relay named D2D-relay instead of the traditional cellular relay. The optimal relay selection strategy is formulated by searching the maximum trust value that is obtained by assessing the link stability and social connections between two users. Then, the resource allocation problem, which turns out to be a mixed-integer nonlinear fractional programming (MINLFP) problem, is solved by maximizing the total EE under physical constraint and social constraint synthetically. To improve the solution efficiency, a novel iterative algorithm is proposed by integrating the Dinkelbach theory and Lagrange dual decomposition method. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the existing social-blind and social-aware schemes, it significantly improves the probability of successful relay selection and total EE of the D2D pairs.