Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access (NOMA) is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. NOMA allows multiple terminals to share the same resource unit at the same time. The receiver usually needs to configure successive interference cancellation (SIC). The receiver eliminates co-channel interference (CCI) between users and it can significantly improve the system throughput. In order to meet the demands of users and improve fairness among them, this paper proposes a new power allocation scheme. The objective is to maximize user fairness by deploying the least fairness in multiplexed users. However, the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker (KKT) constraints. Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.     

Power control for interference mitigation by evolutionary game theory in uplink NOMA for 5G networks

The demand for mobile uplink traffic has increased significantly in the past few decades with the development of the Internet of Things (IoT) and mobile Internet. This has subsequently imposed challenges on 5G networks to provide high spectral efficiency and low-power massive connectivity. Non-orthogonal multiple access (NOMA) is a viable alternative to the current state-of-the-art orthogonal multiple access (OMA) techniques to address the challenges in 5G systems. In addition, a power control (PC) mechanism to mitigate the effect of interference between users can be accommodated to improve network performance. In this paper, we discuss the basic principles, key features, and strengths/weaknesses of the various power domain NOMA schemes. Moreover, we propose an uplink PC scheme for the users of a power domain NOMA network. The proposed PC method makes use of the evolutionary game theory (EGT) model to adaptively adjust the transmitted power level of the users which helps in mitigating user interference. A successive interference cancellation (SIC) receiver is applied at a base station (BS) in order to separate the users’ signals. By performing simulations, we show that the proposed EGT-based PC scheme achieves higher network efficiency, spectral efficiency, and energy efficiency.     

An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

With the emergence of 5G mobile multimedia services, end users’ demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communication mode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks. One of the important factors which affects the quality-of-service (QoS) of D2D communications is co-channel interference. In order to solve this problem of co-channel interference, this paper proposes a graph coloring based algorithm. The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource. The proposed algorithm also provides simpler power control. The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired. Simulation results show that the proposed algorithm effectively reduced the co-channel interference, power consumption and improved the system throughput as compared with existing algorithms.     

An Efficient Genetic Hybrid PAPR Technique for 5G Waveforms

Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in designing the NOMA waveform. However, the arrangement of NOMA is different from the orthogonal frequency division multiplexing. Thus, traditional reduction methods cannot be applied to NOMA. A partial transmission sequence (PTS) is commonly utilized to minimize the PAPR of the transmitting NOMA symbol. The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol. In this study, an efficient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm (BFOA) for PTS (BFOA-PTS). The PAPR minimization is accomplished in a two-stage process. In the initial stage, PTS is applied to the NOMA signal, resulting in the partition of the NOMA signal into an act of sub-blocks. In the second stage, the best phase factor is generated using BFOA. The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS. The simulation outcomes reveal that the BFOA-PTS efficiently optimizes the PAPR performance with inconsequential complexity. The proposed method can significantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.     

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact of signal to noise ratio (SNR) per subcarrier and the channel gains of the paired users on the performance of NOMA in terms of the capacity and the user fairness is presented. Next, a closed-form equation for the power allocation coefficient of CMFPA as a function of SNR, and the channel gains of the paired users is provided. In addition, to jointly optimize UP and PA in NOMA systems an efficient low-complexity UP (ELCUP) method is proposed to be incorporated with the proposed CMFPA to compromise the proposed joint resource allocation (JRA). Simulation results demonstrate that the proposed CMFPA can improve the capacity and fairness performance of existing UP methods, such as conventional UP, and random UP methods. Furthermore, the simulation results show that the proposed JRA significantly outperforms the existing schemes and gives a near-optimal performance.     

Cross-layer design for tree-type routing and level-based centralised scheduling in IEEE 802.16 based wireless mesh networks

Infrastructure wireless mesh network, also named as mesh router, is one key topology for the next generation wireless networking. In this work, the performance optimisation for the infrastructure wireless mesh network is presented and the sub-optimum solution mechanism is investigated. A cross-layer design for tree-type routing, level-based centralised scheduling and distributed power control theme is proposed as the sub-optimum solution strategy. The cross-layer design relies on the channel information and the distributed transmission power control in the physical layer, and the wireless scheduling in the medium access control (MAC) layer, as well as the routing selection mechanism in the MAC upper layer. In this work, a modified distributed power control algorithm is proposed first. In addition, a tree-type routing construction algorithm for centralised scheduling is presented to improve the network throughput by jointly considering interference and hop-count to construct the routing tree. Simulation results show that the proposed cross-layer design strategy can effectively improve the network throughput performance, decrease the power consumption and achieve better performances.     

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device (D2D) communication which is also called terminal pass-through technology. It can directly communicate between devices under the control of a base station and does not require a base station to forward it. The advantages of applying D2D communication technology to cellular networks are: It can increase the communication system capacity, improve the system spectrum efficiency, increase the data transmission rate, and reduce the base station load. Aiming at the problem of co-channel interference between the D2D and cellular users, this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning, which creates multi-agent learners from multiple D2D users, and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users. The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted. The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users. Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.     

Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed to maximize the market advantage of LEO satellites and reduce interference between LEO and NLEO satellites. In the proposed model, the resource price is formulated as the dynamic state of the LEO satellites, using the resource allocation strategy as the control variable. Based on the proposed dynamic game model, an open-loop Nash equilibrium is analyzed, and an algorithm is proposed for the resource pricing and allocation problem. Numerical simulations validate the model and algorithm.     

Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation scheme, including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker (KKT) conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning (RL). Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations. The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.     

毫米波大规模MIMO系统波束选择方案的设计

针对毫米波大规模MIMO系统采用全数字预编码时,所需射频链路数量过多而导致能量消耗高的问题,提出了一种基于透镜的波束选择方案。该方案首先通过分析用户受干扰的可能性,将所有的用户分为干扰用户组和非干扰用户组,然后对于非干扰用户,直接利用最大功率准则进行波束选取,而对于干扰用户,则通过低复杂度增量算法选择合适的波束使系统和速率最大化。仿真结果表明,在有效减少系统所需射频链路数量和降低计算复杂度的基础上,该方案的系统和速率能够达到接近全数字预编码方案的水平,并且能够获得更高的能量效率。     

Modelling and resolving the joint problem of capacity expansion and allocation with multiple resources and a limited budget in the semiconductor testing industry

In the semiconductor testing process, many resources such as testers, handlers, loadboards and toolings are required to be ready simultaneously so that testing tasks can be conducted. A limited budget under depressed economy enhances the need for exploring better solutions of testing capacity expansion and allocation. However, to maximize profit as planning with multiple resources is very challenging. This study focused on the issues pertaining to the decisions of (1) the type and number of testers that should be invested to deal with forthcoming orders at a semiconductor testing facility under a constrained budget and (2) the allocation of tester capacity for the orders so as to maximize company profit with limited, multiple resources. Owing to the high computational complexity of the problem, the study developed a genetic algorithm to resolve the two issues simultaneously. A mathematical model was developed to formalize the problem and serve as a benchmark for comparison with the proposed algorithm that attacked the same problem more efficiently. Taguchi experimental design was employed to find the most appropriate parameters for the proposed genetic algorithm under a variety of budget set-up. Experimental results indicated that the proposed algorithm was robust enough to budget plans, and its performance approximated closely with that of the mathematical model.     

基于细菌觅食和蚁群算法的工艺路线优化

针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化（bacteria foraging ant colony optimization,BFACO）算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。     

Outage Capacity Analysis for Cognitive Non-Orthogonal Multiple Access Downlink Transmissions Systems in the Presence of Channel Estimation Error

In this paper, we propose a downlink cognitive non-orthogonal multiple access (NOMA) network, where the secondary users (SUs) operate in underlay mode. In the network, secondary transmitter employs NOMA signaling for downlink transmission, and the primary user (PU) is interfered by the transmission from SU. The expressions for the outage probabilities are derived in closed-form for both primary and secondary users in the presence of channel estimation error. Numerical simulation results show that the channel estimation error and the inter-network interference cause degradation of the downlink outage performance. Also the power allocation and the location have a significant impact on the outage probability. The numerical experiments demonstrate that the analytic expressions of the outage probabilities match with the simulation results.     

An improved algorithm for optimizing a closed queueing network model of a flexible manufacturing system

Vinod and Solberg (1985) address an optimization problem within the framework of a closed queueing network model for a Flexible Manufacturing System (FMS). The above problem is referred to as the Optimal System Configuration Problem of an FMS. The optimization parameters in the problem are the number of machines at each workstation and the total number of jobs circulating in the system. In this paper we suggest a more efficient algorithm for the solution of the above-mentioned problem. Our algorithm is of an implicit enumeration nature and derives its efficiency from the exploitation of properties of the throughput function of a closed queueing network. The efficiency of the algorithm is demonstrated with the use of extensive computational results.     

Fairness and throughput enhancement based resource allocation scheme for underlay cognitive radio networks

Acquiring good throughput and diminishing interference to primary users (PU) are the main objectives for secondary users in a cognitive radio (CR) network. This paper proposes a centralized subcarrier and power allocation scheme for underlay multi-user orthogonal frequency division multiplexing considering the rate loss and the interference those the PU can tolerate. The main purpose of the proposed scheme is to efficiently distribute the available subcarriers among cognitive users to enhance both the fairness and the throughput performance of the cognitive network while maintaining the QoS of primary users. Simulation results show that the proposed scheme achieves a significantly higher CR network throughput than that of the conventional interference power constraint (IPC) based schemes and provides a significantly enhanced fairness performance. Also, contrary to the conventional IPC based schemes, the proposed scheme is able to significantly increase the achieved throughput as the number of CR users increases.     

5G异构接入网系统频谱资源的统一化表示及动态分配

针对5G通信网络系统提供以大容量、高速率、低时延为主要特征的服务导致频谱资源紧缺现象日趋严重的问题,进行了5G频谱资源动态分配的研究,给出了5G异构接入网频谱资源特征描述、表达方法和一种基于双层优化的动态频谱分配方案。仿真实验结果表明,给出的动态频谱分配方案在系统吞吐量和分配公平性两个指标上均有较好表现,具有较好应用价值。     

克隆选择算法在多用户信号检测中的应用

为了减少计算复杂度,改善CDMA(Code Division Multiple Access)系统性能．将具有解决复杂组合优化问题能力的克隆选择算法(CLONALG)应用于码分多址系统多用户信号检测中．确定每一个用户传输的符号位．仿真结果表明,基于克隆选择算法的多用户检测器不仅具有克服多址干扰,抗“远-近效应”的能力,而且通过调整算法参数可以在检测时间和系统平均误码性能间进行均衡,与最佳多用户检测器相比明显地降低了计算复杂度．     

Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.     

多级分销网络设计及库存控制整合优化模型

从供应链集成的角度出发,基于多目标规划,根据排队论探讨了随机性需求下多级分销网络设计与库存控制的整合优化问题,提出了多级分销网络设计和库存控制整合优化的多目标规划模型.针对遗传算法收敛速度慢、易陷入局部最优等缺点,采用了基于并列选择法的遗传-模拟退火算法混合优化策略.实验证明,模拟退火算法提高了遗传算法的全局搜索能力,改善了遗传算法的求解性能.