Prediction‐based MAC‐layer sensing in cognitive radio networks

We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.     

Subgrouping‐based multicast transmission over high‐throughput satellite systems with beam cooperation

This paper investigates the multicast transmission for multicast services in high‐throughput satellite (HTS) systems. Considering the multibeam multicast feature of HTSs, cooperative transmission among beams is involved in to improve the efficiency of the multicast transmission. Since the multicast transmission rate depends on the worst user channel state, all the users experience an unreasonably low rate. In this situation, subgrouping techniques are employed to increase transmission rates of users. A subgrouping‐based multicast transmission problem aiming at maximizing the lowest transmission rate of the users is studied to guarantee fairness among users. We formulate the problem as a max–min optimization problem and propose two low‐complex subgrouping algorithms for this problem. Additionally, we also consider multicasting in a single beam and devise a two‐layer transmission scheme for it. In the performance evaluation part, besides the impact of parameters on subgrouping performance, we analyze the performance and the computational complexity of the proposed algorithms. The results indicate that the two subgrouping algorithms can achieve favorable performance with low complexity.     

Joint queue control and user scheduling in MIMO broadcast channel under zero‐forcing multiplexing

This paper studies the problem of queue control and user scheduling in multi‐antenna broadcast (downlink) systems under zero forcing beamforming transmit strategy. In the system, we assume that the data packet arrives randomly to the buffered transmitter. By taking the broadcast channel as a controlled queueing system, we deduce the property of queue control function that maximizes the weighted system throughput while guarantees the delay fairness among users. We also present a low‐complexity user selection algorithm with the consideration of queue state and channel state together. Simulation results show that the joint queue control and user selection policy can achieve considerable fairness and stability among users. Copyright © 2009 John Wiley & Sons, Ltd.     

基于应用时间窗多用户MIMO-OFDM系统中的比例公平算法

该文针对基于延时信道状态信息的多用户MIMO-OFDM系统,在用户比例速率要求和功率限制的情况下,以最大化时间窗内系统吞吐量为目标,提出了一种基于应用时间窗比例公平算法。该算法首先设计各子载波上满足用户误比特率要求的星座距离,然后把系统中每个用户按照其比例映射为相应数目的虚拟用户,最后根据影子价格把子载波最优地分配给虚拟用户。仿真结果表明,该算法在保证用户公平性的基础上,有效地提高了系统吞吐量。     

基于MADDPG的无人机辅助通信功率分配算法

针对多无人机(unmanned aerial vehicle, UAV)作为空中基站辅助通信的吞吐量和公平性问题,提出了一种基于多智能体深度确定性策略梯度算法(multi-agent deep deterministic policy gradient algorithms, MADDPG)的功率分配算法,该算法通过联合优化UAV基站的功率分配和用户接入以提高系统吞吐量和公平性。本文首先构建了UAV基站为地面建立通信服务的三维场景,然后通过联合功率、用户关联和UAV位置约束,构建了吞吐量和公平性最大化的问题模型。考虑到该问题的复杂性,本文将所构建的优化问题建模为马尔科夫决策过程(Markov decision process, MDP),通过引入深度确定性策略梯度算法(deep deterministic policy gradient algorithm, DDPG)解决该问题。仿真结果表明,本文提出的基于MADDPG的UAV基站功率分配算法与其他算法相比,可以有效地提升系统的吞吐量和用户的公平性,提高通信的服务质量。     

Blind channel estimation for single‐input multiple‐output OFDM systems: zero padding based or cyclic prefix based?

Orthogonal frequency division multiplexing (OFDM) transmission equipped with multiple receive antennas constitutes a single‐input multiple‐output (SIMO) OFDM system. SIMO‐OFDM systems have been widely used in wireless communications. Compared to those approaches using training sequences, blind channel estimation methods for SIMO‐OFDM systems have the advantage of saving bandwidth and improving energy efficiency and system throughput. As far as blind channel identification is concerned, it is known that zero padding (ZP)‐based single‐input single‐output (SISO)‐OFDM systems have desirable features compared to conventional cyclic prefix (CP)‐based SISO‐OFDM systems. However, it is yet unknown whether ZP‐ or CP‐based SIMO‐OFDM systems are favourable for blind channel estimation. To investigate this problem, we first propose a short‐data effective method for blind channel estimation for ZP‐based SIMO‐OFDM systems. Then we analyse a number of issues surrounding blind channel estimation for ZP‐ and CP‐based SIMO‐OFDM systems. The issues brought up in the paper have not been discussed in the existing research. The significance of our investigation is that it provides a deep insight into blind channel estimation for ZP‐ and CP‐based SIMO‐OFDM systems. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive subcarrier and bit allocation based on ant colony optimization

The problem of resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) system is a combinatorial optimization problem, difficult to obtain optimal solutions in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, an algorithm based on ant colony optimization (ACO), which is a typical algorithm of metaheuristic methods, is proposed for the problem, utilizing excellent search performance of ACO to obtain good solutions. In addition, a parameter is applied to balance the efficiency and fairness of resource allocation. Performance analysis between algorithms based on ACO and genetic algorithm (GA) is carried out, indicating that the proposed algorithm based on ACO outperforms traditional linear algorithms as well as GA in the system throughput with assurance of fairness simultaneously, being as a promising technology for OFDM resource allocation.     

Optimal power allocation for multi-user OFDM and distributed antenna cognitive radio with RoF

In cognitive radio (CR), power allocation plays an important role in protecting primary user from disturbance of secondary user. Some existing studies about power allocation in CR utilize 'interference temperature' to achieve this protection, which might not be suitable for the OFDM-based CR. Thus in this paper, power allocation problem in multi-user orthogonal frequency division multiplexing (OFDM) and distributed antenna cognitive radio with radio over fiber (RoF) is firstly modeled as an optimization problem, where the limitation on secondary user is not 'interference temperature', but that total throughput of primary user in all the resource units (RUs) must be beyond the given threshold. Moreover, based on the theorem about maximizing the total throughput of secondary user, equal power allocation algorithm is introduced. Furthermore, as the optimization problem for power allocation is not convex, it is transformed to be a convex one with geometric programming, where the solution can be obtained using duality and Karush-Kuhn-Tucker (KKT) conditions to form the optimal power allocation algorithm. Finally, extensive simulation results illustrate the significant performance improvement of the optimal algorithm compared to the existing algorithm and equal power allocation algorithm.     

Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints

In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.     

Novel schemes for interference‐resilient OFDM wireless communication

This research work introduces some novel techniques for interference‐resilient OFDM wireless communication. Firstly, novel schemes for spatial multiplexing and interference cancelation based on signal subspace estimation are proposed. Secondly, the OFDM system is designed such that it meets three main objectives simultaneously, namely, (i) interference‐resiliency, (ii) throughput maximization, and (iii) energy consumption minimization. Interference‐cancelation techniques in prior art mostly consider maximization of throughput without any focus on energy consumption. On the other hand, the literature in energy minimization is limited to interference‐free environments. Most of the previous techniques also have a drawback that they cannot operate in smart or dynamically changing interference environments. The focus here is on interference‐resilient communication with equal attention to both throughput and energy‐efficiency maximization in dynamic hostile environments. The optimization problem is formulated, and then optimal policies and switching thresholds are found for throughput and energy‐efficient interference‐resilient communication. Methodologies for estimating the channel and jammer conditions and then adapting the transmission strategies accordingly are proposed. Moreover, to have an effective defense against smart jamming scenario, a constant‐payoff scheme is also introduced. Simulation results are compared with previous techniques that demonstrate the efficacy of proposed research. Copyright © 2015 John Wiley & Sons, Ltd.     

多用户OFDM系统中一种新颖的自适应功率分配方案

针对多用户正交频分复用(OFDM)系统自适应资源分配的问题,提出了一种新的自适应子载波分配方案。子载波分配中首先通过松弛用户速率比例约束条件确定每个用户的子载波数量,然后对总功率在所有子载波间均等分配的前提下,按照最小比例速率用户优先选择子载波的方式实现子载波的分配;在功率分配中提出了一种基于人工蜂群算法和模拟退火算法(ABC-SA)相结合的新功率分配方案,并且通过ABC-SA算法的全局搜索实现了在所有用户之间的功率寻优,同时利用等功率的分配方式在每个用户下进行子载波间的功率分配,最终实现系统容量的最大化。仿真结果表明,与其他方案相比,所提方案在兼顾用户公平性的同时还能有效地提高系统的吞吐量,进而证明了所提方案的有效性。     

QoS‐aware LTE‐A downlink scheduling algorithm: A case study on edge users

4G/LTE‐A (Long‐Term Evolution—Advanced) is the state of the art wireless mobile broadband technology. It allows users to take advantage of high Internet speeds. It makes use of the OFDM technology to offer high speed and provides the system resources both in time and frequency domain. A scheduling algorithm running on the base station holds the allocation of these resources. In this paper, we investigate the performance of existing downlink scheduling algorithms in two ways. First, we look at the performance of the algorithms in terms of throughput and fairness metrics. Second, we suggest a new QoS‐aware fairness criterion, which accepts that the system is fair if it can provide the users with the network traffic speeds that they demand and evaluate the performance of the algorithms according to this metric. We also propose a new QoS‐aware downlink scheduling algorithm (QuAS) according to these two metrics, which increases the QoS‐fairness and overall throughput of the edge users without causing a significant degradation in overall system throughput when compared with other schedulers in the literature.     

Efficient allocation of LTE downlink spectral resource to improve fairness and throughput

For the current generation of cellular communication systems, long‐term evolution (LTE) has been the major protocol to support high‐speed data transmission. It is critical to allocate downlink spectral resource in LTE, namely, resource blocks (RBs), but the issue is not well addressed in the standard. Therefore, the paper develops an efficient RB allocation algorithm with 4 mechanisms to improve both fairness and throughput in LTE. For fairness concern, our RB allocation algorithm uses a resource‐reservation mechanism to prevent cell‐edge user equipments from starvation, and a credit‐driven mechanism to keep track of the amount of resource given to each user equipment. For throughput concern, it adopts both weight‐assignment and RB‐matching mechanisms to allocate each RB to a packet according to its flow type and length. Through simulations, we demonstrate that the proposed RB allocation algorithm can significantly increase both throughput and fairness while reducing packet dropping and delays of real‐time flows, as compared with previous methods.     

DO‐Fast: a round‐robin opportunistic scheduling protocol for device‐to‐device communications

In this paper, we consider the distributed opportunistic scheduling problem for the Orthogonal Frequency Division Multiplexing OFDM‐based device‐to‐device (D2D) communications, where D2D links contend for access to the dedicated spectrum with limited assistance from cellular infrastructures. Particularly, a synchronous distributed opportunistic scheduling protocol under fairness constraints (DO‐Fast) is prompted. In DO‐Fast, a round‐robin strategy is integrated with the opportunistic scheduling to tackle the trade‐off between system throughput and access fairness. Moreover, without instantaneous channel state information at receivers, we incorporate a priority allocation scheme, where access priorities are assigned randomly in a local fashion. Consequently, DO‐Fast is robust against imperfect channel estimates and inaccurate channel state information ordering. In addition, the opportunistic strategy in DO‐Fast is distinguished from the existing ones in that efficient spatial reuse is exploited by allowing concurrent transmissions based on the signal‐to‐interference ratio scheduling criterion. Meanwhile, access opportunities are moderately granted for poor quality links by the round‐robin strategy for fairness considerations. We analyze and compare three practical scheduling strategies in terms of the access probability. We also evaluate access fairness through Jain's Index. It is shown via numerical and simulation results that DO‐Fast could achieve efficient spectrum utilization and guarantee the short‐term fairness. Copyright © 2014 John Wiley & Sons, Ltd.     

Fairness Guaranteed Cooperative Resource Allocation in Femtocell Networks

User-deployed low-power femtocell access points (FAPs) can provide better indoor coverage and higher data rates than conventional cellular networks. However, a major problem in this uncoordinated frequency reuse scenario is the inter-cell interference. In this paper, we propose a graph based distributed algorithm called fairness guaranteed cooperative resource allocation (FGCRA) to manage interference among femtocells. Since the optimal resource allocation is a NP-hard problem, which is difficult to get global optimization in femtocell networks, our proposed FGCRA algorithm provides sub-optimal resource allocation via cooperation among interfering neighbors. First, we propose a specific fairness factor obtained from two-hop interference relations, to determine the lower bound amount of subchannels that each FAP can use and guarantee the fairness among femtocells. Second, we propose scalable rules for distributed resource allocation and the solution to avoid the conflicts among interfering neighbors. Simulation results show that our proposed FGCRA significantly enhances both average user throughput and cell edge user throughput, and provides better fairness.     

On proportional fairness of uplink spectral efficiency in cell‐free massive MIMO systems

This paper is concerned with the proportional fairness (PF) of the spectral efficiency (SE) maximization of uplinks in a cell‐free (CF) massive multiple‐input multiple‐output (MIMO) system in which a large number of single‐antenna access points (APs) connected to a central processing unit (CPU) serve many single‐antenna users. To detect the user signals, the APs use matched filters based on the local channel state information while the CPU deploys receiver filters based on knowledge of channel statistics. We devise the maximization problem of the SE PF, which maximizes the sum of the logarithm of the achievable user rates, as a jointly nonconvex optimization problem of receiver filter coefficients and user power allocation subject to user power constraints. To handle the challenges associated with the nonconvexity of the formulated design problem, we develop an iterative algorithm by alternatively finding optimal filter coefficients at the CPU and transmit powers at the users. While the filter coefficient design is formulated as a generalized eigenvalue problem, the power allocation problem is addressed by a gradient projection (GP) approach. Simulation results show that the SE PF maximization not only offers approximately the achievable sum rates as compared to the sum‐rate maximization but also provides an improved trade‐off between the user rate fairness and the achievable sum rate.     

A distributed pricing algorithm for achieving network‐wide proportional fairness

Proportional fairness (PF) scheduling achieves a balanced tradeoff between throughput and fairness and has attracted great attention recently. However, most previous work on PF only considers the single cell scenario. This paper focuses on the problem of achieving network‐wide PF in a generalized multiple base station multiple user network. The problem is formulated as a maximization model and solved using the dual method. By decomposing the dual objective function, we get a distributed pricing based algorithm. Optimality of this algorithm is presented. Although the algorithm is derived using fixed link rate assumption, it can still apply in the presence of time‐varying rates. The proposed algorithm is suitable for distributed systems in the sense that it does not need any inter base station communication at all. Simulations illustrate that the proposed distributed network‐wide PF scheduling algorithm achieves almost the same performance as the centralized one. Compared with traditional local PF (LPF) scheduling, the network‐wide PF scheduling achieves higher throughput, lower throughput oscillation, and greater fairness. Copyright © 2010 John Wiley & Sons, Ltd.     

Resource allocation with proportional rate fairness in orthogonal frequency division multiple access relay networks

We address the problem of subchannel and transmission power allocation in orthogonal frequency division multiple access relay networks with an aim to maximize the sum rate and maintain proportional rate fairness among users. Because the formulated problem is a mixed‐integer nonlinear optimization problem with an extremely high computational complexity, we propose a low‐complexity suboptimal algorithm, which is a two‐step separated subchannel and power allocation algorithm. In the first step, subchannels are allocated to each user, whereas in the second step, the optimal power allocation is carried out on the basis of the given subchannel allocation and the nonlinear interval Gauss–Seidel method. Simulation results have demonstrated that the proposed algorithm can achieve a good trade‐off between the efficiency and the fairness compared with two other existing relevant algorithms. In particular, the proposed algorithm can always achieve 100% fairness under various conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Full‐Duplex Operations in Wireless Powered Communication Networks

In this paper, a wireless powered communication network (WPCN) consisting of a hybrid access point (H‐AP) and multiple user equipment (UE), all of which operate in full‐duplex (FD), is described. We first propose a transceiver structure that enables FD operation of each UE to simultaneously receive energy in the downlink (DL) and transmit information in the uplink (UL). We then provide an energy usage model in the proposed UE transceiver that accounts for the energy leakage from the transmit chain to the receive chain. It is shown that the throughput of an FD WPCN using the proposed FD UE (FD‐WPCN‐FD) can be maximized by optimal allocation of the UL transmission time to the UE by solving a convex optimization problem. Simulation results reveal that the use of the proposed FD UE efficiently improves the throughput of a WPCN with a practical self‐interference cancellation capability at the H‐AP. Compared to the WPCN with FD H‐AP and half‐duplex (HD) UE, FD‐WPCN‐FD achieved an 18% throughput gain. In addition, the throughput of FD‐WPCN‐FD was shown to be 25% greater than that of WPCN in which an H‐AP and UE operated in HD.     

Joint Scheduling and Resource Allocation with Fairness Based on the Signal-to-Leakage-plus-Noise Ratio in the Downlink of CoMP Systems

Recent research has shown that coordinated multi point (CoMP) transmission can provide significant gains in terms of the overall cell capacity and cell-edge user throughput [1]. The main purpose of this paper is to enhance the overall cell throughput, the cell-edge user’s throughput, and the fairness among user equipment terminals (UEs) in LTE-Advanced (LTE-A) systems using CoMP. Towards that end, we propose two novel resource allocation (RA) strategies based on the Signal-to-Leakage-plus-Noise-Ratio (SLNR) for the downlink of CoMP transmission in LTE-A systems. The proposed RA strategies select the UEs that can efficiently share the same resource block (RB) without degrading the overall throughput by using the SLNR metric. Moreover, a fairness algorithm is proposed to achieve certain level of fairness among the UEs and to improve the cell-edge UEs throughput. In addition, we compare the proposed strategies to the RA based on the more common Signal-to-Interference-plus-Noise-Ratio (SINR) strategy. The SLNR-based RA is shown to provide significant gains in throughput reaching up to 80 % in the overall system and is shown to have even less complexity than the typical SINR-based RA. Moreover, by evaluating the proposed strategies in terms of the average cell throughput, cell-edge user throughput, and fairness among UEs, simulations show that the proposed strategies present superior performance compared to the more common SINR strategy. With such advantages as enhanced throughput and lower complexity, the proposed schemes are suitable for application in practical cellular systems.