Fairness based resource allocation for multiuser MISO-OFDMA systems with beamforming

Resource allocation problem in multiuser multiple input single output-orthogonal frequency division multiple access (MISO-OFDMA) systems with downlink beamforming for frequency selective fading channels is studied. The article aims at maximizing system throughput with the constraints of total power and bit error rate (BER) while supporting fairness among users. The downlink proportional fairness (PF) scheduling problem is reformulated as a maximization of the sum of logarithmic user data rate. From necessary conditions on optimality obtained analytically by Karush-Kuhn-Tucker (KKT) condition, an efficient user selection and resource allocation algorithm is proposed. The computer simulations reveal that the proposed algorithm achieves tradeoff between system throughput and fairness among users.     

Achievable sum‐rate analysis of correlated two‐antenna MIMO uplink channels

This paper analyzes the achievable sum‐rate of correlated two‐antenna multiple‐input multiple‐output (MIMO) uplink channels. Most of previous works have considered the case when a single user has multiple transmit antennas (i.e. multi‐antenna single‐user scenario). This paper considers the case when two‐antenna MIMO uplink channels comprise two users with a single transmit antenna (i.e. single‐antenna two‐user scenario). The analytic and simulation results show that the achievable sum‐rate of correlated single‐antenna two‐user MIMO uplink channels highly depends on the angle difference between the receive correlation coefficients of two users. It is also shown that the achievable sum‐rate of correlated single‐antenna two‐user MIMO uplink channels is larger than that of correlated two‐antenna single‐user MIMO uplink channels and can even be larger than that of independent and identically distributed Rayleigh two‐antenna MIMO uplink channels. Copyright © 2009 John Wiley & Sons, Ltd.     

Power allocation scheme based on sum capacity maximization for signal‐to‐leakage‐and‐noise ratio precoded multiuser multiple‐input single‐output downlink

This paper proposes a power allocation scheme to maximize the sum capacity of all users for signal‐to‐leakage‐and‐noise ratio (SLNR) precoded multiuser multiple‐input single‐output downlink. The designed scheme tries to explore the effect of the power allocation for the SLNR precoded multiuser multiple‐input single‐output system on sum capacity performance. This power allocation problem can be formulated as an optimization problem. With high signal‐to‐interference‐plus‐noise ratio assumption, it can be converted into a convex optimization problem through the geometric programming and hence can be solved efficiently. Because the assumption of high signal‐to‐interference‐plus‐noise ratio cannot be always satisfied in practice, we design a globally optimal solution algorithm based on a combination of branch and bound framework and convex relaxation techniques. Theoretically, the proposed scheme can provide optimal power allocation in sum capacity maximization. Then, we further propose a judgement‐decision algorithm to achieve a trade‐off between the optimality and computational complexity. The simulation results also show that, with the proposed scheme, the sum capacity of all the users can be improved compared with three existing power allocation schemes. Meanwhile, some meaningful conclusions about the effect of the further power allocation based on the SLNR precoding have been also acquired. The performance improvement of the maximum sum capacity power allocation scheme relates to the transmit antenna number and embodies different variation trends in allusion to the different equipped transmit antenna number as the signal‐to‐noise ratio (SNR) changes.Copyright © 2013 John Wiley & Sons, Ltd.     

User scheduling and power allocation for nonfull buffer traffic in NOMA downlink systems

Nonorthogonal multiple access (NOMA) is one of the key technologies for 5G, where the system capacity can be increased by allowing simultaneous transmission of multiple users at the same radio resource. The most of the proportional fairness (PF)–based resource allocation studies for NOMA systems assumes full buffer traffic model, while the traffic in real‐life scenarios is generally nonfull buffer. In this paper, we propose User Demand–Based Proportional Fairness (UDB‐PF) and Proportional User Satisfaction Fairness (PUSF) algorithms for user scheduling and power allocation in NOMA downlink systems when traffic demands of the users are limited and time‐varying. UDB‐PF extends the PF‐based scheduling by allocating optimum power levels towards satisfying the traffic demand constraints of user pair in each resource block. The objective of PUSF is to maximize the network‐wide user satisfaction by allocating sufficient frequency and power resources according to traffic demands of the users. In both cases, user groups are selected first to simultaneously transmit their signals at the same frequency resource, while the optimal transmission power level is assigned to each user to optimize the underlying objective function. In addition, the genetic algorithm (GA) approach is employed for user group selection to reduce the computational complexity. When the user traffic rate requirements change rapidly over time, UDB‐PF yields better sum rate (throughput) while PUSF provides better network‐wide user satisfaction results compared with the PF‐based user scheduling. We also observed that the GA‐based user group selection significantly reduced the computational load while achieving the comparable results of the exhaustive search.     

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.     

Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.     

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.     

Proportional-fair energy-efficient radio resource allocation for OFDMA smallcell networks

This paper investigates the energy-efficient radio resource allocation problem of the uplink smallcell networks. Different from the existing literatures which focus on improving the energy efficiency (EE) or providing fairness measured by data rates, this paper aims to provide fairness guarantee in terms of EE and achieve EE-based proportional fairness among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account each user’s quality of service, and the cross-tier interference limitation to ensure the macrocell transmission. Instead of dealing with the problem in forms of sum of logarithms directly, the problem is transformed into a form of sum of ratios firstly. Then, a two-step scheme which solves the subchannel and power allocation separately is adopted, and the corresponding subchannel allocation algorithm and power allocation algorithm are devised, respectively. The subchannel allocation algorithm is heuristic, but can achieve close-to-optimal performance with much lower complexity. The power allocation scheme is optimal, and is derived based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the capability of EE fairness provisioning. Specifically, it is suggested that the proposed algorithms can improve the fairness level among smallcell users by 150–400 % compared to the existing algorithms.     

A proportional fair scheduling for multicarrier transmission systems

This letter extends the proportional fair (PF) scheduling proposed in the high data rate (HDR) system to multicarrier transmission systems. It is known that the PF allocation (F. P. Kelly et al. (1998)) results in the maximization of the sum of logarithmic average user rates. We propose a PF scheduling that assigns users to each carrier while maximizing the sum of logarithmic average user rates.     

Opportunistic power allocation strategies and fair subcarrier allocation in OFDM-based cognitive radio networks

In this paper we have studied the subcarrier and optimal power allocation strategy for OFDM-based cognitive radio (CR) networks. Firstly, in order to protect the primary user communication from the interference of the cognitive user transmissions in fading wireless channels, we design an opportunistic power control scheme to maximize the cognitive user capacity without degrading primary user’s QoS. The mathematical optimization problem is formulated as maximizing the capacity of the secondary users under the interference constraint at the primary receiver and the Lagrange method is applied to obtain the optimal solution. Secondly, in order to limit the outage probability within primary user’s tolerable range we analyze the outage probability of the primary user with respect to the interference power of the secondary user for imperfect CSI. Finally, in order to get the better tradeoff between fairness and system capacity in cognitive radio networks, we proposed an optimal algorithm of jointing subcarrier and power allocation scheme among multiple secondary users in OFDM-based cognitive radio networks. Simulation results demonstrate that our scheme can improve the capacity performance and efficiently guarantee the fairness of secondary users.     

Resource allocation for sum-rate maximization in NOMA-based generalized spatial modulation

The Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) based on Spatial Modulation (SM-MIMO-NOMA) system has been proposed to achieve better spectral efficiency with reduced radio frequency chains comparing to the traditional MIMO-NOMA system. To improve the performance of SM-MIMO-NOMA systems, we extend them to generalized spatial modulation scenarios while maintaining moderate complexity and fairness. In this paper, system spectral efficiency and transmission quality improvements are proposed by investigating a sum-rate maximization resource allocation problem that is subject to the total transmitted power, user grouping, and resource block constraints. To solve this non-convex and difficult problem, a graph-based user grouping strategy is proposed initially to maximize the mutual gains of intragroup users. An auxiliary-variable approach is then adopted to transform the power allocation subproblem into a convex one. Simulation results demonstrate that the proposed algorithm has better performance in terms of bit error rate and sum rates.     

Cooperative bargaining solution for efficient and fair spectrum management in cognitive wireless networks

This paper studies the fairness among the primary users (PUs) and the secondary users (SUs) for resource allocation in cognitive radio systems. We propose a novel co‐opetition strategy based on the Kalai–Smorodinsky bargaining solution to balance the system efficiency and the fairness among users. The strategy formulates the spectrum sharing problem as a nonlinear and integral sum utility maximization subject to a set of constraints describing the co‐opetition among the PUs and the SUs. Then, we solve the maximization problem by proposing a heuristical method that consists of four steps: multi‐PU competition, PU's subcarrier contribution, multi‐SU competition, and SU's subcarrier contribution. Extensive simulation results are presented by comparing the co‐opetition strategy with several conventional ones, including the Kalai–Smorodinsky bargaining solution, sum rate maximization as well as the Max–Min. Results indicate that the co‐opetition strategy can jointly balance the system efficiency and fairness in multiuser resource allocation, as it is able to support more satisfied users and in the meanwhile improve the utility of those unsatisfied. Moreover, the co‐opetition can help enable the coexistence of the PUs and the SUs in cognitive radio systems. Copyright © 2013 John Wiley & Sons, Ltd.     

Power Allocation with Max–Min and Min–Max Fairness for Cognitive Radio Networks with Imperfect CSI

This paper consider the power allocation strategies in the cognitive radio (CR) system in the presence of channel estimation errors. As the user has different channel condition in CR systems, different amount of power resource is required to meets the QoS request. In order to guarantee the fairness of each CR user, ensure the interference from the primary user and other CR users meet the QoS requirement of the CR user and limit the interference that is caused by CR users on primary user within the range into the level that primary user can tolerate, we proposed some new power allocation schemes. The targets are to minimize the maximum power allocated to CR users, to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all CR users and to minimize the maximum outage probability over all CR users. The first power allocation scheme can be formulated using Geometric Programming (GP). Since GP problem is equivalent to the convex optimization problem, we can obtain the optimal solutions for the first scheme. The latter two power allocation schemes are not GP problems. We propose iterative algorithms to solve them. Simulation results show that proposed schemes can efficiently guarantee the fairness of CR users under the QoS constraint of the primary user and CR users.     

一种分布式的OFDMA系统资源分配算法

本文研究OFDMA系统的资源分配问题,把该问题建模为一个在基站的总发射功率一定的条件下,使系统中各个用户的权重速率之和最大化的数学模型。并提出一种基于对偶分解的分布式资源分配算法,将该问题分解为一个关于基站的主问题以及若干个关于用户的子问题。各个用户可以通过对子问题的求解获得各自的子载波以及功率的分配方案;而基站通过对主问题的求解使得满足子载波与功率的分配能够满足约束条件的要求,实现各用户权重速率和最大化的优化目标。所提算法能够把一个复杂的优化问题分解为若干个独立的子问题进行并行求解,因此可以有效地降低计算的复杂度以及基站的运算量。仿真结果表明,该算法能够在较少的迭代步数内得到一个近似最优解。      

基于合作博弈的多信道认知无线网络中的频谱共享算法简

采用合作博弈对多信道认知无线网络中的频谱共享问题进行了建模分析,提出了次用户在各信道上的信干噪比乘积作为合作博弈的效用函数。次用户在各信道上保证对主用户的干扰小于一定门限的要求下,通过最大化各自效用函数的乘积来进行功率分配。由于最大化次用户效用函数的乘积问题是非凸的,通过变量替换将其转化为了一个等价的凸优化问题,利用该凸优化问题的对偶分解,提出了一种次用户间的频谱共享算法。仿真结果表明,所提算法在次用户和速率与公平性之间进行了有效折中。     

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.     

多小区WiMAX资源分配研究

针对多小区WiMAX下行链路资源分配问题,本文通过建立符合实际应用场景的同道干扰模型,并考虑到功率和带宽对资源分配的影响,提出功率、带宽联合优化的多小区WiMAX资源分配策略。该策略基于系统和容量最大化、最小容量最大化以及发射总功率最低准则,把网络资源分配问题建模为不同的数学优化模型,并利用凸优化问题求解得到不同准则条件下带宽、功率分配的最优解。仿真结果表明,提出的资源分配方法可以保证资源分配的公平性、提高系统容量并降低功率消耗      

多用户多服务无线网络中感知用户体验的比例公平调度

The effective radio resource allocation algorithms, which satisfy diversiform requirements of mobile multimedia services in wireless cellular networks, have recently attracted more and more attention. This paper proposes a service-aware scheduling algorithm, in which the Mean Opinion Score (MOS) is chosen as the unified metric of the Quality of Experience (QoE). As the network needs to provide satisfactory services to all the users, the fairness of QoE should be considered. The Proportional Fair (PF) principle is adopted to achieve the trade-off between the network performance and user fairness. Then, an integer programming problem is formed and the QoE-aware PF scheduling principle is derived by solving the relaxed problem. Simulation results show that the proposed scheduling principle can perform better in terms of user fairness than the previous principle maximizing the sum of MOS. It also outperforms the max-min scheduling principle in terms of average MOS.     

An efficient resource-allocation scheme for spatial multiuser access in MIMO/OFDM systems

Fast adaptive transmission has been recently identified as a key technology for exploiting potential system diversity and improving power-spectral efficiency in wireless communication systems. An adaptive resource-allocation approach, which jointly adapts subcarrier allocation, power distribution, and bit distribution according to instantaneous channel conditions, is proposed for multiuser multiple-input multiple-output (MIMO)/orthogonal frequency-division multiplexing systems. The resultant scheme is able to: 1) optimize the power efficiency; 2) guarantee each user's quality of service requirements, including bit-error rate and data rate; 3) ensure fairness to all the active users; and 4) be applied to systems with various types of multiuser-detection schemes at the receiver. For practical implementation, a reduced-complexity allocation algorithm is developed. This algorithm decouples the complex multiuser joint resource-allocation problem into simple single-user optimization problems by controlling the subcarrier sharing according to the users' spatial separability. Numerical results show that significant power and diversity gains are achievable, compared with nonadaptive systems. It is also demonstrated that the MIMO system is able to multiplex several users without sacrificing antenna diversity by using the proposed algorithm.     

Secrecy sum rate optimization in MISO nonorthogonal multiple access systems with orthogonal space‐time block coding

In this paper, we investigate the secrecy sum rate optimization problem for a multiple‐input single‐output (MISO) nonorthogonal multiple access (NOMA) system with orthogonal space‐time block codes (OSTBC). This system consists of a transmitter, two users, and a potential eavesdropper. The transmitter sends information by orthogonal space‐time block codes. The transmitter's precoder and the power allocation scheme are designed to maximize achievable secrecy sum rate subject to the power constraint at the transmitter and the minimum transmission rate requirement of the weak user. We consider two cases of the eavesdropper's channel condition to obtain positive secrecy sum rate. The first case is the eavesdropper's equivalent channel is the weakest, and the other is the eavesdropper's equivalent channel between the strong user and weak user. For the former case, we employ the constrained concave convex procedure (CCCP)‐based iterative algorithm with one‐dimensional search. While for the latter, we adopt the method of alternating optimization (AO) between precoder and power allocation. We solve a semidefinite programming to optimize the precoder and drive a closed‐form expression of power allocation. The simulation results obtained by our method demonstrate the superiority of our proposed scheme.