Adaptive subcarrier bandwidth and power in OFDM‐based cognitive radio systems for high mobility applications

OFDM‐based cognitive radio systems are spectrally flexible and efficient, but they are vulnerable to intercarrier interference (ICI), especially in high mobility environments. High mobility of the terminal causes large Doppler frequency spread resulting in serious ICI. Such ICI severely degrades the system performance, which is ignored in the existing resource allocation of OFDM‐based cognitive radio systems. In this paper, an adaptive subcarrier bandwidth along with power allocation problem in OFDM‐based cognitive radio systems for high mobility applications is investigated. This adaptive subcarrier bandwidth method should choose the suitable subcarrier bandwidth not only to balance the tradeoff between ICI and intersymbol interference but also to be large enough to tolerate an amount of Doppler frequency spread but less than the coherence bandwidth. The power budget and interference to primary users caused by cognitive radio users are imposed for primary users' protection. With these constraints, a joint optimization algorithm of subcarrier bandwidth and power allocation is proposed to maximize the bandwidth efficiency of OFDM‐based cognitive radio systems in such conditions. Numerical simulation results show that the proposed algorithm could maximize the system bandwidth efficiency and balance this tradeoff while satisfying the constraints. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive Fair Subcarrier/Rate Allocation in Multirate OFDMA Networks: Radio Link Level Queuing Performance Analysis

This paper presents a semi-analytical methodology for radio link level performance analysis in a multirate "orthogonal frequency-division multiple-access" (OFDMA) network with adaptive fair rate allocation. Multirate transmission is assumed to be achieved through adaptive modulation, and fair rate allocation is based on the principle of generalized processor sharing to allocate the subcarriers adaptively among the users. The fair rate allocation problem is formulated as an optimization problem with the objective of maximizing system throughput while maintaining fairness (in terms of transmission rate) among the users. The "optimal" fair rate allocation is obtained by using the "Hungarian method." A heuristic-based approach, namely the "iterative approach," that is more implementation friendly is also presented. The throughput performance of the iterative fair rate allocation is observed to be as good as that of optimal fair rate allocation and is better than that of the static subcarrier allocation scheme. Also, the iterative fair allocation provides better fairness compared to that for each of the optimal and the static subcarrier allocation schemes. To this end, a queuing model is formulated to analyze radio link level performance measures such as packet dropping probability and packet transmission delay under the above rate allocation schemes. In this formulation, packet arrivals are modeled by the discrete Markov modulated Poisson process, which is flexible to model different types of traffic arrival patterns. The proposed framework for radio link level performance analysis of multirate OFDMA networks is validated by extensive simulations. Also, examples on the application of the proposed model for connection admission control and quality-of-service provisioning are illustrated     

Efficient scheduling algorithms for mixed services in wireless OFDMA system

Orthogonal Frequency Division Multiple Access (OFDMA) is an attracting system for combating the frequency selective fading. It’s widely considered as a promising candidate modulation and access scheme for next generation mobile communication systems. However, the explosive growth of multimedia applications raises higher performance requirements for Radio Resource Management (RRM) in OFDMA system. In particular, limited bandwidth and fierce resource competition impose a challenging issue on the design of packet scheduler. In this paper, we propose two scheduling algorithms applicable to mixed services (i.e., real-time and non-real-time services), that is QoS-oriented Dynamic Threshold Control (DTC) algorithm and fairness-oriented Fairness Aware and QoS Aware (FAQA) algorithm. These two algorithms are both composed of two stages, i.e., initial subcarrier allocation and subcarrier reallocation. For the DTC algorithm, in the stage of initial subcarrier allocation, based on the different metrics to weigh QoS between both services, we design a unique scheduling strategy for each type of service. A dynamic threshold is adopted to help users quickly recover from starvation, so that any one user in system can escape from severely degraded QoS. In the stage of subcarrier reallocation, we will reallocate the surplus subcarriers from the buffer-empty users to the buffer-nonempty users so as to increase spectrum efficiency. For FAQA algorithm, in the stage of initial subcarrier allocation, for the purpose of achieving Proportional Fairness (PF) with lower complexity, we deduce a simple scheduling strategy satisfying PF criterion by means of Greedy algorithm. In the stage of subcarrier reallocation, in order to increase the number of users satisfying QoS requirements, we’ll reallocate the surplus subcarriers from the QoS-satisfied users to the QoS-unsatisfied users. Simulation results demonstrate that, on one hand, the DTC algorithm not only guarantees the quality of both services, but also increases the spectrum efficiency; on the other hand, the FAQA algorithm well maintains the fairness among users, and increases the QoS satisfaction degree at the same time.     

Efficient user pairing algorithm for enhancement of spectral efficiency and interference cancelation in downlink NOMA system

A new subcarrier-user allocation algorithm for the downlink non-orthogonal multiple access system is presented in this paper. The proposed algorithm aims to enhance the spectral efficiency of the system and the successive interference cancelation performance by guaranteeing a high difference in channel-gain between the paired users per subcarrier. To enhance the spectral efficiency, the proposed algorithm provides a higher priority to the subcarrier that has a higher best (maximum) channel gain value rather than that has a lower best channel gain value. Also, it pairs the strong user with the second minimum channel-gain user rather than the minimum channel gain user. Besides, the proposed algorithm divides the subcarriers into two groups according to the standard deviation of the channel gain of each subcarrier. Then, it gives the priority to the group with low standard deviation values during subcarrier-user allocation to guarantee a high difference in channel-gain between the paired users per subcarrier. Later, fractional transmit power allocation is applied to distribute the subcarrier power between the paired users. Simulation results prove that the proposed algorithm improves the spectral efficiency of the system, and guarantees a significantly higher difference in channel-gain between the paired users per subcarrier compared to the conventional algorithms.

     

Fairness and Load Balancing in Wireless LANs Using Association Control

The traffic load of wireless LANs is often unevenly distributed among the access points (APs), which results in unfair bandwidth allocation among users. We argue that the load imbalance and consequent unfair bandwidth allocation can be greatly reduced by intelligent association control. In this paper, we present an efficient solution to determine the user-AP associations for max-min fair bandwidth allocation. We show the strong correlation between fairness and load balancing, which enables us to use load balancing techniques for obtaining optimal max-min fair bandwidth allocation. As this problem is NP-hard, we devise algorithms that achieve constant-factor approximation. In our algorithms, we first compute a fractional association solution, in which users can be associated with multiple APs simultaneously. This solution guarantees the fairest bandwidth allocation in terms of max-min fairness. Then, by utilizing a rounding method, we obtain the integral solution from the fractional solution. We also consider time fairness and present a polynomial-time algorithm for optimal integral solution. We further extend our schemes for the on-line case where users may join and leave dynamically. Our simulations demonstrate that the proposed algorithms achieve close to optimal load balancing (i.e., max-min fairness) and they outperform commonly used heuristics.     

D2D中继辅助通信的能效优化算法研究

针对蜂窝系统下D2D中继辅助通信的能量效率优化问题,在保证蜂窝用户与D2D用户服务质量的条件下,提出了一种能效优化算法。首先,以最大化系统中总D2D用户能效为目标,将优化问题建模为一个混合整数非线性规划问题。然后,将能效优化问题转化为功率控制、中继选择和信道分配3个子问题分别进行求解。最后,利用Dinkelbach方法和拉格朗日乘子方法解决功率控制问题;利用所提出的基于Q学习的中继选择算法完成中继的选择;基于功率控制和中继选择,利用匈牙利算法完成信道的分配。仿真结果表明,与现有算法相比,所提算法可有效提高系统D2D用户的总能效。     

基于EPON的公平带宽分享算法及其应用

考虑光学开放接入网（optical open access network）中的公平性,将最大最小公平算法用到双向服务等级协议（D-SLA）中,提出了一种基于以太无源光学网络（EPON）的易于实现的双向服务等级协议公平带宽分配算法。算法使用了两轮分配,通过分级管理确定首要SLA和次要SLA,对业务提供商和用户进行分级区...     

一种功率最小化的双层迭代子载波分配算法

研究OFDM下行链路通信系统中关于子载波和对应功率分配的功率消耗问题。在满足用户最小速率和子载波约束的条件下,形成了关于系统功率消耗最小化的混合优化问题。通过变量代换,将原始问题转化为只与子载波分配有关的整数优化问题。在此基础上,提出了一种功率最小化的双层迭代子载波分配算法,每次内部迭代求解两个用户的局部最优值,外部迭代判断收敛停止条件,最后通过数值仿真验证了所提算法的有效性。     

Dynamic subcarrier and power allocation based on cooperative game theory in symmetric cooperative OFDMA networks

In order to improve the efficiency and fairness of radio resource utilization,a scheme of dynamic cooperative subcarrier and power allocation based on Nash bargaining solution(NBS-DCSPA) is proposed in the uplink of a three-node symmetric cooperative orthogonal frequency division multiple access(OFDMA) system.In the proposed NBS-DCSPA scheme,resource allocation problem is formulated as a two-person subcarrier and power allocation bargaining game(SPABG) to maximize the system utility,under the constraints of each user’s maximal power and minimal rate,while considering the fairness between the two users.Firstly,the equivalent direct channel gain of the relay link is introduced to decide the transmission mode of each subcarrier.Then,all subcarriers can be dynamically allocated to the two users in terms of their selected transmission mode.After that,the adaptive power allocation scheme combined with dynamic subcarrier allocation is optimized according to NBS.Finally,computer simulation is conducted to show the efficiency and fairness performance of the proposed NBS-DCSPA scheme.     

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.     

Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS–PU couldn’t be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.     

An uplink resource allocation scheme for OFDMA‐based cognitive radio networks

Cognitive radio makes it possible for an unlicensed user to access a spectrum unoccupied by licensed users. In cognitive radio networks, extra constraints on interference temperature need to be introduced into radio resource allocation. In this paper, the uplink radio resource allocation is investigated for OFDMA‐based cognitive radio networks. In consideration of the characteristics of cognitive radio and OFDMA, an improved water‐filling power allocation scheme is proposed under the interference temperature constraints for optimal performance. Based on the improved water‐filling power allocation, a simple subcarrier allocation algorithm for uplink is proposed. The subcarrier allocation rules are obtained by theoretical deduction. In the uplink subcarrier allocation algorithm, the subcarriers are allocated to the users with the best channel quality initially and then adjusted to improve the system performance. A cursory water‐filling level estimation method is used to decrease the complexity of the algorithm. Asymptotic performance analysis gives a lower bound of the stability of the water‐filling level estimation. The complexity and performance of the proposed radio resource allocation scheme are investigated by theoretical analysis and numerical results. Copyright © 2008 John Wiley & Sons, Ltd.     

Transmit power adaptation for multiuser OFDM systems

In this paper, we develop a transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission. We generally formulate the data rate maximization problem by allowing that a subcarrier could be shared by multiple users. The transmit power adaptation scheme is derived by solving the maximization problem via two steps: subcarrier assignment for users and power allocation for subcarriers. We have found that the data rate of a multiuser OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain for that subcarrier and the transmit power is distributed over the subcarriers by the water-filling policy. In order to reduce the computational complexity in calculating water-filling level in the proposed transmit power adaptation method, we also propose a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers. Results show that the total data rate for the proposed transmit power adaptation methods significantly increases with the number of users owing to the multiuser diversity effects and is greater than that for the conventional frequency-division multiple access (FDMA)-like transmit power adaptation schemes. Furthermore, we have found that the total data rate of the multiuser OFDM system with the proposed transmit power adaptation methods becomes even higher than the capacity of the AWGN channel when the number of users is large enough.     

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。     

Discrete polynary coding immune clonal selection‐based joint subcarrier and power allocation in uplink cognitive OFDM network

Cognitive radio has been considered to be one of the main technologies to solve the problem of low spectrum utilization, while the adaptive allocation of network resource is one of the key technologies. A discrete polynary coding immune clonal selection (DPICS)‐based joint subcarrier and power allocation algorithm is proposed to solve the resource allocation problem in uplink cognitive OFDM networks. The novelties of DPICS include the following: A unique coding method is adopted to deal with multi‐value discrete variables. Compared with the traditional methods, the proposed method can acquire the shortest code. Meanwhile, the constraints of the subcarrier allocation are avoided. A heuristic mutation scheme is used to direct the mutation. Subcarriers are reallocated randomly to the secondary users with larger homotactic noise, which has a large probability to produce the optimal solution and improves the searching process. Subcarriers and power are allocated simultaneously, which is different with the traditional biphasic resource allocation algorithms. The biphasic resource allocation algorithms cannot acquire the subcarrier allocation result and power allocation result simultaneously, which makes the final result imprecise. The proposed algorithm avoids this situation and improves the accuracy of the final result. Compared with state‐of‐the‐art algorithms, the proposed algorithm is shown as effective by simulation results. Copyright © 2014 John Wiley & Sons, Ltd.     

A sub-optimal joint subcarrier and power allocation algorithm for multiuser OFDM

This paper investigates subcarrier and power allocation in multiuser OFDM. The aim is to maximize the overall rate while achieving proportional fairness amongst users under a total power constraint. Achieving the optimal solution is computationally demanding thereby necessitating the use of sub-optimal techniques. Existing sub-optimal techniques either use fixed power allocation and perform only subcarrier allocation or handle subcarrier and power allocation separately. In this paper, we propose an algorithm that performs joint subcarrier and power allocation. Simulation results are shown to compare the performance of the proposed algorithm with that of existing algorithms.     

协作系统中基于比例公平的资源分配方法

针对多用户协作中继系统中的资源分配问题,提出了一种在满足用户速率比例公平约束条件下的新算法。该算法先将由2个时隙组成的中继用户传输链路转换为一个等效信道链路,将涉及子载波分配、中继选择和功率分配的组合优化问题转化为分步的次优化问题。该算法在等功率分配情况下,根据各用户速率比例公平系数进行初步子载波数目分配;以瞬时信道增益最佳原则,进行剩余子载波数目分配及具体子载波分配,同时完成中继选择;在速率比例公平约束条件下推导出次优化功率分配的闭式表达式,从而完成各子载波上的功率分配。仿真结果表明,该算法在有效提高系统容量的同时,保证了各用户速率之间的比例公平性。     

A game theoretic framework for bandwidth allocation and pricing inbroadband networks

In this paper, we present a game theoretic framework for bandwidth allocation for elastic services in high-speed networks. The framework is based on the idea of the Nash bargaining solution from cooperative game theory, which not only provides the rate settings of users that are Pareto optimal from the point of view of the whole system, but are also consistent with the fairness axioms of game theory. We first consider the centralized problem and then show that this procedure can be decentralized so that greedy optimization by users yields the system optimal bandwidth allocations. We propose a distributed algorithm for implementing the optimal and fair bandwidth allocation and provide conditions for its convergence. The paper concludes with the pricing of elastic connections based on users' bandwidth requirements and users' budget. We show that the above bargaining framework can be used to characterize a rate allocation and a pricing policy which takes into account users' budget in a fair way and such that the total network revenue is maximized     

Utility‐based resource allocation in uplink of OFDMA‐based cognitive radio networks

Cognitive radio makes it possible for an unlicensed user to access a spectrum opportunistically on the basis of non‐interfering to licensed users. This paper addresses the problem of resource allocation for multiaccess channel (MAC) of OFDMA‐based cognitive radio networks. The objective is to maximize the system utility, which is used as an approach to balance the efficiency and fairness of wireless resource allocation. First, a theoretical framework is provided, where necessary and sufficient conditions for utility‐based optimal subcarrier assignment and power allocation are presented under certain constraints. Second, based on the theoretical framework, effective algorithms are devised for more practical conditions, including ellipsoid method for Lagrangian multipliers iteration and Frank–Wolfe method for marginal utilities iteration. Third, it is shown that the proposed scheme does not have to track the instantaneous channel state via an outage‐probability‐based solution. In the end, numerical results have confirmed that the utility‐based resource allocation can achieve the optimal system performance and guarantee fairness. Copyright © 2009 John Wiley & Sons, Ltd.