认知无线电下行链路中的OFDMA资源分配算法

摘 要： 本文提出一种适用于下行认知无线电系统的正交频分多址接入资源分配算法,在总发射功率、误比特率和对授权用户的干扰受限的条件下最大化系统信息传输速率.本算法分两步实现：首先通过比较各认知用户在各子载波上单位信号发送条件下接收信号的信干噪比实现子载波分配;然后利用凸优化理论求解非负实数域内的比特数和功率值的最优解,并将其调整为符合实际系统需要的比特数和功率值,实现比特和功率分配.仿真结果表明,相比传统的基于频谱空洞的资源分配算法,本算法可以提供显著的系统信息传输速率增益.     

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.     

Weighted sum rate maximization for OFDM-based cognitive radio systems

In this paper, we focus on the subcarrier and power allocation problem in the downlink of an OFDM system under the cognitive radio environment. We aim to maximize the weighted sum rate of secondary users, without causing adverse interferences to primary users. We formulate the optimization problem subject to a total transmit power constraint and interference constraints, and give the optimality conditions, from which we derive a power limited multilevel water-filling algorithm. Simulation results show that our proposed algorithm yields significant improvement in terms of weighted sum rate, and provides good convergence with low computational burden.     

Proportional-fairness resource allocation for uplink OFDM-based cognitive radio systems

This paper has proposed a proportional-fairness resource allocation algorithm, including both subcarrier assignment algorithm and power allocation algorithm, for uplink orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems. First, to get a better performance in the proposed system model, the influence factor (a,b,c) was introduced to realize the assignment of the subcarriers. Second, the transmit power of the secondary users (SUs) was allocated to the corresponding subcarriers in order to maximize the uplink capacity of the SUs subject to both power and interference constraints. With the appropriate influence factor in the subcarrier assignment, the loss of transmitted data rate arising from the fairness was minimized. Simulation results showed that the proposed algorithm can achieve a perfect fairness among the SUs while maximizing the system capacity simultaneously, and is of a low computation complexity.     

Resource allocation in orthogonal frequency division multiple access‐based cognitive radio systems with minimum rate constraints

In this paper, resource allocation problem in orthogonal frequency division multiple access‐based cognitive radio (CR) systems to maintain minimum transmission rate constraints of CR users (CRUs) with the specified interference thresholds is investigated. Firstly, a single primary user (PU) CR system is considered, and a suboptimal resource allocation algorithm to maximize the sum transmission rate of all CRUs is proposed. Secondly, the single‐PU scenario is extended to multiple‐PU case, and an asymptotically optimal resource allocation algorithm is proposed using dual methods subject to constraints on both interference thresholds of PUs and total transmit power of all CRUs. Analysis and numerical results show that, in contrast to classical resource allocation algorithms, the proposed algorithm can achieve higher transmission rate and guarantee each CRU's minimum transmission rate in both scenarios. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

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.     

Joint overlay and underlay resource allocation with weighted fairness in OFDM‐based cognitive radio systems

In this paper, the problem of hybrid overlay and underlay spectrum access is investigated for OFDM‐based cognitive radio (CR) systems. Both the metrics for system (e.g. capacity) and users (e.g. fairness) are integrated into the unified framework of weighted‐capacity maximization with the interference constraint in CR systems. For easing the procedure of resource allocation, two criteria, respectively, for subcarrier assignment and power allocation are theoretically derived based on the Karush–Kuhn–Tucker conditions. Under the guidance of the two criteria, max‐capacity subcarrier assignment and optimal power allocation are proposed to flexibly distribute spectrum resources to secondary users. In order to reduce the computational complexity further, a suboptimal power allocation algorithm, referred to as cap‐limited waterfilling, is also presented by decomposing the interference constraint. Simulation results show that the capacity and fairness performance of the proposed algorithms is considerably better than the conventional ones in references. The proposed suboptimal algorithm with substantially lower complexity approaches to optimal power allocation in the vicinity of only 1% performance gap. Meanwhile, joint access model is greatly beneficial to spectrum efficiency enhancement in CR systems. Copyright © 2014 John Wiley & Sons, Ltd.     

A joint subcarrier selection and power allocation scheme using variational inequality in OFDM‐based cognitive relay networks

Introducing orthogonal frequency division multiplexing (OFDM) into cognitive radio (CR) can potentially increase the spectrum efficiency, but it also leads to further challenges for the resource allocation of CR networks. In OFDM‐based cognitive relay networks, two of the most significant research issues are subcarrier selection and power allocation. In this paper, a non‐cooperative game model is proposed to maximize the system throughput by jointly optimizing subcarrier selection and power allocation. First, taking the direct and relay links into consideration, an equivalent channel gain is presented to simplify the cooperative relay model into a non‐relay model. Then, a variational inequality method is utilized to prove the existence and uniqueness of the Nash equilibrium solution of the proposed non‐cooperative game. Moreover, to compute the solution of the game, a suboptimal algorithm based on the Lagrange function and distributed iterative water‐filling algorithm is proposed. The proposed algorithm can jointly optimize the process of subcarrier selection and power allocation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed joint subcarrier selection and power allocation scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

认知无线电下行链路中的频谱共享算法

该文提出了一种适用于下行认知无线电系统的频谱共享算法,在保证对授权用户的干扰低于限制和总发射功率受限的条件下最大化系统总容量。分析了对授权用户的干扰并给出设置干扰限制的方法,在此基础上分两步实现总容量的最大化：先通过最大信道信干噪比原则实现最优的子载波分配;然后利用提出的双注水方法计算最优的功率分配。仿真结果表明,相比传统的频谱共享算法,该算法可以获得显著的系统容量增益。     

Adaptive Subcarrier and Power Allocation with Fairness for Multi-user Space-Time Block-Coded OFDM System

This paper investigates the subcarrier and power allocation problems of multi-user space-time block coded OFDM based cellular systems. Based on the tradeoff between the number of assigned subcarriers and the amount of allocated power for users, a less complexity algorithm that separates subcarrier allocation and power allocation is proposed. Simulation results show that the proposed resource allocation algorithm can improve the capacity significantly compared with static FDMA fixed allocation algorithm and the MIMO-OFDMA scheme, and the more important thing is that it can make the capacity be distributed more fairly, very close to the ideal rate constraints, among users than the scheme which maximizes the system capacity only.     

时频双选信道OFDM系统的ICI消除与均衡

在正交频分复用（OFDM）系统中,因高速移动造成的多普勒效应导致子载波间正交性的破坏并产生载波间干扰（ICI）。为了消除ICI,本文通过分析ICI分布特性及带状矩阵特性,提出了低复杂度的迭代干扰抵消算法和基于最小均方误差准则的排序串行干扰抵消(MMSE-OSIC)算法。仿真结果表明,与传统子载波间干扰频域均衡算法相比,新算法在计算复杂度和性能之间取得了良好的平衡,且MMSE-OSIC算法可以利用时变信道的时间分集特性在高信噪比情况下有效地消除“地板效应”。      

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.     

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。     

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.     

Power allocation in OFDM cognitive radio relay networks with outdated channel state information

In this paper, we study power allocation in OFDM cognitive radio (CR) relay networks. The objective of power allocation is to maximize the instantaneous capacity of the CR network. It is assumed that the available channel state information between the secondary and primary users is an outdated but correlated version of the actual instantaneous channel state information. Optimal power allocation schemes are developed for both decode‐and‐forward and amplify‐and‐forward relay assisted CR transmission, assuming that the primary users are subject to average interference constraints and the CR transmitters are subject to maximum transmit power constraint. In addition, suboptimal power allocation schemes with reduced complexity are also proposed. Performance of the proposed schemes is compared with uniform power allocation and numerical results confirm that the proposed power allocation schemes achieve significant capacity improvement in comparison to uniform power loading. Furthermore, the proposed suboptimal power allocation schemes can be used as less complex alternatives for optimal power allocation with some capacity degradation. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

QoS-Driven Jointly Optimal Subcarrier Pairing and Power Allocation for Decode-and-Forward OFDM Relay Systems

In this paper, we investigate the quality-of-service (QoS) driven subcarrier pairing and power allocation for two-hop decode-and-forward (DF) OFDM relay systems. By integrating the concept of effective capacity, our goal is to maximize the system throughput subject to a given delay-QoS constraint. Based on whether the destination can receive the signal transmitted by the source, we consider two scenarios, i.e. OFDM DF relay systems without diversity and OFDM DF relay systems with diversity, respectively. For OFDM DF relay systems without diversity, we demonstrate that the jointly optimal subcarrier pairing and power allocation can be implemented with two separate steps. For OFDM DF relay systems with diversity, we propose an iterative algorithm to achieve jointly optimal subcarrier pairing and power allocation. Furthermore, we find that the analytical results show different conclusions for the two types of OFDM relay systems. For OFDM relay systems without diversity, the optimal power allocation depend on not only the channel quality of subcarriers but also the delay QoS constraints, while the optimal subcarrier pairing just depends on the channel quality of subcarriers. For OFDM relay systems with diversity, both the optimal subcarrier pairing and power allocation depend on the channel quality of subcarriers and the delay QoS constraints. Simulation results show that our proposed scheme offers a superior performance over the existing schemes.     

Fractionally spaced equalizer based on dynamically varying modulus algorithm for spectrally efficient channel compensation in SC-FDMA based systems

Orthogonal frequency division multiplexing (OFDM) based wireless communication systems are expected to satisfy the thirst for ever increasing demand on higher spectral efficiency. But, OFDM systems suffer from peak to average power ratio (PAPR) and inter carrier interference (ICI) problems. It is observed that when OFDM is used in the uplink, PAPR problem is more severe and the relative mobility of the user equipments with respect to the base station will cause Doppler spread which leads to ICI. One of the solutions to minimize PAPR and ICI is single carrier frequency division multiple access. But there is a tradeoff in spectral efficiency. The main objective of this paper is to evaluate the performance of fractionally spaced equalizer (FSE) for blind channel estimation based on higher order statistics and to identify any better alternative to improve its performance. Dynamically varying modulus algorithm (DVMA) based FSE is proposed in this paper which is a better alternative for supervised equalization. The simulation results prove that FSE blind equalizer based on DVMA outperform the conventional supervised and blind equalizers.     

Multiuser OFDM with adaptive subcarrier, bit, and power allocation

Multiuser orthogonal frequency division multiplexing (OFDM) with adaptive multiuser subcarrier allocation and adaptive modulation is considered. Assuming knowledge of the instantaneous channel gains for all users, we propose a multiuser OFDM subcarrier, bit, and power allocation algorithm to minimize the total transmit power. This is done by assigning each user a set of subcarriers and by determining the number of bits and the transmit power level for each subcarrier. We obtain the performance of our proposed algorithm in a multiuser frequency selective fading environment for various time delay spread values and various numbers of users. The results show that our proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-division multiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes. We have also quantified the improvement in terms of the overall required transmit power, the bit-error rate (BER), or the area of coverage for a given outage probability