Joint estimation of the channel and I/Q imbalance for two-way relay networks

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

     

Chunk‐based and fairness‐based resource allocation in multicast distributed MISO‐OFDMA systems

The resource allocation problem for the downlink of orthogonal frequency‐division multiple access (OFDMA) wireless multicast systems is investigated. It is assumed that the base station consists of multiple antennas in a distributed antenna system (DAS), whereas each user is equipped with a single antenna. The multicasting technology is able to support several groups of users with flexible quality of service (QoS) requirements. The general mathematical formulation is provided, but achieving the optimal solution has a high computational cost. In our approach, the allocation unit is not the subcarrier, as in conventional OFDMA systems, but a set of contiguous subcarriers, which is called ‘chunk’. For practical implementation, a suboptimal but efficient algorithm is proposed in order to maximize the sum of the maximum attainable data rates of multicast groups of users, subject to total available power and proportional maximum attainable data rate constraints among multicast groups of users. Simulation and complexity analyses are provided to support the benefits of chunk‐based resource allocation to multicast OFDMA DASs, supporting that the proposed algorithm can be applied to latest‐generation wireless systems that provide QoS guarantees. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive Resource Allocation for MC‐CDMA and OFDMA in Reconfigurable Radio Systems

This paper studies the uplink resource allocation for multiple radio access (MRA) in reconfigurable radio systems, where multiple‐input and multiple‐output (MIMO) multicarrier‐code division multiple access (MC‐CDMA) and MIMO orthogonal frequency‐division multiple access (OFDMA) networks coexist. By assuming multi‐radio user equipment with network‐guided operation, the optimal resource allocation for MRA is analyzed as a cross‐layer optimization framework with and without fairness consideration to maximize the uplink sum‐rate capacity. Numerical results reveal that parallel MRA, which uses MC‐CDMA and OFDMA networks concurrently, outperforms the performance of each MC‐CDMA and OFDMA network by exploiting the multiuser selection diversity.     

Adaptive subcarrier and bit allocation in OFDMA systems supporting heterogeneous services

Orthogonal Frequency Division Multiple Access (OFDMA) is an efficient multiple access method for the future wireless systems. This paper studies the adaptive subcarrier and bit allocation problem in OFDMA systems to support heterogeneous services. The goal of the considered resource optimization technique is to maximize the total system throughput under the overall transmit power constraint while guaranteeing the QoS requirement of realtime users and supporting proportional fairness among non-realtime users. First, we introduce a Rate Adaptive (RA) resource allocation algorithm for non-realtime users and a Margin Adaptive (MA) algorithm for realtime users. Then, based on the previous algorithms, a novel algorithm is proposed to allocate the resource to both classes of users, which makes an efficient tradeoff between the resource usage of realtime users and non-realtime users. The algorithm is locally optimal solution provided that the MA and RA algorithms are utilized. Also, to reduce the computational complexity, a suboptimal method based on the balancing of the average power per subcarrier is also introduced. Monte Carlo simulation results show that all the proposed algorithms outperform the existing counterparts. The results also show that the suboptimal method for heterogeneous services can efficiently reduce the computational complexity at the cost of very little performance degradation. This work was supported by the National Natural Science Foundation of China (Nos. 60472079, 60572115), and by the Natural Science Foundation of Zhejiang Province (No. Z104252), China.     

Bat algorithm–based beamforming for mmWave massive MIMO systems

In this paper, an optimized analog beamforming scheme for millimeter‐wave (mmWave) massive MIMO system is presented. This scheme aims to achieve the near‐optimal performance.by searching for the optimized combination of analog precoder and combiner. In order to compensate for the occurrence of attenuation in the magnitude of mmWave signals, the codebook‐dependent analog beamforming in conjunction with precoding at transmitting end and combining signals at the receiving end is utilized. Nonetheless, the existing and traditional beamforming schemes involve a more difficult and complicated search for the optimal combination of analog precoder/combiner matrices from predefined codebooks. To solve this problem, we have referred to a modified bat algorithm to find the optimal combination value. This algorithm will explore the possible pairs of analog precoder/combiner as a way to come up with the best match in order to attain near‐optimal performance. The analysis shows that the optimized beamforming scheme presented in this paper can improve the performance that is very close to the beam steering benchmark that we have considered.     

On the use of NSGA-II for multi-objective resource allocation in MIMO-OFDMA systems

This paper investigates the problem of dynamic subcarrier and bit allocation in downlink of Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiple Access (OFDMA) Systems. Using Singular Value Decomposition, the MIMO fading channel of each subcarrier is transformed into an equivalent bank of parallel Single Input Single Output sub-channels. To achieve the capacity bound, one must solve a multiuser subcarrier allocation and the optimal bit allocation jointly. To alleviate the computational complexity of joint subcarrier and bit allocation, several suboptimal solutions have been proposed. These suboptimal solutions handle subcarrier and bits individually. We propose the use of Non-dominated Sorting Genetic Algorithm (NSGA)-II, which is a multi-objective Genetic Algorithm, for joint allocation of bits and subcarriers, in the downlink of MIMO-OFDMA system. NSGA-II is intended for optimization problems involving multiple conflicting objectives. Here the two conflicting objectives are Rate Maximization and Transmit Power Minimization. The simulation results indicate remarkable improvement in terms of convergence over previous approaches involving Evolutionary algorithms. At the same time capacity achieved by the proposed algorithm is found to be comparable with that of previous algorithms.     

A joint resource optimization and adaptive modulation framework for uplink single‐carrier frequency‐division multiple access systems

In this paper, we study joint resource allocation and adaptive modulation in single‐carrier frequency‐division multiple access systems, which is adopted as the multiple access scheme for the uplink in the 3GPP Long Term Evolution standard. We formulate an adaptive modulation and sum‐cost minimization (JAMSCmin) problem. Unlike orthogonal frequency‐division multiple access, in addition to the restriction of allocating a subchannel to one user at most, the multiple subchannels allocated to a user in single‐carrier frequency‐division multiple access systems should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for orthogonal frequency‐division multiple access, etc.) cannot help towards its optimal solution. We propose a novel optimization framework for the solution of this problem that is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary‐integer programming (BIP) problem and then transform this BIP problem into continuous space canonical dual problem that is the concave maximization problem. Based on the solution of the canonical dual problem, we derive joint resource allocation and adaptive modulation algorithm, which has polynomial time complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature. The results show a tremendous performance gain. Copyright © 2013 John Wiley & Sons, Ltd.     

OFDMA系统基于QoS保证和最大最小公平性准则下的动态资源分配

正交频分多址(OFDMA)技术以其更高的频谱效率和抗多径衰落特性成为高速无线通信网络的候选标准。兼顾效率和公平性是OFDMA系统资源分配亟待解决的问题。本文研究了OFDMA系统中的无线资源分配问题,既要保证QoS用户的最小速率要求,同时“尽力而为”用户之间必须满足最小速率最大化公平性(max-min fairness)准则;该资源分配问题可以表述为一个系统总功率约束下的子载波分配和功率控制的混合离散型优化模型,这是难解的NP-hard问题,穷举搜索的代价是极其巨大的。针对该非凸模型,本文设计一个拉格朗日松弛的优化算法,该算法中采用修正的椭球算法求解对偶问题。算法具有多项式时间复杂度,且与子载波数目呈线性增长关系。仿真结果表明,该算法能近似最优地满足用户QoS及最大最小公平性要求。      

Resource Allocation and Adaptive Modulation in Uplink SC-FDMA Systems

In this paper, we study joint power and sub-channel allocation, and adaptive modulation in Single Carrier Frequency Division Multiple Access (SC-FDMA) which is adopted as the multiple access scheme for the uplink in the 3GPP-LTE standard. A sum-utility maximization problem is considered. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel optimization framework for the solution of this problem which is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary-integer programming problem, and then transform this binary-integer programming problems into a continuous space canonical dual problem that is a concave maximization problem. Based on the solution of the continuous space dual problem, we derive joint power and sub-channel allocation algorithm whose computational complexity is polynomial. We provide conditions under which the proposed algorithms are optimal. We also propose an adaptive modulation scheme which selects an appropriate modulation strategy for each user. We compare the proposed algorithm with the existing algorithms in the literature to assess their performance. The results show a tremendous performance gain.     

Resource Allocation Algorithm for Multi‐cell Cognitive Radio Networks with Imperfect Spectrum Sensing and Proportional Fairness

This paper addresses the resource allocation (RA) problem in multi‐cell cognitive radio networks. Besides the interference power threshold to limit the interference on primary users PUs caused by cognitive users CUs, a proportional fairness constraint is used to guarantee fairness among multiple cognitive cells and the impact of imperfect spectrum sensing is taken into account. Additional constraints in typical real communication scenarios are also considered—such as a transmission power constraint of the cognitive base stations, unique subcarrier allocation to at most one CU, and others. The resulting RA problem belongs to the class of NP‐hard problems. A computationally efficient optimal algorithm cannot therefore be found. Consequently, we propose a suboptimal RA algorithm composed of two modules: a subcarrier allocation module implemented by the immune algorithm, and a power control module using an improved sub‐gradient method. To further enhance algorithm performance, these two modules are executed successively, and the sequence is repeated twice. We conduct extensive simulation experiments, which demonstrate that our proposed algorithm outperforms existing algorithms.     

Efficient Near‐Optimal Detection with Generalized Sphere Decoder for Blind MU‐MIMO Systems

In this letter, we propose an efficient near‐optimal detection scheme (that makes use of a generalized sphere decoder (GSD)) for blind multi‐user multiple‐input multiple‐output (MU‐MIMO) systems. In practical MU‐MIMO systems, a receiver suffers from interference because the precoding matrix, the result of the precoding technique used, is quantized with limited feedback and is thus imperfect. The proposed scheme can achieve near‐optimal performance with low complexity by using a GSD to detect several additional interference signals. In addition, the proposed scheme is suitable for use in blind systems.     

Nash bargaining solution based joint time‐frequency‐code‐power resource management algorithm for TD‐LTE systems

This paper presents a joint time‐frequency‐code‐power resource management algorithm based on the Nash bargaining solution in time‐division long term evolution systems. First, a joint radio resource allocation scheme at the time, frequency, code and power domain simultaneously is provided for the time‐division long term evolution system. Second, the proposed algorithm is modeled as a cooperative game under the constraints of each user's minimal rate requirement and available resources, for example, the maximal transmitting power. To reduce the computational complexity, the joint resource allocation algorithm is divided into time‐frequency‐code and power domain resource allocation. Also, we could approach the Pareto optimal rate as closely as possible by iterations. Simulation results show that compared with the other resource allocation algorithms, the proposed algorithm has achieved a good tradeoff between the overall system throughput and fairness among different users. Copyright © 2012 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.     

Efficient optimal and suboptimal radio resource allocation in OFDMA system

A fast optimal algorithm for solving radio resource allocation (RRA) problems in orthogonal frequency division multiple access (OFDMA) systems is proposed based on branch- and-bound (BnB) approach. The proposed algorithm offers the same performance as that achieved by some other existing optimal algorithms but with much reduced average computational complexity. As an effort in providing trade-off between performance and computational complexity, two suboptimal algorithms are also developed. Simulation results are shown to compare the performance and complexity of the proposed suboptimal algorithms with several existing algorithms.     

Prototype implementation of adaptive beamforming‐MIMO OFDMA system based on IEEE 802.16e WMAN standard and its experimental results

This paper details the downlink performance analysis of a MIMO (Multi‐Input Multi‐Output) system that combines adaptive beamforming (ABF) and spatial multiplexing (SM) procedures. The combination of MIMO signal processing with ABF is applied to WiBro (Wireless Broadband), the South Korean orthogonal frequency division multiple access (OFDMA) system that follows the IEEE 802.16e standard. Performance analysis is based on the results of experiments and simulations obtained from a prototype system implementation and fixed‐point simulator. Both the prototype experiments and simulations demonstrate that the ABF‐MIMO OFDMA system improves the required signal to noise ratio (SNR) over the conventional MIMO OFDMA system by 3 dB (QPSK)/2.5 dB (16‐QAM) for the frame error rate of 1% in the WiBro signal environments. From the implementation of the prototype system and its experimental results, we verify the feasibility of the ABF‐MIMO technology for realizing a practical WiBro base station. Copyright © 2011 John Wiley & Sons, Ltd.     

Joint beamforming and power control in downlink multiuser multiple‐input multiple‐output systems

In this paper, we study joint beamforming and power control for downlink multiple‐input multiple‐output systems with multiple users and target values for signal‐to‐interference plus noise ratios (SINRs). We formulate this as a constrained optimization problem of minimizing total interference subject to constraints on the beamforming vector norms, target SINRs, and total transmit power. Necessary and sufficient conditions satisfied by the optimal beamformer and power allocation are presented, and a new algorithm for joint beamforming and power control is proposed. This adapts the beamforming vectors and transmit powers incrementally, and it stops when the specified SINR targets are achieved with minimum powers. The proposed algorithm is illustrated with numerical results obtained from simulations, which study its convergence and compare it with other similar algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Resource Allocation for OFDMA Based Cognitive Radio Networks with Arbitrarily Distributed Finite Power Inputs

The existing works on resource allocation for OFDMA based cognitive radio networks are based on the assumption of Gaussian inputs whereas in practical systems the inputs are taken from a set of finite symbol alphabets. This paper considers a system with arbitrarily distributed finite power inputs and solve the resource allocation problem by employing the relationship between mutual information and minimum mean-square error. To protect the primary users’ links, constraint on interference power of the secondary users (SUs) is imposed. In OFDMA based CR networks, a tone can be assigned to one SU at most (exclusivity constraint), due to which the resource allocation problem becomes combinatorial and its solution becomes prohibitively difficult.In this paper, first, the exclusivity constraint on tones allocation is relaxed, the problem is convexified and an optimal solution is derived that provides an upper bound on the system performance. Then, an integer tone allocation and optimal power allocation (ITA–OPA) algorithm is developed that guarantees the assignment of each tone to a single SU with close-to-optimal performance. Finally, keeping in view the complexity of the optimal solution and ITA–OPA algorithm, a low-complexity suboptimal algorithm is devised that accounts for exclusive tone assignment. Simulation results show that the suboptimal algorithm also achieves near-optimal performance. The proposed algorithms outperforms the algorithms that assume Gaussian inputs.     

Joint subcarrier and power allocation for downlink cellular OFDMA systems by intercell interference limitation

This paper addresses resource allocation for sum throughput maximization in a sectorized two-cell downlink orthogonal frequency-division multiple-access (OFDMA) system impaired by multicell interference. It is well known that the optimization problem for this scenario is NP-hard and combinational, which is here converted to a novel sum throughput maximization problem in cellular OFDMA systems based on the intercell interference limitation. Then, three subclasses of this new problem are solved. By the first subclass, on the assumption that subcarrier allocation parameters are fixed, an algorithm for optimal power allocation is obtained. However, the optimal resource allocation requires an exhaustive search, including the optimal power allocation which cannot be implemented in practice due to its high complexity. By the second subclass, the problem is reduced to a single cell case where the intercell interference in each subcarrier is limited to a certain threshold. Based on the solution of the single cell problem, a distributed resource allocation scheme with the aim of small information exchange between the coordinated base stations is proposed. By the third subclass, the centralized resource allocation for two adjacent cells as a general problem is solved. Here, the algorithm allocates simultaneously the subcarriers and the power of the considered two cells while the resource allocation parameters of both cells are coupled mutually. The present study shows that distributed and centralized resource allocation algorithms have much less complexity than the algorithm used in the exhaustive search and can be used in practice as efficient multicell resource allocation algorithms. Simulation results illustrate the performance improvements of the proposed schemes in comparison to the schemes with no intercell interference consideration.     

Analysis of minimum transmit sum power and scheduling power gain for multi‐user MIMO‐OFDM networks with rate constraints

Minimum transmit sum power (MTSP) is of high theoretical and practical value in multi‐user rate‐constrained systems; it is, however, quite difficult to be numerically characterized in complex channels for the prohibitively high computational power required. In this paper, we present a computationally efficient method to approximate the MTSP in multi‐user multiple‐input multiple‐output orthogonal frequency division multiplexing (MU‐MIMO‐OFDM) wireless networks. Specifically, we propose both lower and upper bounds of the MTSP, which are asymptotically accurate in the limit of large K, the number of users. Then, we develop two iterative water‐filling algorithms to numerically solve the proposed bounds. These algorithms are with low complexity, that is, linear in K, and therefore enable the analysis of MTSP in complex channels even if K is large. Numerical results demonstrate the effectiveness of the bounds in approximating the MTSP and the high computational efficiency of the proposed iterative water‐filling algorithms. With the proposed bounds, we further numerically study scheduling power gain (SPG), which is defined as MTSP reduction achieved by scheduling resources over multiple channel blocks in time domain. We simulate the SPG in different wireless environments defined in Third Generation Partnership Project spatial channel extended model and find insignificant SPG in some cases, indicating that the benefit from scheduling over multiple channel blocks is limited and simply allocating resources within the present channel is sufficient. Our analysis on the MTSP and SPG provides guidelines on the design of resource schedulers in MU‐MIMO‐OFDM networks. Copyright © 2014 John Wiley & Sons, Ltd.