一种新的认知无线电功率分配算法（英文）

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks.Instead of using conventional primary user interference constraint,we give a new criterion called allowable signal to interference plus noise ratio(SINR) loss constraint in cognitive transmission to protect primary users.Considering power allocation problem for cognitive users over flat fading channels,in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user,we propose a new power allocation algorithm.The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.     

一种分布式天线系统的最大化能源效率传输优化算法

为实现绿色通信,考虑在每个远端射频头的发送功率受限、前端链路容量受限和保证用户目标速率的情况下,研究分布式天线系统的最大化能源效率传输优化算法。受分布式前端链路容量表达式及能源效率分式形式的约束,所考虑优化问题是非凸优化问题,难以获得其最优解。针对此优化问题,通过分式规划和凸近似方法将原始非凸问题转化为凸优化问题,提出了有效的二层迭代优化算法,并理论分析证明了所提算法的收敛性。仿真结果表明,所提算法优于传统算法,且随着远端射频头调度的用户数减少,能效压缩预编码策略的能源效率增大。      

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.     

Joint Beamforming and Power Allocation for Cognitive MIMO Systems Under Imperfect CSI Based on Game Theory

The problem of joint beamforming and power allocation for cognitive multi-input multi-output systems is studied via game theory. The objective is to maximize the sum utility of secondary users (SUs) subject to the primary user (PU) interference constraint, the transmission power constraint of SUs, and the signal-to-interference-plus-noise ratio (SINR) constraint of each SU. In our earlier work, the problem was formulated as a non-cooperative game under the assumption of perfect channel state information (CSI). Nash equilibrium (NE) is considered as the solution of this game. A distributed algorithm is proposed which can converge to the NE. Due to the limited cooperation between the secondary base station (SBS) and the PU, imperfect CSI between the SBS and the PU is further considered in this work. The problem is formulated as a robust game. As it is difficult to solve the optimization problem in this case, existence of the NE cannot be analyzed. Therefore, convergence property of the sum utility of SUs will be illustrated numerically. Simulation results show that under perfect CSI the proposed algorithm can converge to a locally optimal pair of transmission power vector and beamforming vector, while under imperfect CSI the sum utility of SUs converges with the increase of the transmission power constraint of SUs.     

一种改进的分散功率控制算法

在现有的分散定步长功率控制算法基础上,通过引入多个功率控制步长,得到一种改进的分散功率控制算法。通过分析可知,新算法具有更好的收敛精度,仿真结果表明与原有算法相比,本算法收敛速度获得了提高。     

Power control with partially known link gain matrix

In power control, the convergence rate is one of the most important criteria that can determine the practical applicability of a given algorithm. The convergence rate of power control is especially important when propagation and traffic conditions are changing rapidly. To track these changes, the power control algorithm must converge quickly. The purpose of this paper is to generalize the existing power control framework such that we can utilize partially known link gain information in improving the convergence speed. For the purpose, block power control (BPC) is suggested with its convergence properties. BPC is centralized within each block in the sense that it exchanges link gain information within the same block. However, it is distributed in a block-wise manner, and no information is exchanged between different blocks. Depending on availability of link gain information, a block can be any set of users, and can even consist of a single user. Computational experiments are carried out on a direct-sequence code-division multiple-access system, illustrating how BPC utilizes available link gain information in increasing the convergence speed of the power control.     

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.     

Differential Game Based Cooperative Power Control in Cognitive Radio Networks

This paper presents a cooperative power control algorithm in Cognitive radio (CR) system. The algorithm is based on the economic concepts of non-cooperative differential game, with interference constraint at the primary user. Based on the model, optimal power allocated to each secondary user for data transmission can be derived to maximize the secondary users’ utilization. The algorithm can solve the interference problem between secondary users and primary user and achieve high power efficiency. It is shown by way of simulation that by introducing game theory in the power control algorithm, performance improvements can be obtained in terms of game theory in CR system.     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Algorithms for Dynamic Spectrum Access With Learning for Cognitive Radio

We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.      

The Linearly Constrained LSCMA for Blind Multi-user Detection

The least square constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. The proposed algorithm ensures the algorithm convergence to the desired user. Thus the performance of the system is improved. The simulation results demonstrate that the proposed algorithm offers faster convergence rate and provides better output signal-to-interference-plus-noise-ratio (SINR) and bit error rate (BER) performance compared with the traditional LSCMA.     

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.     

基于SumTree采样结合Double DQN的非合作式多用户动态功率控制方法

为了保证认知无线网络中次用户本身的通信服务质量,同时降低次用户因发射功率不合理而造成的功率损耗,提出了一种基于SumTree采样结合深度双Q网络（Double Deep Q Network,Double DQN）的非合作式多用户动态功率控制方法。通过这种方法,次用户可以不断与辅助基站进行交互,在动态变化的环境下经过不断的学习,选择以较低的发射功率完成功率控制任务。其次,该方法可以解耦目标Q值动作的选择和目标Q值的计算,能够有效减少过度估计和算法的损失。并且,在抽取经验样本时考虑到不同样本之间重要性的差异,采用了结合优先级和随机抽样的SumTree采样方法,既能保证优先级转移也能保证最低优先级的非零概率采样。仿真结果表明,该方法收敛后的算法平均损失值能稳定在0.04以内,算法的收敛速度也至少快了10个训练回合,还能提高次用户总的吞吐量上限和次用户功率控制的成功率,并且将次用户的平均功耗降低了0.5 mW以上。     

Robust Probabilistic Distributed Power Control Algorithm for Underlay Cognitive Radio Networks under Channel Uncertainties

Due to limited cooperation among users and erratic nature of wireless channel, it is difficult for secondary users (SUs) to obtain exact values of system parameters, which may lead to severe interference to primary users (PUs) and cause communication interruption for SUs. In this paper, we study robust power control problem for spectrum underlay cognitive radio networks with multiple SUs and PUs under channel uncertainties. Precisely, our objective is to minimize total transmit power of SUs under the constraints that the satisfaction probabilities of both interference temperature of PUs and signal-to-interference-plus-noise ratio of SUs exceed some thresholds. With knowledge of statistical distribution of fading channel, probabilistic constraints are transformed into closed forms. Under a weighted interference temperature constraint, a globally distributed power control iterative algorithm with forgetting factor to increase convergence speed is obtained by dual decomposition methods. Numerical results show that our proposed algorithm outperforms worst case method and non-robust method.     

使用对偶分解的MU-CoMP-JT联合资源分配

在MU-Co MP-JT(Multi-User Coordinated Multiple-Points Joint Transmission)联合资源分配问题中,传统的迫零预编码矩阵会使得每根天线发送功率互不相同,当Co MP节点发射功率仅满足总功率约束时性能损失不明显,而当Co MP节点分布在不同的地理位置时将受到单节点功率约束,这势必会降低系统功率利用率。为了进一步提升系统吞吐量,基于对偶分解理论提出了一种联合预编码优化的资源分配算法。该算法以最大化用户权重速率为目标,将原优化问题分解成若干个优化的子问题,不同子问题对应不同接收天线数的联合优化问题。当子信道的发送天线数大于接收天线数时,通过多次迭代计算得到预编码矩阵,并且预编码矩阵会随着拉格朗日因子的变化而变化。仿真结果表明所提联合预编码优化的联合资源分配算法能够明显提升系统吞吐量,且提高天线功率利用效率。     

Multi-cell uplink power allocation game for user minimum performance guarantee in OFDMA systems

The multi-cell uplink power allocation problem for orthogonal frequency division multiplexing access (OFDMA) cellular networks is investigated with the uplink transmission power allocation on each co-frequency subchannel being defined as a multi-cell non-cooperative power allocation game (MNPG). The principle of the design oftbe utility function is given and a novel utility function is proposed for MNPG. By using this utility function, the minimum signal to interference plus noise ratio (SINR) requirement of a user can be guaranteed. It can be shown that MNPG will converge to the Nash equilibrium and that this Nash equilibrium is unique. In considering the simulation results, the effect of the algorithm parameters on the system performance is discussed, and the convergence of the MNPG is verified. The performance of MNPG is compared with that of traditional power allocation schemes, the simulation results showing that the proposed algorithm increases the cell-edge user throughput greatly with only a small decrease in cell total throughput; this gives a good tradeoff between the throughput of cell-edge users and the system spectrum efficiency.     

Rate of convergence for minimum power assignment algorithms in cellular radio systems

In wireless communication systems, mobile users adapt to a time varying radio channel by regulating transmitter powers. This power control is intended to provide each user an acceptable connection, as measured by a carrier to interference ratio (CIR), by eliminating unnecessary interference. It is important that a power control algorithm can converge quickly to a fixed point at which either all users have acceptable connections or an infeasibility can be detected. In this work, we show that an iterative power control and base station assignment algorithm based on CIR measurements converges to a unique fixed point at a geometric rate. This conclusion is shown to hold even if some or all of the users are subject to maximum power constraints. The rate of convergence is evaluated by simulation of a one dimensional CDMA system.     

Opportunistic packet scheduling algorithm for non-real-time service with a soft QoS requirement in a mobile broadband wireless access system

In this paper, we present a packet scheduling algorithm for a non-real-time service, with soft QoS requirements, which allows for degrading the QoS level, e.g., typically the packet delay, whenever necessary, in mobile broadband wireless Internet access systems. This algorithm is designed to properly trade off system throughput and delay performance, which can improve the system capacity by relaxing the delay constraint with respect to the underlying soft QoS requirement. This is as opposed to most of the existing packet scheduling algorithms for non-real-time service which are simply designed to maximize the system throughput without a delay constraint. The proposed adaptive exponential scheduling algorithm intentionally introduces additional delay to some users, especially under bad channel conditions, opportunistically allowing for serving users only under good channel conditions, as long as the resulting QoS degradation is acceptable for non-real-time service users. The results from a system-level simulation demonstrate that the system capacity can be significantly increased over existing algorithms, by as much as 65%, using the adaptive exponential scheduling algorithm while satisfying the given QoS-level requirements.     

Asymptotic RZF cooperative beamforming algorithm based on energy efficiency

A low complexity asymptotic regularized zero forcing cooperative beamforming algorithm based on energy efficiency in heterogeneous massive MIMO system was proposed,aiming at the problem that the current multi-flow regularization zero forcing beamforming algorithm sets the power constraint of each antenna in the regularization term as a fixed value and ignores the influences of factors such as the number of antennas,the number of users and QoS.The algorithm selects the optimal antenna power constraint set through the optimization method,and the optimal beamforming was asymptotically ob-tained to balance the interference among users to achieve the optimal energy efficiency,considering the impact of the number of antennas and users with the constraints of the antenna power and QoS.In view of the importance of backhaul in massive MIMO system,a backhaul power consumption model and the impact of backhaul power consumption on system performance was analyzed.Analysis and simulation results show that the proposed algorithm has great improvement of the performance,especially when the number of antennas is large.The algorithm is close to optimal performance,especially suitable for massive MIMO system of next generation communication.     

Rate of convergence for minimum power assignment algorithms in cellular radio systems

In wireless communication systems, mobile users adapt to a time varying radio channel by regulating transmitter powers. This power control is intended to provide each user an acceptable connection, as measured by a carrier to interference ratio (CIR), by eliminating unnecessary interference. It is important that a power control algorithm can converge quickly to a fixed point at which either all users have acceptable connections or an infeasibility can be detected. In this work, we show that an iterative power control and base station assignment algorithm based on CIR measurements converges to a unique fixed point at a geometric rate. This conclusion is shown to hold even if some or all of the users are subject to maximum power constraints. The rate of convergence is evaluated by simulation of a one dimensional CDMA system. This revised version was published online in July 2006 with corrections to the Cover Date.