Joint optimal power control and beamforming in wireless networksusing antenna arrays

The interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. The minimum variance distortionless response beamformer maximizes the signal-to-interference-and-noise ratio (SINR) when it is employed in the receiver of a wireless link. In a system with omnidirectional antennas, power control algorithms are used to maximize the SINR as well. We consider a system with beamforming capabilities in the receiver, and power control. An iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that it converges to the jointly optimal beamforming and transmission power vector. The algorithm is distributed and uses only local interference measurements. In an uplink transmission scenario, it is shown how base assignment can be incorporated in addition to beamforming and power control, such that a globally optimum solution is obtained. The network capacity and the saving in mobile power are evaluated through numerical study     

System evaluation of optimal downlink beamforming with congestion control in wireless communication

We investigate the use of congestion control and joint optimal downlink beamforming, power control, and access point allocation, in a multi-cell wireless communication system. The access points of the system employ smart antennas and single antennas are used at the terminals. The possibility to send messages to multiple terminals at the same frequency in the same time slot is exploited. We show how previously proposed algorithms for optimal downlink beamforming easily can be extended to determine also the optimal access point for each mobile terminal. In order to assign resources, optimal beamforming requires a feasible set of mobiles, i.e. that all admitted users can be offered the required signal-to-interference-and-noise ratio. Therefore, an algorithm for deciding which mobile terminals to admit or reject from a congested system is proposed and evaluated. Using the proposed congestion algorithm, joint optimal downlink beamforming is evaluated and the throughput increase as compared to decentralized beamforming algorithms and other congestion control strategies is assessed from a system point of view. The results show that the proposed strategy can almost double the throughput compared to decentralized beamforming algorithms and give a fivefold increase in throughput compared to conventional beamforming without any interference suppression.     

Multi-cell Optimal Downlink Beamforming Algorithm with Per-base Station Power Constraints

We consider a problem of optimizing multi-cell downlink throughput in multiple-input single-output (MISO) beamforming with single user per sub-channel in the wireless communication system. Previous work based on the generalization of uplink-downlink duality has already reformulated the maximum achievable downlink throughput into dual uplink throughput maximization problem. Since the dual uplink problem is nonconvex, it is difficult to find its optimal solution. The main contribution of this paper is a novel practical algorithm based on heuristic to find the solution of beamforer design satisfying the necessary optimality conditions of the dual uplink problem. Meanwhile the converged beamforming vectors can in turn improve the system sum rate significantly. As the dual problem is a mixed optimization, we also provide algorithms for its two sub-optimal problems. Simulation results validate the convergence and the efficiency of proposed algorithms.     

Joint Channel and Power Allocation in Wireless Mesh Networks: A Game Theoretical Perspective

This paper addresses the throughput maximization problem in wireless mesh networks. For the case of cooperative access points, we present a negotiation-based throughput maximization algorithm which adjusts the operating channel and power level among access points automatically, from a gametheoretical perspective. We show that this algorithm converges to the optimal channel and power assignment which yields the maximum overall throughput with arbitrarily high probability. Moreover, we analyze the scenario where access points belong to different regulation entities and hence non-cooperative. The longterm behavior and corresponding performance are investigated and the analytical results are verified by simulations.     

Multi-cell uplink-downlink beamforming throughput duality based on Lagrangian duality with per-base station power constraints

Despite significant research efforts in beamforming, the maximum achievable downlink throughput with beamforming in a multi-cell environment is not available due to difficulty in finding optimal downlink beamforming. Thus, to reformulate the problem into a more solvable form, we derive dual uplink throughput optimization problem to multi-cell downlink beam- forming throughput maximization with per-base station (BS) power constraints based on Lagrangian duality. The optimal downlink beamforming is shown to be a minimum mean squared error (MMSE) beamforming in the dual uplink. It is also shown that the dual uplink problem achieves the same optimal throughput as the primal downlink problem.     

Software radio architecture with smart antennas: a tutorial onalgorithms and complexity

There has been considerable interest in using antenna arrays in wireless communication networks to increase the capacity and decrease the cochannel interference. Adaptive beamforming with smart antennas at the receiver increases the carrier-to-interference ratio (CIR) in a wireless link. This paper considers a wireless network with beamforming capabilities at the receiver which allows two or more transmitters to share the same channel to communicate with the base station. The concrete computational complexity and algorithm structure of a base station are considered in terms of a software radio system model, initially with an omnidirectional antenna. The software radio computational model is then expanded to characterize a network with smart antennas. The application of the software radio smart antenna is demonstrated through two examples. First, traffic improvement in a network with a smart antenna is considered, and the implementation of a hand-off algorithm in the software radio is presented. The blocking probabilities of the calls and total carried traffic in the system under different traffic policies are derived. The analytical and numerical results show that adaptive beamforming at the receiver reduces the probability of blocking and forced termination of the calls and increases the total carried traffic in the system. Then, a joint beamforming and power control algorithm is implemented in a software radio smart antenna in a CDMA network. This shows that, by using smart antennas, each user can transmit with much lower power, and therefore the system capacity increases significantly     

3D MIMO多小区多用户系统基于QBFO 算法的垂直波束下倾角优化

本文针对大尺度衰落下3D MIMO多用户系统中天线下倾角优化问题,首先在采用最大比传输预编码的基础上,假设有用信号功率、用户间干扰功率、小区间干扰功率都服从伽玛分布,将系统和速率公式简化为关于波束下倾角的函数。然后,以系统和速率最大化为目标函数,利用改进的量子菌群觅食优化算法（quantum bacterial foraging algorithm）,求解最优的天线下倾角。计算机仿真表明,本文提出的3D波束赋形方案在低信噪比和用户数较多时可以获得更高的系统和速率。      

Energy-Efficient Resource Allocation in Mobile Networks with Distributed Antenna Transmission

This paper address the energy conservation issues in wireless downlink of mobile networks with distributed antenna transmission. From the basic information theory for MIMO channels, we derived a simple energy efficiency defined as number of bits per Watt. we then identified three approaches to improve the optimal energy efficiency with a higher capacity, which include alleviating channel fading loss, mitigating the interference, and increasing the number of antennas. We considered the scenario of a single cell with distributed antennas to jointly investigate above three factors. We first proposed a beamforming based energy-efficient resource allocation algorithm, which can achieve the optimal energy efficiency with a higher capacity through adaptively allocating resources and managing interferences. Due to the computational complexity of this algorithm and real-time processing requirement, we further proposed a low-complexity antenna-selection based resource allocation algorithm, which is more tractable and only with slightly performance loss. Finally, we compared different network configurations with these algorithms by extensive simulations, the results demonstrate that the proposed algorithms in distributed antenna configurations achieve better energy efficiency at a high operational throughput point.     

Adaptive opportunistic fair scheduling over multiuser spatial channels

We consider the problem of opportunistic fair scheduling (OFS) of multiple users in downlink time-division multiple-access (TDMA) systems employing multiple transmit antennas and beamforming. OFS is an important technique in wireless networks to achieve fair bandwidth usage among users, which is performed on a per-frame basis at the media access control layer. Multiple-transmit-antenna beamforming provides TDMA systems with the capability of supporting multiple concurrent transmissions, i.e., multiple spatial channels at the physical layer. Given a particular subset of users and their channel conditions, the optimal beamforming scheme can be calculated. The multiuser opportunistic scheduling problem then refers to the selection of the optimal subset of users for transmission at each time instant to maximize the total throughput of the system subject to a certain fairness constraint on each individual user's throughput. We propose discrete stochastic approximation algorithms to adaptively select a better subset of users. We also consider scenarios of time-varying channels for which the scheduling algorithm can track the time-varying optimal user subset. We present simulation results to demonstrate the performance of the proposed scheduling algorithms in terms of both throughput and fairness, their fast convergence, and the excellent tracking capability in time-varying environments.     

Balancing transport and physical Layers in wireless multihop networks: jointly optimal congestion control and power control

In a wireless network with multihop transmissions and interference-limited link rates, can we balance power control in the physical layer and congestion control in the transport layer to enhance the overall network performance while maintaining the architectural modularity between the layers? We answer this question by presenting a distributed power control algorithm that couples with existing transmission control protocols (TCPs) to increase end-to-end throughput and energy efficiency of the network. Under the rigorous framework of nonlinearly constrained utility maximization, we prove the convergence of this coupled algorithm to the global optimum of joint power control and congestion control, for both synchronized and asynchronous implementations. The rate of convergence is geometric and a desirable modularity between the transport and physical layers is maintained. In particular, when congestion control uses TCP Vegas, a simple utilization in the physical layer of the queueing delay information suffices to achieve the joint optimum. Analytic results and simulations illustrate other desirable properties of the proposed algorithm, including robustness to channel outage and to path loss estimation errors, and flexibility in trading off performance optimality for implementation simplicity. This work presents a step toward a systematic understanding of "layering" as "optimization decomposition," where the overall communication network is modeled by a generalized network utility maximization problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as the optimization variables coordinating the subproblems. In the case of the transport and physical layers, link congestion prices turn out to be the optimal "layering prices.".     

Adaptive Counting Rule for Cooperative Spectrum Sensing Under Correlated Environments

We propose in this paper a dual-antenna-array (with transmitter antenna array and receiver antenna array) architecture, where the antenna elements are divided into several antenna element sets and each traffic channel is transmitted over an antenna element set, to realize the multiple traffic channels set up by a user. A SINR feedback based algorithm, which can regulate the transmission rate by iteratively adjusting the power on each traffic channel, is proposed to execute the rate control for the proposed dual-antenna-array architecture under cochannel interference. It is shown that the proposed algorithm can make the throughput meet the throughput requirement or achieve the weighted bandwidth sharing for certain fairness. In addition, we further propose a traffic channel configuration algorithm to help the SINR feedback based algorithm find the optimal traffic channel configuration that can meet the throughput requirement for each traffic channel or results in the maximal total throughput for each user.     

Reverse Link Performance of DS-CDMA Cellular Systems through Closed-Loop Power Control, Base Station Assignment, and Antenna Arrays in 2D Urban Environment

The interference reduction capability of antenna arrays, base station assignment, and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. In this paper, we propose smart step closed-loop power control (SSPC) algorithm and base station assignment method based on minimizing the transmitter power (BSA-MTP) technique for direct sequence-code division multiple access (DS-CDMA) receiver in a 2D urban environment. This receiver consists of conjugate gradient adaptive beamforming and matched filter in two stages using antenna arrays. In addition, we study an analytical approach for the evaluation of the impact of power control error (PCE) on the DS-CDMA cellular systems. The simulation results indicate that the SSPC algorithm and the BSA-MTP technique can significantly improve the network bit error rate in comparison with conventional methods. Our proposed methods can also significantly save total transmit power and extend battery life in mobile units. In addition, we show that the convergence speed of the SSPC algorithm is faster than that of conventional algorithms. Finally, we discuss two parameters of PCE and channel propagation conditions (path-loss parameter and variance of shadowing) and their effects on the capacity of the system via some computer simulations.     

Power allocation for OFDM using adaptive beamforming over wireless networks

The performance of a multiuser wireless network using orthogonal frequency-division modulation (OFDM), combined with power control and adaptive beamforming for uplink transmission is presented here. A network-wide adaptive power control algorithm is used to achieve the desired signal-to-interference-and-noise-ratio at each OFDM subcarrier and increase the power efficiency of the network. As a result, we can achieve a better overall error probability for a fixed total transmit power. With the assumption of fixed-modulation for all subcarriers, transmit powers and beamforming weight vectors at each subcarrier are updated jointly, using an iterative algorithm that converges to the optimal solution for the entire network. Unlike most of the loading algorithms, this approach considers fixed bit allocation and optimizes the power allocation and reduces the interference for the entire network, rather than a single transmitter. We also propose joint time-domain beamforming and power control to reduce the complexity resulting from the number of beamformers and fast Fourier transformed blocks. The proposed algorithm is also extended to COFDM and we show that it improves the performance of those systems.     

User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) – non-orthogonal multiple access (NOMA) downlink system for 5G wireless communications. This work consider the imperfect channel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as non-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beamforming design which establishes on majorization minimization (MM) technique to find the optimal transmit beamforming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.     

Physical layer techniques and maximum throughput scheduling with antenna arrays

We consider the downlink of a wireless system, where a base station transmits to users with an antenna array. We introduce scheduling algorithms that employ randomization to achieve maximum throughput, at low computational complexity. The algorithms operate in conjunction with either one of two physical layer techniques, namely transmit beamforming and Costa precoding. Simulation results show that the proposed randomized scheduling with Costa precoding algorithms carry the superior performance of Costa precoding at the physical, over to throughput benefits at higher layers.     

Network Beamforming Using Relays With Perfect Channel Information

This paper deals with beamforming in wireless relay networks with perfect channel information at the relays, receiver, and transmitter if there is a direct link between the transmitter and receiver. It is assumed that every node in the network has its own power constraint. A two-step amplify-and-forward protocol is used, in which the transmitter and relays not only use match filters to form a beam at the receiver but also adaptively adjust their transmit powers according to the channel strength information. For networks with no direct link, an algorithm is proposed to analytically find the exact solution with linear (in network size) complexity. It is shown that the transmitter should always use its maximal power while the optimal power of a relay can take any value between zero and its maxima. Also, this value depends on the quality of all other channels in addition to the relay's own. Despite this coupling fact, distributive strategies are proposed in which, with the aid of a low-rate receiver broadcast, a relay needs only its own channel information to implement the optimal power control. Then, beamforming in networks with a direct link is considered. When the direct link exists during the first step only, the optimal power control is the same as that of networks with no direct link. For networks with a direct link during the second step only and both steps, recursive numerical algorithms are proposed. Simulation shows that network beamforming achieves the maximal diversity order and outperforms other existing schemes.      

5G异构超密度网络中吞吐量与覆盖的联合优化

为了权衡5G异构超密度网络(HeterogeneousUltra-DenseNetworks,HUDN)中系统吞吐量最大化与覆盖优化间的冲突,提出基于功率控制的吞吐量和覆盖的联合优化(CJTC)算法。首先,推导系统吞吐量最大化的目标函数,再通过凸优等式转换,求解实现吞吐量最大化的基站发射功率;然后,推导切换失败率最小化的覆盖优化的目标函数,再利用扩展技术,并通过迭代联合求解最优的基站发射功率。仿真结果表明,提出的CJTC算法的吞吐量和覆盖方面的性能优于同类算法。     

Optimal beamforming for two-way multi-antenna relay channel with analogue network coding

This paper studies the wireless two-way relay channel (TWRC), where two source nodes, S1 and S2, exchange information through an assisting relay node, R. It is assumed that R receives the sum signal from S1 and S2 in one timeslot, and then amplifies and forwards the received signal to both S1 and S2 in the next time-slot. By applying the principle of analogue network coding (ANC), each of S1 and S2 cancels the so-called "self-interference" in the received signal from R and then decodes the desired message. Assuming that S1 and S2 are each equipped with a single antenna and R with multi-antennas, this paper analyzes the capacity region of the ANC-based TWRC with linear processing (beamforming) at R. The capacity region contains all the achievable bidirectional rate-pairs of S1 and S2 under the given transmit power constraints at S1, S2, and R. We present the optimal relay beamforming structure as well as an efficient algorithm to compute the optimal beamforming matrix based on convex optimization techniques. Low-complexity suboptimal relay beamforming schemes are also presented, and their achievable rates are compared against the capacity with the optimal scheme.     

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.     

A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks

The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks