Now showing 1 - 10 of 30
  • Publication
    Multi-group multicast beamformer design for MIMO-OFDM transmission
    We study the problem of designing multicast precoders for multiple groups with the objective of minimizing total transmit power under certain guaranteed quality-of-service (QoS) requirements. To avail both spatial and frequency diversity, we consider a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. The problem of interest is in fact a nonconvex quadratically constrained quadratic program (QCQP) for which the prevailing semidefinite relaxation (SDR) technique is inefficient for at least two reasons. At first, the relaxed problem cannot be equivalently reformulated as a semidefinite programming (SDP). Secondly, even if the relaxed problem is solved, the so-called randomization procedure should be used to generate a high quality feasible solution to the original QCQP. However, such a randomization procedure is difficult in the considered system model. To overcome these shortcomings, we adopt successive convex approximation (SCA) framework in this paper to find beamformers directly. The proposed method not only avoids the randomization procedure mentioned above but also requires lower computational complexity compared to the SDR approach. Numerical experiments are carried out to demonstrate the effectiveness of the proposed algorithm.
      51
  • Publication
    Optimal Joint Remote Radio Head Selection and Beamforming Design for Limited Fronthaul C-RAN
    This paper considers the downlink transmission of cloud-radio access networks (C-RANs) with limited fronthaul capacity. We formulate a joint design of remote radio head (RRH) selection, RRH-user association, and transmit beamforming for simultaneously optimizing the achievable sum rate and total power consumption, using the multiobjective optimization concept. Due to the nonconvexity of perfronthaul capacity constraints and introduced binary selection variables, the formulated problem lends itself to a mixed-integer nonconvex program, which is generally non-deterministic polynomial-time hard. Motivated by powerful computing capability of C-RAN and for benchmarking purposes, we propose a branch and reduce and bound-based algorithm to attain a globally optimal solution. For more practically appealing approaches, we then propose three iterative low-complexity algorithms. In the first method, we iteratively approximate the continuous nonconvex constraints by convex conic ones using successive convex approximation framework. More explicitly, the problem obtained at each iteration is a mixed-integer second-order cone program (MI-SOCP) for which dedicated solvers are available. In the second method, we first relax the binary variables to be continuous to arrive at a sequence of SOCPs and then perform a postprocessing procedure on the relaxed variables to search for a high-performance solution. In the third method, we solve the considered problem in view of sparsity-inducing regularization. Numerical results show that our proposed algorithms converge rapidly and achieve near-optimal performance as well as outperform the known algorithms.
      495Scopus© Citations 50
  • Publication
    Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink
    We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system where large number of access points (APs) simultaneously serve a group of users. Two fundamental problems are of interest, namely (i) to maximize the total spectral efficiency (SE), and (ii) to maximize the minimum SE of all users. As the considered problems are non-convex, existing solutions rely on successive convex approximation to find a sub-optimal solution. The known methods use off-the-shelf convex solvers, which basically implement an interior-point algorithm, to solve the derived convex problems. The main issue of such methods is that their complexity does not scale favorably with the problem size, limiting previous studies to cell-free massive MIMO of moderate scales. Thus the potential of cell-free massive MIMO has not been fully understood. To address this issue, we propose an accelerated projected gradient method to solve the considered problems. Particularly, the proposed solution is found in closed-form expressions and only requires the first order information of the objective, rather than the Hessian matrix as in known solutions, and thus is much more memory efficient. Numerical results demonstrate that our proposed solution achieves far less run-time, compared to other second-order methods.
      300Scopus© Citations 6
  • Publication
    Energy-Efficient Bit Allocation for Resolution-Adaptive ADC in Multiuser Large-Scale MIMO Systems: Global Optimality
    We consider uplink multiuser wireless communications systems, where the base station (BS) receiver is equipped with a large-scale antenna array and resolution adaptive analog-to-digital converters (ADCs). The aim is to maximize the energy efficiency (EE) at the BS subject to constraints on the users' quality-of-service. The approach is to jointly optimize both the number of quantization bits at the ADCs and the on/off modes of the radio frequency (RF) processing chains. The considered problem is a discrete nonlinear program, the optimal solution of which is difficult to find. We develop an efficient algorithm based on the discrete branch-reduce-and-bound (DBRnB) framework. It finds the globally optimal solutions to the problem. In particular, we make some modifications, which significantly improve the convergence performance. The numerical results demonstrate that optimizing jointly the number of quantization bits and on/off mode can achieve remarkable EE gains compared to only optimizing the number of quantization bits.
      299Scopus© Citations 3
  • Publication
    Multigroup Multicast Beamformer Design for MISO-OFDM With Antenna Selection
    We study the problem of designing transmit beamformers for a multigroup multicasting by considering a multiple-input single-output orthogonal frequency-division multiplexing framework. The design objective involves either minimizing the total transmit power for certain guaranteed quality of service or maximizing the minimum achievable rate among the users for a given transmit power budget. The problem of interest can be formulated as a nonconvex quadratically constrained quadratic programming (QCQP) for which the prevailing semidefinite relaxation (SDR) technique is inefficient for at least two reasons. At first, the relaxed problem cannot be reformulated as a semidefinite programming. Second, even if the relaxed problem is solved, the so-called randomization procedure should be used to generate a feasible solution to the original QCQP, which is difficult to derive for the considered problem. To overcome these shortcomings, we adopt successive convex approximation framework to find multicast beamformers directly. The proposed method not only avoids the need of randomization search, but also incurs less computational complexity compared to an SDR approach. In addition, we also extend multicasting beamformer design problem with an additional constraint on the number of active elements, which is particularly relevant when the number of antennas is larger than that of radio frequency chains. Numerical results are used to demonstrate the superior performance of our proposed methods over the existing solutions.
      395Scopus© Citations 23
  • Publication
    Traffic Aware Resource Allocation Schemes for Multi-Cell MIMO-OFDM Systems
    We consider a downlink multi-cell multiple-input multiple-output (MIMO) interference broadcast channel (IBC) using orthogonal frequency division multiplexing (OFDM) with multiple users contending for space-frequency resources in a given scheduling instant. The problem is to design precoders efficiently to minimize the number of backlogged packets queuing in the coordinating base stations (BSs). Conventionally, the queue weighted sum rate maximization (Q-WSRM) formulation with the number of backlogged packets as the corresponding weights is used to design the precoders. In contrast, we propose joint space-frequency resource allocation (JSFRA) formulation, in which the precoders are designed jointly across the space-frequency resources for all users by minimizing the total number of backlogged packets in each transmission instant, thereby performing user scheduling implicitly. Since the problem is nonconvex, we use the combination of successive convex approximation (SCA) and alternating optimization (AO) to handle nonconvex constraints in the JSFRA formulation. In the first method, we approximate the signal-to-interference-plus-noise ratio (SINR) by convex relaxations, while in the second approach, the equivalence between the SINR and the mean squared error (MSE) is exploited. We then discuss the distributed approaches for the centralized algorithms using primal decomposition and alternating directions method of multipliers. Finally, we propose a more practical iterative precoder design by solving the Karush-Kuhn-Tucker expressions for the MSE reformulation that requires minimal information exchange for each update. Numerical results are used to compare the proposed algorithms to the existing solutions.
      247Scopus© Citations 41
  • Publication
    Queue Aware Resource Optimization in Latency Constrained Dynamic Networks
    Low latency communications is one of the key design targets in future wireless networks. We propose a queue aware algorithm to optimize resources guaranteeing low latency in multiple-input single-output (MISO) networks. Proposed system model is based on dynamic network architecture (DNA), where some terminals can be configured as temporary access points (APs) on demand when connected to the Internet. Therein, we jointly optimize the user-AP association and queue weighted sum rate of the network, subject to limitations of total transmit power of the APs and minimum delay requirements of the users. The user-AP association is viewed as finding a sparsity constrained solution to the problem of minimizing ℓ q -norm of the difference between queue and service rate of users. Finally, the efficacy of the proposed algorithm in terms of network latency and its fast convergence are demonstrated using numerical experiments. Simulation results show that the proposed algorithm yields up to two-fold latency reductions compared to the state-of-the-art techniques.
      277
  • Publication
    Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and ℓ0-Approximation Methods
    We study downlink of multiantenna cloud radio access networks with finite-capacity fronthaul links. The aim is to propose joint designs of beamforming and remote radio head (RRH)-user association, subject to constraints on users' quality-of-service, limited capacity of fronthaul links and transmit power, to maximize the system energy efficiency. To cope with the limited-capacity fronthaul we consider the problem of RRH-user association to select a subset of users that can be served by each RRH. Moreover, different to the conventional power consumption models, we take into account the dependence of the baseband signal processing power on the data rate, as well as the dynamics of the efficiency of power amplifiers. The considered problem leads to a mixed binary integer program which is difficult to solve. Our first contribution is to derive a globally optimal solution for the considered problem by customizing a discrete branch-reduce-and-bound approach. Since the global optimization method requires a high computational effort, we further propose two suboptimal solutions able to achieve the near optimal performance but with much reduced complexity. To this end, we transform the design problem into continuous (but inherently nonconvex) programs by two approaches: penalty and l 0 -approximation methods. These resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results are provided to evaluate the effectiveness of the proposed approaches.
      314Scopus© Citations 18
  • Publication
    Decentralized coordinated beamforming for weighted sum energy efficiency maximization in multi-cell MISO downlink
    We study energy-efficient decentralized coordinated beam-forming in multi-cell multiuser multiple-input single-output system. The problem of interest is to maximize the weighted sum energy efficiency subject to user-specific quality of service constraints. The original problem is iteratively approximated as a convex program according to successive convex approximation (SCA) principle. The convex problem at each iteration is then formulated as a general global consensus problem, which is solved via alternating direction method of multipliers (ADMM). This enables base stations to independently and in parallel optimize their beamformers relying only on local channel state information and limited backhaul information exchange. In addition to waiting for the ADMM to converge as conventionally when solving the approximate convex program, we propose a method where only one ADMM iteration is performed after each SCA update step. Numerical results illustrate the fast convergence of the proposed methods and show that performing only one ADMM iteration per each convex problem can significantly improve the convergence speed.
      159Scopus© Citations 17
  • Publication
    Energy Efficiency Fairness for Multi-Pair Wireless-Powered Relaying Systems
    We consider a multi-pair amplify-and-forward relay network where the energy-constrained relays adopting the time-switching protocol harvest energy from the radio-frequency signals transmitted by the users for assisting user data transmission. Both one-way and two-way relaying techniques are investigated. Aiming at energy efficiency (EE) fairness among the user pairs, we construct an energy consumption model incorporating rate-dependent signal processing power, the dependence on output power level of power amplifiers’ efficiency, and nonlinear energy harvesting (EH) circuits. Then, we formulate the max-min EE fairness problems in which the data rates, users’ transmit power, relays’ processing coefficient, and EH time are jointly optimized under the constraints on the quality of service and users’ maximum transmit power. To achieve efficient suboptimal solutions to these nonconvex problems, we devise monotonic descent algorithms based on the inner approximation (IA) framework, which solve a second-order-cone program in each iteration. To further simplify the designs, we propose an approach combining IA and zero-forcing beamforming, which eliminates inter-pair interference and reduces the numbers of variables and required iterations. Finally, extensive numerical results are presented to validate the proposed approaches. More specifically, the results demonstrate that ignoring the realistic aspects of power consumption might degrade the performance remarkably, and jointly designing parameters involved could significantly enhance the EE.
      460Scopus© Citations 27