Now showing 1 - 10 of 18
  • 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.
  • Publication
    Energy-efficient coordinated beamforming with rate dependent processing power
    This paper studies energy-efficient coordinated beamforming in multi-cell multi-user multiple-input single-output (MISO) system. On contrary to the existing approaches where the power consumption of a base station is modeled as a convex or linear function, we consider a more practical model where part of the processing power depends on the rate provided by the base stations. Two optimization criteria are considered, namely network energy efficiency maximization and weighted sum energy efficiency maximization. We develop successive convex approximation based algorithms to tackle these difficult nonconvex problems. The numerical results illustrate that the rate dependent power consumption has a large impact on the energy efficiency, and, thus, has to be taken into account when devising energy-efficient transmission strategies.
      164Scopus© Citations 8
  • 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.
      169Scopus© Citations 17
  • 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.
  • 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.
      484Scopus© Citations 28
  • Publication
    Energy-Efficient Beam Coordination Strategies With Rate-Dependent Processing Power
    This paper proposes energy-efficient coordinated beamforming strategies for multicell multiuser multiple-input single-output system. We consider a practical power consumption model, where part of the consumed power depends on the base station or user specific data rates due to coding, decoding, and backhaul. This is different from the existing approaches where the base station power consumption has been assumed to be a convex or linear function of the transmit powers. Two optimization criteria are considered, namely network energy efficiency maximization and weighted sum energy efficiency maximization. We develop successive convex approximation-based algorithms to tackle these difficult nonconvex problems. We further propose decentralized implementations for the considered problems, in which base stations perform parallel and distributed computation based on local channel state information and limited backhaul information exchange. The decentralized approaches admit closed-form solutions and can be implemented without invoking a generic external convex solver. We also show an example of the pilot contamination effect on the energy efficiency using a heuristic pilot allocation strategy. The numerical results are provided to demonstrate that the rate dependent power consumption has a large impact on the system energy efficiency, and, thus, has to be taken into account when devising energy-efficient transmission strategies. The significant gains of the proposed algorithms over the conventional low-complexity beamforming algorithms are also illustrated.
      409Scopus© Citations 28
  • 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.
      326Scopus© Citations 18
  • Publication
    Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks
    Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users' quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.
      459Scopus© Citations 7
  • 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.
      272Scopus© Citations 41
  • 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.
      311Scopus© Citations 3