Now showing 1 - 8 of 8
  • 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.
    Scopus© Citations 3  292
  • 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 â„“ 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.
      269
  • Publication
    Efficient Algorithms for Sum Rate Maximization in Fronthaul-Constrained C-RANs
    We consider downlink transmission of a fronthaul-constrained cloud radio access network. Our aim is to maximize the system sum data rate via jointly designing beamforming and user association. The problem is basically a mixed integer non-convex programs for which a global solution requires a prohibitively high computational effort. The focus is thus on efficient solutions capable of achieving the near optimal performance with low complexity. To this end, we transform the design problem into continuous programs by two approaches: penalty and sparse approximation methods. The resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results indicate that the proposed methods are near-optimal, and outperform existing suboptimal methods in terms of achieved performances and computational complexity.
      260
  • 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.
    Scopus© Citations 17  156
  • Publication
    A Low-Complexity Algorithm for Achieving Secrecy Capacity in MIMO Wiretap Channels
    We consider a secure transmission including a transmitter, a receiver and an eavesdropper, each being equipped with multiple antennas. The aim is to develop a low-complexity and scalable method to find a globally optimal solution to the problem of secrecy rate maximization under a total power constraint at the transmitter. In principle, the original formulation of the problem is nonconvex. However, it can be equivalently translated into finding a saddle point of a minimax convex-concave program. An existing approach finds the saddle point using the Newton method, whose computational cost increases quickly with the number of transmit antennas, making it unsuitable for large scale antenna systems. To this end, we propose an iterative algorithm based on alternating optimization, which is guaranteed to converge to a saddle point, and thus achieves a globally optimal solution to the considered problem. In particular, each subproblem of the proposed iterative method admits a closed-form solution. We analytically show that the iteration cost of our proposed method is much cheaper than that of the known solution. As a result, numerical results demonstrate that the proposed method remarkably outperforms the existing one in terms of the overall run time.
    Scopus© Citations 6  301
  • Publication
    Globally Optimal Energy Efficiency Maximization for Capacity-Limited Fronthaul Crans with Dynamic Power Amplifiers’ Efficiency
    A joint beamforming and remote radio head (RRH)-user association design for downlink of cloud radio access networks (CRANs) is considered. The aim is to maximize the system energy efficiency subject to constraints on users' quality-of-service, capacity offronthaullinks and transmit power. Different to the conventional power consumption models, we embrace the dependence of baseband signal processing power on the data rate, and the dynamics of the power amplifiers' efficiency. The considered problem is a mixed Boolean nonconvex program whose optimal solution is difficult to find. As our main contribution, we provide a discrete branch-reduce-and-bound (DBRnB) approach to solve the problem globally. We also make some modifications to the standard DBRnB procedure. Those remarkably improve the convergence performance. Numerical results are provided to confirm the validity of the proposed method.
      472
  • 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.
      48
  • 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.
      149Scopus© Citations 8