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.
      20
  • Publication
    Optimization of RIS-aided MIMO Systems via the Cutoff Rate
    The main difficulty concerning optimizing the mutual information (MI) in reconfigurable intelligent surface (RIS)-aided communication systems with discrete signaling is the inability to formulate this optimization problem in an analytically tractable manner. Therefore, we propose to use the cutoff rate (CR) as a more tractable metric for optimizing the MI and introduce two optimization methods to maximize the CR. The first method is based on the projected gradient method (PGM), while the second method is derived from the principles of successive convex approximation (SCA). Simulation results show that the proposed optimization methods significantly enhance the CR and the corresponding MI.
      131Scopus© Citations 10
  • 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.
      102Scopus© Citations 16
  • Publication
    Joint Virtual Computing and Radio Resource Allocation in Limited Fronthaul Green C-RANs
    We consider the virtualization technique in the downlink transmission of limited fronthaul capacity cloud-radio access networks. A novel virtual computing resource allocation (VCRA) method which can dynamically split the users workload into smaller fragments to be served by virtual machines is presented. Under the proposed scheme, we aim at maximizing the network energy efficiency by a joint design of virtual computing resources, transmit beamforming, remote radio head (RRH) selection, and RRH-user association. Moreover, we construct a more realistic fronthaul power consumption model, which is directly proportional to users' rate transmitted by the corresponding RRHs. The formulated problem is combinatorial and difficult to solve in general. Our first contribution is to customize a branch-and-reduce-and-bound method to attain a globally optimal solution. To compute a high-quality approximate solution, a standard routine is used to deal with the continuous relaxation of the original problem. However, the proposed continuous relaxation is non-convex which implies another challenge. For a practically appealing solution approach, we resort to a local optimization method, namely the difference of convex algorithm. Our second contribution is on the use of Lipschitz continuity to arrive at a sequence of convex quadratic programs, which can be solved efficiently by modern convex solvers. Finally, a post-processing procedure is proposed to obtain a high-performance feasible solution from the continuous relaxation. Extensive numerical results demonstrate that the proposed algorithms converge rapidly and achieve near-optimal performance as well as outperform other known methods. Moreover, we numerically show that the VCRA scheme significantly improves the system energy efficiency compared to the existing schemes.
      383Scopus© Citations 27
  • 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.
      178Scopus© Citations 5
  • 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.
      222Scopus© Citations 17
  • Publication
    A Comparison of the Uplink Performance of Cell-Free Massive MIMO using Three Linear Combining Schemes: Full-Pilot Zero Forcing with Access Point Selection, Matched-Filter and Local-Minimum-Mean-Square Error
    (IEEE, 2020-06-12) ;
    In this paper, three types of linear receiver for the uplink of cell-free massive multiple-input-multiple-output (MIMO) will be studied to gain a clear comparison and understanding of their performance. In a cell-free massive MIMO system, a large number of randomly distributed access points (APs) cooperate to serve a much smaller number of users in the same time-frequency resource. The three receivers of interest are matched-filter (MF) combining, full-pilot zero forcing (fpZF) combining and the local-minimum-mean-squared error (L-MMSE) combining. The APs use locally obtained channel state information to perform the combining. Max-min fairness power control is utilised for the MF and fpZF combining to ensure uniformly good service for all users in the system. We note that max-min fairness power control is not required for the L-MMSE combining since the L-MMSE scheme itself can provide the worst served users with the same spectral efficiency as the MF with max-min fairness. In this paper an AP selection scheme is proposed for the fpZF combining. In particular, the proposed AP selection scheme provides users with reasonably good spectral efficiency using a subset of APs rather than all APs serving all users, which proves to increase the overall energy efficiency of the system. The results show that the fpZF consistently outperforms the MF and L-MMSE even while using only a subset of the APs.
      182Scopus© Citations 1
  • Publication
    Revisiting the MIMO Capacity With Per-Antenna Power Constraint: Fixed-Point Iteration and Alternating Optimization
    In this paper, we revisit the fundamental problem of computing MIMO capacity under per-antenna power constraint (PAPC). Unlike the sum power constraint counterpart which likely admits water-filling-like solutions, MIMO capacity with PAPC has been largely studied under the framework of generic convex optimization. The two main shortcomings of these approaches are (i) their complexity scales quickly with the problem size, which is not appealing for large-scale antenna systems, and/or (ii) their convergence properties are sensitive to the problem data. As a starting point, we first consider a single user MIMO scenario and propose two provably-convergent iterative algorithms to find its capacity, the first method based on fixed-point iteration and the other based on alternating optimization and minimax duality. In particular, the two proposed methods can leverage the water-filling algorithm in each iteration and converge faster, compared to current methods. We then extend the proposed solutions to multi-user MIMO systems with dirty paper coding (DPC) based transmission strategies. In this regard, capacity regions of Gaussian broadcast channels with PAPC are also computed using closed-form expressions. Numerical results are provided to demonstrate the outperformance of the proposed solutions over existing approaches.
      385Scopus© Citations 16
  • 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.
      152
  • Publication
    Fall Detection using Wi-Fi Signals and Threshold-Based Activity Segmentation
    (IEEE, 2020-09-03) ;
    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.
      228Scopus© Citations 5