Fast Optimization with Zeroth-Order Feedback in Distributed, Multi-User MIMO Systems
| Autor: | Olivier Bilenne, Panayotis Mertikopoulos, Elena Veronica Belmega |
|---|---|
| Přispěvatelé: | Performance analysis and optimization of LARge Infrastructures and Systems (POLARIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Criteo AI Lab, Criteo [Paris], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), ANR-16-CE33-0004,ORACLESS,Stratégies adaptatives d'allocation des ressources dans les réseaux sans fil dynamiques(2016), ANR-18-CE40-0030,ELIOT,Technologies Emergentes pour l'Internet des Objets(2018), European Project: GAMENET |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer science Gaussian Computer Science - Information Theory G.1.6 Scalar (mathematics) MIMO Multi-user MIMO networks 02 engineering and technology Gradient-free optimization Topology symbols.namesake [INFO.INFO-MC]Computer Science [cs]/Mobile Computing C.2.1 [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.1: Network Architecture and Design/C.2.1.10: Wireless communication Electrical and Electronic Engineering Electrical Engineering and Systems Science - Signal Processing Matrix exponential learning Mathematics - Optimization and Control Computer Science::Information Theory Throughput maximization Information Theory (cs.IT) Estimator 020206 networking & telecommunications Multi-user MIMO Asynchronous communication Optimization and Control (math.OC) ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.6: Optimization Signal Processing symbols Matrix exponential [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] Communication channel |
| Zdroj: | IEEE Transactions on Signal Processing IEEE Transactions on Signal Processing, 2020, 68, pp.6085-6100. ⟨10.1109/TSP.2020.3029983⟩ IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2020, 68, pp.6085-6100. ⟨10.1109/TSP.2020.3029983⟩ |
| ISSN: | 1053-587X |
| DOI: | 10.1109/TSP.2020.3029983⟩ |
| Popis: | In this paper, we develop a gradient-free optimization methodology for efficient resource allocation in Gaussian MIMO multiple access channels. Our approach combines two main ingredients: (i) an entropic semidefinite optimization based on matrix exponential learning (MXL); and (ii) a one-shot gradient estimator which achieves low variance through the reuse of past information. This novel algorithm, which we call gradient-free MXL algorithm with callbacks (MXL0$^{+}$), retains the convergence speed of gradient-based methods while requiring minimal feedback per iteration$-$a single scalar. In more detail, in a MIMO multiple access channel with $K$ users and $M$ transmit antennas per user, the MXL0$^{+}$ algorithm achieves $\epsilon$-optimality within $\text{poly}(K,M)/\epsilon^2$ iterations (on average and with high probability), even when implemented in a fully distributed, asynchronous manner. For cross-validation, we also perform a series of numerical experiments in medium- to large-scale MIMO networks under realistic channel conditions. Throughout our experiments, the performance of MXL0$^{+}$ matches$-$and sometimes exceeds$-$that of gradient-based MXL methods, all the while operating with a vastly reduced communication overhead. In view of these findings, the MXL0$^{+}$ algorithm appears to be uniquely suited for distributed massive MIMO systems where gradient calculations can become prohibitively expensive. Comment: Final version; to appear in IEEE Transactions on Signal Processing; 16 pages, 4 figures |
| Databáze: | OpenAIRE |
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