Randomized Resource Allocation in Decentralized Wireless Networks

被引:6
|
作者
Moshksar, Kamyar [1 ]
Bayesteh, Alireza [2 ]
Khandani, Amir K. [1 ]
机构
[1] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[2] Res Mot, Waterloo, ON N2L 5Z5, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Decentralized networks; frequency hopping (FH); mixed Gaussian interference; multiplexing gain; outage capacity; randomized signaling; spectrum sharing; COGNITIVE RADIO; POWER-CONTROL; SCALING LAWS; CAPACITY; THROUGHPUT; COMMUNICATION; BLUETOOTH; CHANNELS;
D O I
10.1109/TIT.2011.2112030
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we consider a decentralized wireless communication network with a fixed number u of frequency subbands to be shared among N transmitter-receiver pairs. It is assumed that the number of active users is a realization of a random variable with a given probability mass function. Moreover, users are unaware of each other's codebooks and hence, no multiuser detection is possible. We propose a randomized frequency hopping (FH) scheme in which each transmitter randomly hops over a subset of u subbands from transmission slot to transmission slot. Assuming all users transmit Gaussian signals, the distribution of the noise plus interference is mixed Gaussian, which makes calculation of the mutual information between the transmitted and received signals of each user intractable. We derive lower and upper bounds on the mutual information of each user and demonstrate that, for large signal-to-noise ratio (SNR) values, the two bounds coincide. This observation enables us to compute the sum multiplexing gain of the system and obtain the optimum hopping strategy for maximizing this quantity. We compare the performance of the FH system to that of the frequency division (FD) system in terms of the following performance measures: average sum multiplexing gain (eta((1))) and average minimum multiplexing gain per user eta((2)). We show that (depending on the probability mass function of the number of active users) the FH system can offer a significant improvement in terms of eta((1)) and eta((2)) (implying a more efficient usage of the spectrum). In the sequel, we consider a scenario where the transmitters are unaware of the number of active users in the network as well as the channel gains. Developing a new upper bound on the differential entropy of a mixed Gaussian random vector and using entropy power inequality, we obtain lower bounds on the maximum transmission rate per user to ensure a specified outage probability at a given SNR level. We demonstrate that the so-called outage capacity can be considerably higher in the FH scheme than in the FD scenario for reasonable distributions on the number of active users. This guarantees a higher spectral efficiency in FH compared to FD.
引用
收藏
页码:2115 / 2142
页数:28
相关论文
共 50 条
  • [1] Decentralized Wireless Resource Allocation with Graph Neural Networks
    Wang, Zhiyang
    Eisen, Mark
    Ribeiro, Alejandro
    2020 54TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, 2020, : 299 - 303
  • [2] Randomized Resource Allocation for Wireless Networks with Cooperative Relay
    Wu, Di
    Zhu, Gang
    Liu, Lina
    Li, Yuanxuan
    2012 8TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC), 2012, : 974 - 978
  • [3] Pareto-Optimal Resource Allocation in Decentralized Wireless Powered Networks
    Bouzinis, Pavlos S.
    Diamantoulakis, Panagiotis D.
    Fan, Lisheng
    Karagiannidis, George K.
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2021, 69 (02) : 1007 - 1020
  • [4] Implications of Decentralized Q-learning Resource Allocation in Wireless Networks
    Wilhelmi, Francesc
    Bellalta, Boris
    Cano, Cristina
    Jonsson, Anders
    2017 IEEE 28TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2017,
  • [5] GROWS - Improving Decentralized Resource Allocation in Wireless Networks through Graph Neural Networks
    Randall, Martin
    Belzarena, Pablo
    Larroca, Federico
    Casas, Pedro
    PROCEEDINGS OF THE 1ST INTERNATIONAL WORKSHOP ON GRAPH NEURAL NETWORKING, GNNET 2022, 2022, : 24 - 29
  • [6] Resource allocation in wireless networks
    Jordan, S
    JOURNAL OF HIGH SPEED NETWORKS, 1996, 5 (01) : 23 - 34
  • [7] Resource allocation in wireless networks
    Stanczak, Slawomir
    Wiczanowski, Marcin
    Boche, Holger
    RESOURCE ALLOCATION IN WIRELESS NETWORKS: THEORY AND ALGORITHMS, 2006, 4000 : 1 - +
  • [8] Decentralized, adaptive resource allocation for sensor networks
    Mainland, G
    Parkes, DC
    Welsh, M
    USENIX Association Proceedings of the 2nd Symposium on Networked Systems Design & Implementation (NSDI '05), 2005, : 315 - 328
  • [9] Decentralized resource allocation in application layer networks
    Eymann, T
    Reinicke, M
    Ardaiz, O
    Artigas, P
    Freitag, F
    Navarro, L
    CCGRID 2003: 3RD IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER COMPUTING AND THE GRID, PROCEEDINGS, 2003, : 645 - 650
  • [10] DECENTRALIZED RESOURCE ALLOCATION IN DYNAMIC NETWORKS OF AGENTS
    Lakshmanan, Hariharan
    De Farias, Daniela Pucci
    SIAM JOURNAL ON OPTIMIZATION, 2008, 19 (02) : 911 - 940