On Ergodic Sum Capacity of Fading Cognitive Multiple-Access and Broadcast Channels

By Rui Zhang, Shuguang Cui, Ying-Chang Liang. 2008

247 citations recorded by the source corpus.

This paper studies the information-theoretic limits of a secondary or cognitive radio (CR) network under spectrum sharing with an existing primary radio network. In particular, the fading cognitive multiple-access channel (C-MAC) is first studied, where multiple secondary users transmit to the secondary base station (BS) under both individual transmit-power constraints and a set of interference-power constraints each applied at one of the primary receivers. This paper considers the long-term (LT) or the short-term (ST) transmit-power constraint over the fading states at each secondary transmitter, combined with the LT or ST interference-power constraint at each primary receiver. In each case, the optimal power allocation scheme is derived for the secondary users to achieve the ergodic sum capacity of the fading C-MAC, as well as the conditions for the optimality of the dynamic time-division multiple-access (D-TDMA) scheme in the secondary network. The fading cognitive broadcast channel (C-BC) that models the downlink transmission in the secondary network is then studied under the LT/ST transmit-power constraint at the secondary BS jointly with the LT/ST interference-power constraint at each of the primary receivers. It is shown that D-TDMA is indeed optimal for achieving the ergodic sum capacity of the fading C-BC for all combinations of transmit-power and interference-power constraints.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “In the C-MAC system, the optimality of the proposed DL methods, which is established analytically in Propositions 1 and 2, is veriﬁed numerically by investigating the optimal solution provided in [24] both for primal and dual domains.”
Price-Based Resource Allocation for Spectrum-Sharing Femtocell Networks: A Stackelberg Game Approach (2012, background): “1, user i denotes the scheduled user transmitting to its HBS Bi, where i = 1, 2, · · · , N .”
Constrained Deep Learning for Wireless Resource Management (2019, methodology): “, the average transmit power budget and the average quality-of-service constraints at mobile users evaluated over fast fading channel coefficients [10], [11].”
Cognitive radio networking and communications: an overview (2011, background): “The results for multiple-access and broadcast channels under interference power constraints can be found in [69] and [70].”
Throughput maximization in wireless powered communication networks (2013, methodology): “For comparison, we consider UL transmissions in a conventio nal TDMA-based wireless network with WIT only [13]-[16], where each user is equipped with a consta t energy supply, and thus has an equal energy consumption at each block denoted by Ē.”