Traffic-Aware Training and Scheduling for MISO Wireless Downlink Systems

Abstract

In this paper, the problem of feedback and active user selection in MISO wireless systems such that the system's stability region is as big as possible is examined. The focus is on a system in a Rayleigh fading environment where zero forcing precoding is used to serve all active users in every slot.Acquisition of the channel states is done via uplink training in Time Division Duplexing mode by the active users. Clearly, only a subset of users can perform uplink training and the selection of this subset is a challenging and interesting problem especially in MISO systems. The stability regions of a baseline centralized scheme and two novel decentralized policies are examined analytically. In the decentralized schemes, the transmitter broadcasts periodically the queue state information and the users contend for the channel in a CSMA-based manner with parameters based on the outdated queue state information and real-time channel state information. We show that, using infrequent signaling between the base station and the users, the decentralized policies outperform the centralized policy. In addition a threshold-based user selection and training scheme for discrete-time contention is proposed. The results of this work imply that, as far as stability is concerned , the users must be involved in the active user selection and feedback/training decision. This should be leveraged in future communication systems.A.Destounis is with CentraleSupélec,

Whittle Index Policy for Multichannel Scheduling in Queueing Systems

“…There are lot of works which study the problem of resource allocation in wireless networks. For instance, in [5] [6] [16] [7], the authors give a throughput optimal policy for single channel, multi-channel and multi-user MIMO contexts using max weight rule, which is known to not be delay optimal. To overcome this matter, many works have been developed in the past to minimize the average delay of the traffic of the users (e.g.…”

Traffic-aware training and scheduling for the 2-user MISO broadcast channel

“…If F receivers are served, the following result will be useful to calculate the average rates: Proposition 1. It holds [11]:…”

“…This defines a system that, for every step m takes the values of the original system "sampled" every T timeslots q(mT ). It can be proven that the stability regions of both are the same [11], therefore we will restrict our attention to q(m) for the rest of the analysis.…”

“…Let us now prove that their region is indeed achievable by the decentralized policy. For any vector λ inside this region, denotingμ d k (m) be the random variable representing the service receiver k gets at a slot of {mT + 1, ..., mT + T − 1}, we can show, after some calculations [11], that the drift of the quadratic Lyapunov function V (x) = x k (m) > 2Q +C/ , thus, from the Foster-Lyapunov criterion, the system under the decentralized policy achieves indeed the stability region given by (6).…”

“…Combined with some (infrequent) signalling regarding the users queue lengths from the base station, we will see in the following that properly combining the decentralized and centralized approaches leads to a bigger achievable stability region than using the centralized approach alone. Note: Due to space constraints, the proofs of intermediate results are not presented here but can be found in [11].…”