1 research outputs found
Multiple Access in Dynamic Cell-Free Networks: Outage Performance and Deep Reinforcement Learning-Based Design
In future cell-free (or cell-less) wireless networks, a large number of
devices in a geographical area will be served simultaneously in non-orthogonal
multiple access scenarios by a large number of distributed access points (APs),
which coordinate with a centralized processing pool. For such a centralized
cell-free network with static predefined beamforming design, we first derive a
closed-form expression of the uplink per-user probability of outage. To
significantly reduce the complexity of joint processing of users' signals in
presence of a large number of devices and APs, we propose a novel dynamic
cell-free network architecture. In this architecture, the distributed APs are
partitioned (i.e. clustered) among a set of subgroups with each subgroup acting
as a virtual AP equipped with a distributed antenna system (DAS). The
conventional static cell-free network is a special case of this dynamic
cell-free network when the cluster size is one. For this dynamic cell-free
network, we propose a successive interference cancellation (SIC)-enabled signal
detection method and an inter-user-interference (IUI)-aware DAS's receive
diversity combining scheme. We then formulate the general problem of clustering
APs and designing the beamforming vectors with an objective to maximizing the
sum rate or maximizing the minimum rate. To this end, we propose a hybrid deep
reinforcement learning (DRL) model, namely, a deep deterministic policy
gradient (DDPG)-deep double Q-network (DDQN) model, to solve the optimization
problem for online implementation with low complexity. The DRL model for
sum-rate optimization significantly outperforms that for maximizing the minimum
rate in terms of average per-user rate performance. Also, in our system
setting, the proposed DDPG-DDQN scheme is found to achieve around of the
rate achievable through an exhaustive search-based design.Comment: This article has been submitted to IEEE for possible publicatio