Retrospective Interference Alignment for Two-Cell Uplink MIMO Cellular Networks with Delayed CSIT

In this paper, we propose a new retrospective interference alignment for two-cell multiple-input multiple-output (MIMO) interfering multiple access channels (IMAC) with the delayed channel state information at the transmitters (CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF compared to the case of no CSIT. The key idea is to align multiple interfering signals from adjacent cells onto a small dimensional subspace over time by fully exploiting the previously received signals as side information with outdated CSIT in a distributed manner. Remarkably, we show that the retrospective interference alignment can achieve the optimal sum-DoF in the context of two-cell two-user scenario by providing a new outer bound.Comment: 7 pages, 2 figures, to appear in IEEE ICC 201

Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging

The design of beamforming for downlink multi-user massive multi-input multi-output (MIMO) relies on accurate downlink channel state information (CSI) at the transmitter (CSIT). In fact, it is difficult for the base station (BS) to obtain perfect CSIT due to user mobility, latency/feedback delay (between downlink data transmission and CSI acquisition). Hence, robust beamforming under imperfect CSIT is needed. In this paper, considering multiple antennas at all nodes (base station and user terminals), we develop a multi-agent deep reinforcement learning (DRL) framework for massive MIMO under imperfect CSIT, where the transmit and receive beamforming are jointly designed to maximize the average information rate of all users. Leveraging this DRL-based framework, interference management is explored and three DRL-based schemes, namely the distributed-learning-distributed-processing scheme, partial-distributed-learning-distributed-processing, and central-learning-distributed-processing scheme, are proposed and analyzed. This paper \textrm{1)} highlights the fact that the DRL-based strategies outperform the random action-chosen strategy and the delay-sensitive strategy named as sample-and-hold (SAH) approach, and achieved over 90$\%$ of the information rate of two selected benchmarks with lower complexity: the zero-forcing channel-inversion (ZF-CI) with perfect CSIT and the Greedy Beam Selection strategy, \textrm{2)} demonstrates the inherent robustness of the proposed designs in the presence of user mobility.Comment: submitted for publicatio

DoF Analysis of the MIMO Broadcast Channel With Alternating/Hybrid CSIT

We consider a K-user multiple-input singleoutput (MISO) broadcast channel (BC) where the channel state information (CSI) of user i(i = 1,2, .. ., K) may be instantaneously perfect (P), delayed (D), or not known (N) at the transmitter with probabilities λ P i , λ D i , and λ N i , respectively. In this setting, according to the three possible CSI at the transmitter (CSIT) for each user, knowledge of the joint CSIT of the K users could have at most 3K states. In this paper, given the marginal probabilities of CSIT (i.e., λ P i , λ D i , and λ N i ), we derive an outer bound for the degrees of freedom (DoF) region of the K-user MISO BC. Subsequently, we tighten this outer bound by considering a set of inequalities that capture some of the 3K states of the joint CSIT. One of the consequences of this set of inequalities is that for K ≥ 3, it is shown that the DoF region is not completely characterized by the marginal probabilities in contrast to the two-user case. Afterwards, the tightness of these bounds is investigated through the discussion on the achievability. Finally, a two user multiple-input multipleoutput BC having CSIT among P and N is considered in which an outer bound for the DoF region is provided, and it is shown that in some scenarios, it is tight