Wireless MIMO Switching with Zero-forcing Relaying and Network-coded Relaying

A wireless relay with multiple antennas is called a multiple-input-multiple-output (MIMO) switch if it maps its input links to its output links using "precode-and-forward." Namely, the MIMO switch precodes the received signal vector in the uplink using some matrix for transmission in the downlink. This paper studies the scenario of $K$ stations and a MIMO switch, which has full channel state information. The precoder at the MIMO switch is either a zero-forcing matrix or a network-coded matrix. With the zero-forcing precoder, each destination station receives only its desired signal with enhanced noise but no interference. With the network-coded precoder, each station receives not only its desired signal and noise, but possibly also self-interference, which can be perfectly canceled. Precoder design for optimizing the received signal-to-noise ratios at the destinations is investigated. For zero-forcing relaying, the problem is solved in closed form in the two-user case, whereas in the case of more users, efficient algorithms are proposed and shown to be close to what can be achieved by extensive random search. For network-coded relaying, we present efficient iterative algorithms that can boost the throughput further.Comment: This version is to appear in IEEE Journal on Selected Areas in Communications later in 201

The Maximum Stable Broadcast Throughput for Wireless Line Networks with Network Coding and Topology Control

Abstract—We consider broadcasting from a single source to multiple destinations in a linear wireless erasure network with feedback. The problem is to find the maximum stable throughput under different transmission policies with opportunistic network coding and forwarding. Given stochastically varying traffic, we assume that network coding decisions are based on the availability of queued packets. The network is clustered into groups of terminals and network coding is applied locally to packets within each group. This allows us to evaluate the effects of topology control on the maximum stable rate. For each transmission policy we derive the optimal cluster size. We show that network coding improves the stable rate over plain retransmissions, and the network coding gain significantly benefits from opportunistic network coding, forwarding and topology control, ranging from 33 % to 410%, depending on the physical channel parameters in the numerical experiments. I