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Distributed Cooperative Spatial Multiplexing System

We consider distributed spatial multiplexing, in which a group of single-antenna terminals cooperate to transmit information to another such group using spatial multiplexing, thus increasing the capacity of the link between them. We focus on the link between relays and final destination, making use of Slepian-Wolf coding to compress the data and thus increase overall spectral efficiency. Here we implement the Slepian-Wolf coding using a Repeat-Accumulate-Repeat (RAR) code, and also apply an outer code. We analyse the system from the information theory point of view followed by optimization. We demonstrate the advantage of the cooperative spatial multiplexing system over the conventional spatial multiplexing system.

MIMO spatial multiplexing can increase the spectral efficiency of a communication system many-fold, without extra bandwidth or transmit power, however this advantage is based on the assumption that channels fadings between different transmit and receive antennas are independent. However this is not always the case unless elements in antenna array are separated by several wavelengths, which is difficult to implement in practice on small mobile devices. Distributed spatial multiplexing provides a solution: it organizes antennas on distributed mobile stations to form a virtual antenna arrays (VAAs) to support spatial multiplexing transmission from source node to destination node.

When spatially adjacent mobile stations communicate with each other, they form a mutually communicating entity, termed a Virtual Antenna Array (VAA), in which the wired links of traditional MIMO systems are replaced by wireless links. The transmitter sends information over its VAAs via intra-links (blue lines), the information is then forwarded by all the relays in the transmit VAAs cooperatively to the receive VAAs via the MIMO channel (green, purple, orange, black lines). Finally, the receiver gathers information via intra-links (red lines) from all the relays in the receive VAAs.

Members

  • Nian Xie
  • Alister Burr

Dates

  • Start: October 2010

Research