Professor Martin Haenggi and his research group consisting of Sundaram Vanka, Sunil Srinivasa, Zhenhua Gong, Peter Vizi, and Kostas Stamatiou conduct the first successful software-radio implementation of superposition coding. This important work has been accepted for publication in the IEEE Transactions on Wireless Communications.
The problem of communicating with many receivers arises in many “downlink” scenarios such as communication from an access point to stations in WiFi or from a base station in cellular systems. The conventional approach is to set-up orthogonal channels to each user by time/frequency/code-division multiplexing. Although this approach eliminates interference between transmissions it does not, in general, achieve the highest possible transmission rates for a given packet error rate (or reliability). This maximum rate or capacity is achieved by Superposition Coding (SC), a well-known non-orthogonal scheme.
While information theory sufficiently motivates the use of SC, it is largely silent on practical issues such as finite block length codes, finite encoding and decoding complexity, hardware non-idealities (e.g., carrier frequency offset, phase noise) that one would encounter while designing such a system. This motivates the experimental study of SC.
Dr. Haenggi and his team have implemented superposition coding on a software radio platform using off-the-shelf single-user coding and decoding blocks. They experimentally determine the set of achievable rate-pairs for this system under a packet error constraint. Their results suggest that SC can provide substantial gains in spectral efficiencies over those achieved by orthogonal schemes such as time division multiplexing. The findings also question the validity of treating inter-user interference as Gaussian noise to measure system performance in practical systems, and thereby the validity of the Gaussian model for these systems. To the best of their knowledge, this is the first attempt at an experimental characterization of the performance of superposition coding.