Greedy SINR Maximization in Collaborative Multibase Wireless Systems

Abstract

<p>We present a codeword adaptation algorithm for collaborative multibase wireless systems. The system is modeled with multiple inputs and multiple outputs (MIMO) in which information is transmitted using multicode CDMA, and codewords are adapted based on greedy maximization of the signal-to-interference-plus-noise ratio. The procedure monotonically increases the sum capacity and, when repeated iteratively for all codewords in the system, converges to a fixed point. Fixed-point properties and a connection with sum capacity maximization, along with a discussion of simulations that corroborate the basic analytic results, are included in the paper.</p

Simultaneous Water Filling in Mutually Interfering Systems

“…This more global view of a spectrum advisor begs a comparison of collaborative versus noncollaborative approaches since one could imagine that in certain circumstances (such as strong interference) collaborative decoding could produce impressive capacity gains. That is, in a world increasingly populated by smart wideband radios, the best advice might be to cooperate [16], [19], [27], [28], [39] as opposed to finding ways to independently share the signal space. Likewise, there are also scenarios where the increased complexity of collaboration or explicitly coordinating spectrum use might not be worth meager best-case capacity gains.…”

“…which we call the collective capacity in order to distinguish it from the information-theoretic sum capacity used when receivers can collaborate [27], [28]. This measure is useful in the case g 1 g 2 ≥ 1 (where multiple simultaneous water filling points are possible) to evaluate which point is a most desirable from a global perspective.…”

“…In unlicensed bands, spectrum is shared by all transmitters and receivers, and decoding information from a given transmitter by the intended receiver must cope with interference from "alien" transmitters. Under certain conditions (such as co-located receivers, or receivers connected to a common high speed backbone network) a wireless communication system can be modeled as a multiple access channel [13, p. 388], for which a vast body of literature offers numerous methods of decoding information in the presence of interference [16], [19], [27], [28], [39], [40]. That is, if receivers are allowed to collaborate, then they can be regarded as one large receiver with multiple inputs and multiple outputs (MIMO), and results established for MIMO systems pertain [17], [28].…”