IIIT Hyderabad Publications
Distributed Spatial Modulation in Cognitive Relay Network
Author: Kunal Sankhe
Report no: IIIT/TH/2016/31
Spatial modulation (SM) is a recently developed multiple-input-multiple-output (MIMO) technique that uses multiple transmit antennas in a innovative fashion. In SM, a group of information bits is mapped into two constellations: a signal constellation based on modulation scheme, and a spatial con- stellation to encode the index of a transmit antenna. At any time instant, only one transmit antenna is active, whereas other transmit antennas radiate zero power. This completely avoids inter-channel in- terference at the receiver and relaxes the stringent requirement of synchronization among the transmit antennas. Moreover, unlike conventional MIMO system, SM does not require multiple RF chains at the transmitter. SM can outperform other state-of-the-art MIMO schemes in terms of computational complexity, if many transmit antennas are available. Unfortunately, this makes SM useful only in the downlink of the cellular network, where a base station can be equipped with a large number of antennas. On the other hand, use of multiple antennas in mobile terminals has practical limitations. Besides constraints on complexity and cost, decreasing terminal size restricts the application of SM in the uplink of cellular network. To overcome these problems, distributed spatial modulation (DSM) offers a promising solu- tion. In DSM, multiple relays form a virtual antenna array and assist a source to transmit its information to a destination by applying SM in a distributed manner. The source broadcasts its signal, which is inde- pendently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all equipped with cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end symbol error rate (SER) as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.
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