Slotted Aloha for Networked Base Stations: Algorithms and Performance

Konferenz: European Wireless 2014 - 20th European Wireless Conference
14.05.2014 - 16.05.2014 in Barcelona, Spain

Tagungsband: European Wireless 2014

Seiten: 6Sprache: EnglischTyp: PDF

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Bajovic, Dragana; Jakovetic, Dusan (BioSense Center, University of Novi Sad, Novi Sad, Serbia)
Vukobratovic, Dejan; Crnojevic, Vladimir (Department of Power, Electronics, and Communications Engineering, University of Novi Sad, Novi Sad, Serbia)

We study slotted Aloha with multiple base stations, where m base stations cooperate to decode signals from n users. Both users and base stations are placed uniformly at random over an area. Each user transmits its packet replicas at multiple slots and is heard by all base stations in its geographical vicinity. We present cooperative decoding algorithms where base stations can utilize the successive interference cancellation (SIC) mechanism in two ways-spatially and temporally. Spatial SIC allows for the interference cleaning across neighboring base stations: when a base station decodes a user, it sends the users signal to all other stations that cover the user, thus enabling the interference cleaning at the receiving stations. With temporal SIC, each base station cleans the interference locally, across different time slots. Specifically, we present decoding schemes with four different degrees of cooperation, namely: 1) non-cooperative; 2) with spatial SIC only (spatial cooperation); with temporal SIC only (temporal cooperation); and 4) with both spatial and temporal SICs (spatio-temporal cooperation). We present several results on the performance of decoding algorithms. First, with each of the four algorithms, the peak throughput increases linearly in the number of base stations m. Second, simulation examples show that spatio-temporal cooperation yields significant gains over the other two schemes. Finally, we analyze with each of the schemes the threshold on the normalized load–largest load for which the decoding probability equals to its maximal possible value.