Massive MIMO cooperative communications for wireless sensor networks: Throughput and energy efficiency analysis

Abstract

The objective of this study is to analyze a new disruptive deployment of wireless sensors in order to cope with the explosive demand for bandwidth while taking into account energy consumption considerations. The work is grounded on the idea of massive network densification by drastically increasing the number of sensors in a given area in a Time Division Duplex (TDD) mode. Using ideas from the recent Massive MIMO technology (more than 400 antennas, without any modification of the network infrastructure), we transpose the idea to a massive deployment of sensors and show the benefits of such an infrastructure. This research is expected to provide the optimal deployment of massive wireless sensor networks in terms of cost/performance/complexity/energy efficiency trade-off and define the next generation wireless Machine-to-Machine (M2M) communication.

Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems

“…In third step, SE-EE relation is computed for the selection of M number of antennas by considering P CC , and CP as computed in Eqs. ( 17)- (19). Fourth step computes the SE-EE relation for the selection of multiple UEs as formulated in Eqs.…”

“…The model proposed in [18] has considered the PC model of the transceiver PC and the radiating PC, and at the same time uses a tight expression form to achieve the best EE-SE trade-off. In [19], the method based on the Pareto optimal set uses the proposed multi-objective optimization method to calculate the EE-SE compromise method. The method takes into account the number of antennas available at the PC and BS, and the Cobb-Douglas production model calculates a trade-off matrix to convert the optimization function into a single objective function.…”

“…In [9], the author developed a model to maximize throughput and EE. In [19], we can also find another model that contains 400 antennas and multiple transmitters for throughput analysis. This model provides a detailed background for studying the EE and throughput of wireless networks.…”

“…Step 2: Generate random estimated channel vectors H l l ∈ C for UL and DL Step 3: Compute SE-EE relation for the selection of multiple M as Eqs. ( 17)- (19) Step 4: Compute SE-EE relation for the selection of multiple UEs as Eqs. ( 22)- (24) Step 5: Compute receive combining vectors for UL as in Tab.…”