• A. Suban
• G.Bhuvaneshwaran

To improve the cellular energy efficiency, without sacrificing quality-of-service (QoS) at the users, the network topology must be densified to enable higher spatial reuse. In this work a combination of two densification approaches, namely “massive” multiple-input multiple-output (MIMO) base stations and multiflowbeamforming technique are incorporated to improve the performance of the System. The fundamental limit on the performance of Massive -MIMO antenna systems is due to failure in accurate channel estimation. To address this problem, an estimation of channel parameters of the desired links in a target cell is proposed. It is shown that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. The signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators; the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination. In addition to channel estimation, efficient energy power control technique for massive MIMO is adopted i.e., the total power consumption is minimized (both dynamic emitted power and static hardware power) while satisfying QoS constraints. This problem is proved to have a hidden convexity that enables efficient solution algorithms. Interestingly, the optimal solution promotes exclusive assignment of users to transmitters. Furthermore, promising simulation results showing how the total power consumption can be greatly improved by combining massive MIMO and joint multiflowbeamforming are provided.

Massive MIMO, Joint Multiflow Beamforming, Convex Optimization

"IMPROVING ENERGY EFFICIENCY BY BAYESIAN CHANNEL ESTIMATION AND JOINT MULTIFLOW BEAMFORMING IN MASSIVE MIMO SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.662-667, November-2018, Available :http://www.jetir.org/papers/JETIR1811A85.pdf

"IMPROVING ENERGY EFFICIENCY BY BAYESIAN CHANNEL ESTIMATION AND JOINT MULTIFLOW BEAMFORMING IN MASSIVE MIMO SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp662-667, November-2018, Available at : http://www.jetir.org/papers/JETIR1811A85.pdf