Abstract
This paper proposes an adaptive large scale antenna system (ALSAS) for enhancing energy efficiency in low density wireless network scenarios. The proposed ALSAS comprises of two stages, a novel adaptive discontinuous transmission (ADTx) stage and an antenna array optimization (AAO) one. The basic idea is to utilize prior knowledge of the users' quality of service (QoS) requirements as well as precoding selection in the ADTx stage to maximize the transmitter hibernation periods subject to a certain complexity constraint. In the AAO stage, further power saving is achieved by reducing the number of active antenna elements subject to a certain QoS requirement. It is shown that, relative to conventional large scale antenna system (LSAS), the proposed ALSAS system achieves significant energy efficiency improvements under various scenarios. The results show that the proposed technique can provide energy efficiency improvement between 125% and 1124% in the suburban scenario, and between 196% and 952% in the rural scenario. It is also demonstrated that for rural environments with relatively small short inter-site-distance (ISD) values, ALSAS can provide up to 500% power saving for the fixed bit rate requirement case.
Highlights
It is well-known that multiple-input multiple-output (MIMO) technology can significantly increase the network capacity through spatial multiplexing [1]
The achievable bit rate per orthogonal frequency division multiplexing (OFDM) symbol for conjugate beamforming (CJ) and zero forcing (ZF) requires the signal-to-interference and noise ratio (SINR) of the CJ and ZF precoders at the lth scheduled transmissions (STs) for all users, which are given by γ G,l = γG(1,l), γG(2,l), . . . , γG(K,l G,l ) and γ Z,l = γZ(1,l), γZ(2,l), . . . , γZ(,Kl Z,l), respectively, where γG(k,l) = γk | M, KG,l, aG,k, μG, k = 1, . . . , KG,l (11) γZ(k,l) = γk | MZ, KZ,l, aZ,k, μZ, k = 1, . . . , KZ,l (12)
The adaptive DTx (ADTx) stage is responsible for dividing the transmission into L STs and selecting a suitable precoding technique per ST such that the total transmission time and latency are minimized
Summary
It is well-known that multiple-input multiple-output (MIMO) technology can significantly increase the network capacity through spatial multiplexing [1]. The combination of low power consumption and high achievable throughput is what makes LSAS an ideal technology for 5G networks This is especially true for a high user-density scenario where the number of multiplexed users is large. As compared to [30]–[32], this paper extends and refines the DTX scheme to a full adaptive one and tailors its optimization to low density sub-urban and rural scenarios It includes both analytical and simulation results and extends the results and discussion sections to include various parameters of interests such as spectral efficiency and outage probability, in addition to the EE enhancement achieved in different environments.
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