Energy efficiency with adaptive decoding power and wireless backhaul small cell selection

TM Nguyen, A Yadav, W Ajib… - 2016 IEEE Global …, 2016 - ieeexplore.ieee.org
2016 IEEE Global Communications Conference (GLOBECOM), 2016ieeexplore.ieee.org
This paper considers the problem of maximizing energy efficiency on the downlink of two-tier
wireless backhaul small cell heterogeneous networks, where an interference mitigation
strategy that combines reverse time division duplexing and equally orthogonal spectrum
splitting is proposed. By enabling the small cell access points with the capability of switching
ON/OFF, we develop a joint design of transmit beamforming, power and small cell selection
that maximizes the proposed weighted access energy efficiency metric. To better convey the …
This paper considers the problem of maximizing energy efficiency on the downlink of two-tier wireless backhaul small cell heterogeneous networks, where an interference mitigation strategy that combines reverse time division duplexing and equally orthogonal spectrum splitting is proposed. By enabling the small cell access points with the capability of switching ON/OFF, we develop a joint design of transmit beamforming, power and small cell selection that maximizes the proposed weighted access energy efficiency metric. To better convey the total power consumption model, we assume the adaptive decoding power model at each small cell access point. The formulated problem is combinatorial and non-convex, which is NP-hard in general. Hence, to find a more realistic close-to-optimal feasible solution, we iteratively approximate the non-convex constraints in the formulated problem as second order cone ones based on the first order Taylor convex approximation and error-controlled second order cone approximation. The problem arrived at each iteration is a mixed integer second order cone programming, which can be solved optimally and efficiently by available dedicated solver to achieve the final result at convergence. Numerical results are studied to show the improvement of our proposed model compared to previous works.
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