Energy Efficient SWIPT Systems in Multi-Cell MISO Networks

Jang, Seokju; Lee, Hoon; Kang, Seowoo; Oh, Taehyoun; Lee, Inkyu

doi:10.1109/twc.2018.2874646

Cited by 35 publications

(25 citation statements)

References 41 publications

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“…For baseline scheme 1, we consider proposed scheme in [33], where the subchannel and power allocations are decomposed into two subproblems while considering the system throughput optimization. For baseline scheme 2, we adopt the enhance method proposed in [24] where the subcarrier assignemnt is also considered for L = 1. For baseline scheme 3, we consider our proposed scheme based on the OMA in which we set L = 1 for the simulation results.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [23] considered SWIPT in multi-cell multi-user MISO SWIPT network for OFDMA network while considering fix beamforming design such as zero-forcing to maximize EE. Moreover, in [24] proposed beamforming for multi-cell multi-user OFDMA system to maximize energy efficiency (EE). In fact, the work in [24] improved the beamforming design of work [23].…”

Section: B Related Workmentioning

confidence: 99%

“…Moreover, in [24] proposed beamforming for multi-cell multi-user OFDMA system to maximize energy efficiency (EE). In fact, the work in [24] improved the beamforming design of work [23]. However, they addressed SWIPT for the single carrier OFDMA system.…”

Section: B Related Workmentioning

confidence: 99%

“…The EH receiver is a low-power device capable of harvesting energy and the ID receiver is only able to process data. Since the ability to harvest energy deteriorates with distance, EH receivers ought to be closer to the BS or AP than ID receivers (which have to be spatially separated) [14], [24], [34]- [36]. The set of ID and EH users at the j-th cell are defined as…”

Section: System Modelmentioning

confidence: 99%

“…Algorithm 1 Our Proposed Iterative Method via MM approach 1: Initialize t = 0 and maximum number of iteration Tmax, penalty factor λ 1 and feasible set vector a 0 ,W 0 E ,W 0 , and ξ 0 . 2: Repeat 3: UpdateẼ(a),G(W),T (W), andṼ (W) as presented in (24), (25), (26), and (27) 4: Solve optimization problem of (35) and store the intermediate resource allocation policy a t ,W t E ,W t , and ξ t 5: Set t = t + 1 and a t = a,W t E =WE,W t =W, and ξ t = ξ 6: Until convergence or t = Tmax 7: a * = a t ,W * E =W t E ,W * =W t , and ξ * = ξ t . 8: end…”

Section: Proposed Solutionmentioning

confidence: 99%

See 4 more Smart Citations

Resource Allocation for MC MISO-NOMA SWIPT-Enabled HetNets With Non-Linear Energy Harvesting

2020

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This paper proposes a resource allocation for multi carrier (MC) multiple input single output (MISO) non-orthogonal multiple access (NOMA) enabling simultaneous wireless information and power transfer (SWIPT) where a separated architecture (SA)-based heterogeneous network (HetNet) is considered. More precisely, we focus on designing MC MISO-NOMA scheme in HetNets, where each base stations (BSs) serves multiple information decoding (ID) and energy harvesting (EH) users with a non-linear energy harvesting model which are equipped with multi antennas. For this configuration, we propose a joint subcarrier assignment, beamforming design, and energy beamforming to maximize the total rate of the network. Moreover, we take into account the condition of cross-layer interference constraints to coordinate interference, satisfying a minimum data rate requirement, fulfilling a minimum harvested energy, and respecting the maximum allowed power transfer constraints. We also consider successive interference cancellation (SIC) decoding order to detect the signal of each user due to coupling between SIC order, beamforming design, and subcarrier allocation. As a result of interference in the rate function, our optimization problem is non-convex and thus challenging to solve. Subsequently, first we adopt semidefinite relaxation (SDR) for beamforming design and then an efficient algorithm via the majorization minimization (MM) approach based on the difference of convex (DC) programming is proposed to obtain a locally optimal solution for the original problem. Simulation results reveal the superiority of our proposed scheme as compared to other existing works addressed in the literature. INDEX TERMS Multi carrier (MC), multiple input single output (MISO), non-orthogonal multiple access (NOMA), simultaneous wireless information and power transfer (SWIPT), subcarrier assignment, beamforming design, and successive interference cancellation (SIC).

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Section: Simulation Resultsmentioning

confidence: 99%