Multi-Objective Optimization for Distributed MIMO Networks

Li, Zan; Gong, Shiqi; Fei, Zesong; Yan, Xiaolang

doi:10.1109/tcomm.2017.2722478

Cited by 13 publications

(14 citation statements)

References 37 publications

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“…These performance metrics often conflict with each other, and an increase in one metric may lead to a decrease in another metric. Thus, MOO theory has been a promising mathematical tool to cope with the existence of multiple metrics and reveal the tradeoffs among them [18], [19]. For example, the author in [18] developed an MOO framework in cognitive radio networks with SWIPT incorporating three system design objectives: total transmit power minimization, EHE maximization, and interference-power-leakage-totransmit-power ratio minimization.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

“…For example, the author in [18] developed an MOO framework in cognitive radio networks with SWIPT incorporating three system design objectives: total transmit power minimization, EHE maximization, and interference-power-leakage-totransmit-power ratio minimization. Reference [19] utilized MOO theory to study three critical issues for MIMO interference networks, i.e., signal transmission, energy and security.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

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Multi-Objective Optimization for Full-Duplex SWIPT Systems

Tao

et al. 2020

IEEE Access

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This paper studies a multi-objective optimization (MOO) problem in full-duplex (FD) networks with simultaneous wireless information and power transfer (SWIPT). In the considered networks, an FD base station (BS) communicates with multiple half-duplex (HD) uplink (UL) and downlink (DL) users simultaneously. The energy receivers (ERs) harvest energy from the ambient radio frequency (RF) signals and may act as potential eavesdroppers. Specifically, there exists two conflicting yet important system design objectives, i.e., secrecy energy efficiency (SEE) maximization and energy harvesting efficiency (EHE) maximization. An MOO design based on the weighted Tchebycheff approach is proposed to investigate the tradeoff between these two design objectives. The proposed design takes into account the quality-ofservice (QoS) guarantees of secrecy rate and energy harvesting (EH) under the imperfect channel state information (CSI) of ERs. The formulated MOO problem is solved with a two-layer optimization algorithm, where the modified Dinkelbach method is applied to deal with the generalized fractional programming (FP) in the outer layer problem and semidefinite relaxation (SDR), successive convex approximation (SCA) as well as S-procedure methods are applied to transfer the inner layer problem into a convex iterative program. Moreover, we propose a suboptimal solution to the formulated problem and analyze the computational complexities. Finally, numerical results are provided to demonstrate the effectiveness of the proposed algorithms and reveal a tradeoff region between SEE and EHE. INDEX TERMS Full duplex, simultaneous wireless information and power transfer, physical layer security, energy efficiency, multi-objective optimization.

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Section: A Related Work and Motivationmentioning

confidence: 99%

Section: A Related Work and Motivationmentioning

confidence: 99%

Multi-Objective Optimization for Full-Duplex SWIPT Systems

Tao

et al. 2020

IEEE Access

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“…Therefore, to deal with the non-convexity of (21), an SCA based algorithm is proposed to approximate the nonconvex part via its appropriate convex bound [42], [43]. Define F 4 (X ) = Tr(X )−λ max (X ), its convex upper bound F 4 (X , X (n) ) can be given as [45]- [47], i.e.,…”

Section: A Successive Convex Approximation Based Algorithmmentioning

confidence: 99%

“…which is still difficult to solve because of the rank-1 constraint. By expressing the rank-1 constraint as Tr(X ) − λ max (X ) ≤ ς [46], [47], we can transform problem (42) to…”

Section: A Successive Convex Approximation Based Algorithmmentioning

confidence: 99%

MIMO Radar Waveform Optimization in Clutter Under General Power Constraints

Zhao

Wang

et al. 2020

IEEE Access

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In this paper, we investigate a novel framework on the waveform optimization for multipleinput multiple-output (MIMO) radar systems under assumptions of statistical information of scattering matrices and colored noise. Different from most of traditional designs under total power constraint, in our framework, a general power constraint named as multiple weighted power constraints is provided, which includes total power constraint, per-antenna power constraints as its special cases. In the framework, the transmit and receive correlations of parameter matrix to estimate and the spatial and time correlations of noise matrix are all taken into account. Moreover, the signal-dependent interference is also taken into account, which can be considered as a self-interference noise. Based on the available statistics of scattering matrices and colored noise, two kinds of estimation schemes are proposed in this paper, which can realize different trade-offs between complexity and performance. Finally, some numerical results are given to demonstrate the performance advantages of the proposed radar waveform designs. INDEX TERMS MIMO radar system, waveform design, signal-dependent interference, general power constraints, successive convex approximation (SCA), unitary matrix approximation (UMA).

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“…In this regard, the joint EE and SE maximization problem is formulated as a multiobjective optimization (MOO) problem, which tries to maximize the two conflicting objectives simultaneously subject to the partition of spectral resources among the CBS and DBSs, the minimum QoS requirements, and maximum input power constraint. The MOO problem is transformed into a single objective optimization (SOO) problem using the Weighted Tchebycheff method [10]. The transformed SOO problem can Figure 2: Proposed radio resource management procedure CDSA-based 5G networks be solved using standard interior point methods, such as LDD method, allowing us to obtain the Pareto optimal solution resulting in a complete Pareto-Frontier curve by dynamically adjusting the weights of both the objectives.…”

Section: Bsmentioning

confidence: 99%

A New Dimension to Spectrum Management in IoT Empowered 5G Networks

et al. 2019

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Paving the way for future 5G technologies requires a need to overcome the spectrum crunch, which is one of the major challenges impeding the growth of wireless technology. The issue at hand becomes more pronounced when we consider Internetof-Things (IoT), where billions of devices require connectivity. This article motivates the need for exploring new spectrum opportunities with reference to the requirements of IoT networks. Millimeter wave (mmWave) spectrum is considered as a panacea for overcoming the spectrum crunch, providing the much needed breathing space for introducing new applications that require higher rates. A network based on control/data separation architecture (CDSA) could further improve the performance by utilizing the mmWave-based data base stations (DBSs). The control base station (CBS) operates on the sub-6 GHz single band, while the DBS possesses a dual-band capability. This article presents a new dimension to spectrum heterogeneity by utilizing a dual-band approach at the DBS. One of the unique aspects of this work includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), Dynamic sub-carrier allocation (DSA) and Joint power and rate adaptation (JPRA) algorithms. The analysis is further expanded by showing an interplay between the utilization of licensed and unlicensed mmWave resources and how the dynamic spectrum management could help in their efficient utilization.

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Multi-Objective Optimization for Distributed MIMO Networks

Cited by 13 publications

References 37 publications

Multi-Objective Optimization for Full-Duplex SWIPT Systems

Multi-Objective Optimization for Full-Duplex SWIPT Systems

MIMO Radar Waveform Optimization in Clutter Under General Power Constraints

A New Dimension to Spectrum Management in IoT Empowered 5G Networks

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