Multi-objective optimization for information-energy transfer trade-offs in full-duplex multi-user MIMO cognitive networks

Nguyen, Xuan-Xinh; Kha, Ha Hoang; Thái, Phạm Quang; Ta, Hung Quang

doi:10.1007/s11235-020-00696-4

Cited by 3 publications

(5 citation statements)

References 35 publications

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“…More recently, the multiobjective optimization (MOO) framework has been introduced as an effective mathematical tool to overcome this issue as well as to provide a balance among the system performance metrics on designing the wireless communication systems [37,38]. As a result, the MOO framework was recently exploited in many studies to address various trade-offs between two or more system metrics [39][40][41][42][43][44][45][46][47][48]. Particularly, the harvested energy and information transfer trade-offs in SWIPT-enabled wireless system were studied either in cognitive FD MU-MIMO systems [39] or in MIMO broadcast channels [40,41].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…As a result, the MOO framework was recently exploited in many studies to address various trade-offs between two or more system metrics [39][40][41][42][43][44][45][46][47][48]. Particularly, the harvested energy and information transfer trade-offs in SWIPT-enabled wireless system were studied either in cognitive FD MU-MIMO systems [39] or in MIMO broadcast channels [40,41]. In [42], the authors studied the optimal Pareto set for DL and UL transmit power fairness in the FD MISO networks.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…At the DLU, the received signal is divided into two proportions using power splitting (PS) receiver structures, which are then performed for ID and EH purposes. The total amount of harvested energy can be stored at the DLUs to prolong their lifetime or to utilize for signal processing operations as discussed in previous works [26,39,47,48]. In the UL channels, the individual-received baseband signals at RUs are sent to the CU via FH links for central processing.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Meanwhile, P DC k D denotes the maximum power harvested at DLU k D at the saturation point of EH circuit and a k D and b k D are the nonlinear EH parameters by hardware limitations as illustrated in [50]. This quantity of energy, E NL k D , can be stored either in a battery or in a super capacitor and later exploited to prolong its own operation such as processing/decoding signals or transmitting control signals to the RUs [26,39,47,48,50,51].…”

Section: System Modelmentioning

confidence: 99%

“…The value of e req k D is chosen properly providing that the harvested energy is useful and can activate the EH circuitry [50]. Hence, this value should be set up to be larger than or at least equal to the minimum sensitive level of its energy harvester [26,39,48].…”

Section: Power Consumptionmentioning

confidence: 99%

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Energy‐Spectral Efficiency Trade‐Offs in Full‐Duplex MU‐MIMO Cloud‐RANs with SWIPT

Nguyen

Kha

2021

Wireless Communications and Mobile Computing

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The present paper investigates the trade-offs between the energy efficiency (EE) and spectral efficiency (SE) in the full-duplex (FD) multiuser multi-input multioutput (MU-MIMO) cloud radio access networks (CRANs) with simultaneous wireless information and power transfer (SWIPT). In the considered network, the central unit (CU) intends to concurrently not only transfer both energy and information toward downlink (DL) users using power splitting structures but also receive signals from uplink (UL) users. This communication is executed via FD radio units (RUs) which are distributed nearby users and connected to the CU through limited capacity fronthaul (FH) links. In order to unveil interesting trade-offs between the EE and SE metrics, we first introduce three conventional single-objective optimization problems (SOOPs) including (i) system sum rate maximization, (ii) total power minimization, and (iii) fractional energy efficiency maximization. Then, by making use of the multiobjective optimization (MOO) framework, the MOO problem (MOOP) with the objective vector of the achievable rate and power consumption is addressed. All considered problems are nonconvex with respect to designing variables comprising precoding matrices, compression matrices, and DL power splitting factors; thus, it is extremely intractable to solve these problems directly. To overcome these issues, we develop iterative algorithms by utilizing the sequential convex approximation (SCA) approach for the first two SOO problems and the SCA-based Dinkelbach method for the fractional EE problem. Regarding the MOOP, we first rewrite it as an SOOP by applying the modified weighted Tchebycheff method and, then, propose the iterative algorithm-based SCA to find its optimal Pareto set. Various numerical simulations are conducted to study the system performance and appealing EE-SE trade-offs in the considered system.

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