Wireless Information and Energy Transfer for Two-Hop Non-Regenerative MIMO-OFDM Relay Networks

The developments in wireless sensor network (WSN) that enriches with the unique capabilities of cognitive radio technique are giving impetus to the evolution of Cognitive Wireless Sensor Network (CWSN). In a CWSN, wireless sensor nodes can opportunistically transmit on vacant licensed frequencies and operate under a strict interference avoidance policy with the other licensed users. However, typical constraints of energy conservation from batterydriven design, local spectrum availability, reachability with other sensor nodes, and large-scale network architecture with complex topology are factors that maintain an acceptable network performance in the design of CWSN. In addition, the distributed nature of sensor networks also forces each sensor node to act cooperatively for a goal of maximizing the performance of overall network. The desirable features of CWSN make Multi-agent Reinforcement Learning (RL) technique an attractive choice. In this paper, we propose a reinforcement learning-based transmission power and spectrum selection scheme that allows individual sensors to adapt and learn from their past choices and those of their neighbors. Our proposed scheme is multi-agent distributed and is adaptive to both the end-to-end source to sink data requirements and the level of residual energy contained within the sensors in the network. Results show significant improvement in network lifetime when compared with greedy-based resource allocation schemes.

Section: Related Workmentioning

confidence: 99%

Energy-efficiency opportunistic spectrum allocation in cognitive wireless sensor network

Wang

Yin

2018

“…In [22], the authors investigated simultaneous wireless information and power transfer for non-regenerative multiple input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relaying systems and proposed two protocols to maximize the throughput. In [23], an energy-efficient resource allocation scheme was proposed for an OFDM based full-duplex distributed antenna system with energy-harvesting capability.…”

Section: Related Workmentioning

confidence: 99%

“…In [27], the capacity region of a multiple access channel with energy-harvesting transmitters and energy cooperation was derived. Note that, as the works in [22][23][24][25][26][27] are not for CR, providing data traffic offloading service to the primary users is not of concern.…”

Section: Related Workmentioning

confidence: 99%

Offloading data traffic via cognitive small cells with wireless powered user equipments

2017

This paper investigates data traffic offloading by considering a third-party cognitive small cell with wireless powered user equipments (UEs) providing data traffic offloading service to a primary macrocell. The cognitive small cell is assumed to use remaining resources for its own purpose provided that the quality of service (QoS) of the primary macrocell is satisfied. It is assumed that the small cell UEs (SUEs) are wirelessly powered and can harvest energy from the RF signals transmitted by the macrocell UEs (MUEs) as well as the RF signals transmitted by the small cell BS (SBS). Under the assumption that the successive interference cancellation (SIC) decoder is available or not available at the SBS, iterative optimization-based data traffic offloading schemes are proposed to maximize the SUE sum rate provided that the required minimum MUE sum rate is satisfied. It is shown that the proposed data traffic offloading schemes are effective in improving the performance of the MUEs and providing transmission opportunities for the wireless powered SUEs.

“…Following these pioneering works, plenty of works have been done for various wireless networks, including cooperative relaying network [12][13][14][15][16], power allocation strategies [17][18][19], resource scheduling [20,21], and multiple-input multiple-out (MIMO) system [22][23][24] or multiple-input single-output (MISO) [25,26]. In [12], the authors analyzed the system maximal throughput for the time switching and power splitting protocols in AF relaying networks.…”

Section: Introductionmentioning

confidence: 99%

“…In [20], the author proposed a greedy clustering algorithms to reduce the hardware cost of the PS scheme. In [22], a non-regenerative MIMO orthogonal frequency-division multiplexing (OFDM) relaying system was investigated, and the maximal achievable information rates of two protocols, time swithing-based relaying and power splitting-based relaying, were explored. In [23], the authors focused on quality-of-service-constrained energy efficient optimization in MIMO SWIPT systems via joint antenna selection and spatial switching.…”

Section: Introductionmentioning

confidence: 99%

Outage probability of power splitting SWIPT two-way relay networks in Nakagami-m fading

Huang

2018

This paper investigates the outage probability of an energy harvesting (EH) relay-aided cooperative network, where a source node transmits information to its destination node with the help of an energy harvesting cooperative node. For such a system, we derive an explicit closed-form expression of outage probability over Nakagami-m fading channels for both amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, and we verify the explicit closed-form expressions of outage probability with the Monte Carlo method. It is shown that the simulation results match well with the numerical ones. From the numerical analysis and simulation results, it can be observed that the system parameters have great impact on both AF and DF relay systems. For the DF system, with the increment of the power splitting ratio, the system outage probability decreases, while for the AF system, with the increment of the power splitting ratio, it first increases and then decreases. Besides, for both DF and AF systems, when the relay is placed relatively closer to the source, better outage performance will be achieved.