Limiting behavior of receive antennae selection

Abstract-Wireless energy harvesting is regarded as a promising energy supply alternative for energy-constrained wireless networks. In this paper, a new wireless energy harvesting protocol is proposed for an underlay cognitive relay network with multiple primary user (PU) transceivers. In this protocol, the secondary nodes can harvest energy from the primary network (PN) while sharing the licensed spectrum of the PN. In order to assess the impact of different system parameters on the proposed network, we first derive an exact expression for the outage probability for the secondary network (SN) subject to three important power constraints: 1) the maximum transmit power at the secondary source (SS) and at the secondary relay (SR), 2) the peak interference power permitted at each PU receiver, and 3) the interference power from each PU transmitter to the SR and to the secondary destination (SD). To obtain practical design insights into the impact of different parameters on successful data transmission of the SN, we derive throughput expressions for both the delay-sensitive and the delay-tolerant transmission modes. We also derive asymptotic closed-form expressions for the outage probability and the delay-sensitive throughput and an asymptotic analytical expression for the delay-tolerant throughput as the number of PU transceivers goes to infinity. The results show that the outage probability improves when PU transmitters are located near SS and sufficiently far from SR and SD. Our results also show that when the number of PU transmitters is large, the detrimental effect of interference from PU transmitters outweighs the benefits of energy harvested from the PU transmitters.Index Terms-Cognitive relay network, energy harvesting, multiple primary user transceivers.

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“…The outage probability can be evaluated using the results obtained from (34), (35), (37), and (41) in (12).…”

Section: Appendix Amentioning

confidence: 99%

Wireless Energy Harvesting in a Cognitive Relay Network

Liu

Mousavifar

Deng

et al. 2016

IEEE Trans. Wireless Commun.

163

120

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“…The performance of the secondary link is affected by the number of PUs via the interference and the constraint on the transmit power of SBS. As we can see from (16), when K grows large, the power constraint effect causes a significant degradation on the secondary system performance. In this case, SBS cannot be permitted to share the spectrum and transmit the signal to SU.…”

Section: A Effects Of Massive Mimo At Primary Systems On Secondary Nmentioning

confidence: 97%

“…exponential RVs, the distribution of Z 1 is asymptotically normal, as K → ∞. More precisely, from [16,Proposition 1], as K → ∞, we have…”

Section: A Effects Of Massive Mimo At Primary Systems On Secondary Nmentioning

confidence: 99%

Massive MIMO in Spectrum Sharing Networks: Achievable Rate and Power Efficiency

Wang

Ngo

Elkashlan

et al. 2017

IEEE Systems Journal

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Massive multiple-input multiple-output (MIMO) is one of the key technologies for 5G and can substantially improve energy and spectrum efficiencies. This paper explores the potential benefits of massive MIMO in spectrum sharing networks. We consider a multiuser MIMO primary network with NP-antenna primary base station (PBS) and K single-antenna primary users (PUs), and a multiple-input single-output (MISO) secondary network with NS-antenna secondary base station (SBS) and a single-antenna secondary user. Using the proposed model, we derive a tight closed-form expression for the lower bound on the average achievable rate, which is applicable to arbitrary system parameters. By performing large-system analysis, we examine the impact of large number of PBS antennas and large number of PUs on the secondary network. It is shown that when NP and K grow large, NS must be proportional to ln K or larger, to enable successful secondary transmission. In addition, we examine the impact of imperfect channel state information on the secondary network. It is shown that the detrimental effect of channel estimation errors is significantly mitigated as NS grows large.

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“…Recently, the idea of antenna selection has also been analyzed in the massive MIMO context. For instance, the distribution of the mutual information between the transmit and/or receive antennas has been derived for a point‐to‐point massive MIMO communication system using transmit antenna selection in the works of Li et al and Hesami and Laneman . However, there is a lack of comprehensive analysis for the effects of antenna selection in the multiuser (MU) case, which has the greatest potential to be used in the next generation of wireless systems .…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the distribution of the mutual information between the transmit and/or receive antennas has been derived for a point-to-point massive MIMO communication system using transmit antenna selection in the works of Li et al 5 and Hesami and Laneman. 17 However, there is a lack of comprehensive analysis for the effects of antenna selection in the multiuser (MU) case, which has the greatest potential to be used in the next generation of wireless systems. 2,18 As it has been depicted by Li et al, 5 the big challenge lies in finding an exact relation for the mutual information with antenna selection in the MU case.…”

mentioning

confidence: 99%

Employing antenna selection to improve energy efficiency in massive MIMO systems

Arash

Yazdian

Fazel

et al. 2017

Trans Emerging Tel Tech

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Massive MIMO systems promise high data rates by employing large number of antennas, which also increases the power usage of the system as a consequence. This creates an optimization problem which specifies how many antennas the system should employ in order to operate with maximal energy efficiency. Our main goal is to consider a base station with a fixed number of antennas, such that the system can operate with a smaller subset of antennas according to the number of active user terminals, which may vary over time. Thus, in this paper we propose an antenna selection algorithm which selects the best antennas according to the better channel conditions with respect to the users, aiming at improving the overall energy efficiency. Then, due to the complexity of the mathematical formulation, a tight approximation for the consumed power is presented, using the Wishart theorem, and it is used to find a deterministic formulation for the energy efficiency. Simulation results show that the approximation is quite tight and that there is significant improvement in terms of energy efficiency when antenna selection is employed.

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Limiting behavior of receive antennae selection

Cited by 15 publications

References 17 publications

Wireless Energy Harvesting in a Cognitive Relay Network

Wireless Energy Harvesting in a Cognitive Relay Network

Massive MIMO in Spectrum Sharing Networks: Achievable Rate and Power Efficiency

Employing antenna selection to improve energy efficiency in massive MIMO systems

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