Spectrum sharing-based multi-hop decode-and-forward relay networks under interference constraints: Performance analysis and relay position optimization

Bao, Vo Nguyen Quoc; Thanh, Trần Thiện; Nguyen, Tuan Due; Vu, Thanh Dinh

doi:10.1109/jcn.2013.000049

Cited by 39 publications

(24 citation statements)

References 36 publications

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“…It is obvious that the SEFF makes the exponential term in (38) becomes more complicated in derivation of the system performance. It is due to the need to make the integral of the product of (38) and the Q-function approximation over [0, ∞) integrable, as shown in (1). By this motivation, the work in [16] has approximated the Q-function by a polynomial.…”

Section: Polynomial-based Formmentioning

confidence: 99%

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A Survey on Approximations of One-Dimensional Gaussian Q-Function

Bao

Tuyen

Tue

2016

REV-JEC

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Abstract-Predicting the digital communication system performance plays a very important role in the process of system design. This performance is usually quantified by symbol error probability or bit error probability. Computing these probabilities in presence of Additive White Gaussian Noise requires to work with integrals involving the Gaussian Qfunction, which cannot be expressed in closed-form in terms of elementary functions. As a result, approximating the Gaussian Q-function in closed-form expressions with high accuracy becomes a necessity. In this paper, we give an overview about the Gaussian Q-function approximations and via some illustrating examples, we discuss their accuracy, tractability as well as their computational complexity.

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Section: Polynomial-based Formmentioning

confidence: 99%

“…The underlay approach is an efficient cognitive approach as compared with the overlay and interweave approach since it is able to provide concurrent transmission for un-licensed networks [36][37][38]. Here, we consider the underlay cognitive networks as shown in Figure 12.…”

Section: Average Bep Of Cognitive Underlay Networkmentioning

confidence: 99%

A Survey on Approximations of One-Dimensional Gaussian Q-Function

Bao

Tuyen

Tue

2016

REV-JEC

Self Cite

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“…In [12], the authors analyzed the performance of cognitive multihop decode-and-forward relay networks with Rayleigh fading channels. The performance of the cognitive underlay 2 International Journal of Distributed Sensor Networks…”

Section: Introductionmentioning

confidence: 99%

Wireless Energy Harvesting for Cognitive Multihop Wireless Sensor Networks

Nguyen

Shin

2015

International Journal of Distributed Sensor Networks

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This paper analyzes the performance of cognitive multihop transmissions with wireless energy harvesting in a wireless sensor network. Data transmission between the source and the destination nodes is assumed to occur entirely via several clusters of decodeand-forward relays. The source node and relay nodes are assumed to have the ability to harvest energy from the surrounding signals and to use that harvested energy to forward the information to the next hop. Specifically, we derive an exact closed-form expression to determine throughput and outage probability of the cognitive multihop transmission as a function of an energy harvesting overhead and the interference power constraint at the primary receiver. We assume perfect channel state information at the receivers in order to evaluate the throughput and the outage probability of the cognitive multihop system with wireless energy harvesting.

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“…In [11], the N -th best relay selection method is proposed. However, in spite of the potential of underlay DF cognitive networks, only few works have addressed the BER analysis of these systems [26][27][28][29][30]. Nevertheless, the works in [27][28][29][30] have not investigated the impact of relay selection, which will be shown to be a particularly cumbersome task, even in deriving an approximate BER expression.…”

mentioning

confidence: 99%

“…However, in spite of the potential of underlay DF cognitive networks, only few works have addressed the BER analysis of these systems [26][27][28][29][30]. Nevertheless, the works in [27][28][29][30] have not investigated the impact of relay selection, which will be shown to be a particularly cumbersome task, even in deriving an approximate BER expression. It is also noted that the work in [26] studies the effect of relay selection on the BER performance but with a simplified system model, where the relays are assumed geographically close, the source does not interfere with the PU and only interference power constraint is considered.…”

mentioning

confidence: 99%

Bit error rate of underlay decode-and-forward cognitive networks with best relay selection

et al. 2015

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This paper provides an analytic performance evaluation of the bit error rate (BER) of underlay decode-and-forward cognitive networks with best relay selection over Rayleigh multipath fading channels. A generalized BER expression valid for arbitrary operational parameters is firstly presented in the form of a single integral, which is then employed for determining the diversity order and coding gain for different best relay selection scenarios. Furthermore, a novel and highly accurate closed-form approximate BER expression is derived for the specific case where relays are located relatively close to each other. The presented results are rather convenient to handle both analytically and numerically, while they are shown to be in good agreement with results from respective computer simulations. In addition, it is shown that as in the case of conventional relaying networks, the behaviour of underlay relaying cognitive networks with best relay selection depends significantly on the number of involved relays.

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Spectrum sharing-based multi-hop decode-and-forward relay networks under interference constraints: Performance analysis and relay position optimization

Cited by 39 publications

References 36 publications

A Survey on Approximations of One-Dimensional Gaussian Q-Function

A Survey on Approximations of One-Dimensional Gaussian Q-Function

Wireless Energy Harvesting for Cognitive Multihop Wireless Sensor Networks

Bit error rate of underlay decode-and-forward cognitive networks with best relay selection

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