Symbol Error Rate of Underlay Cognitive Relay Systems over Rayleigh Fading Channel

Van, Khuong Ho; Bao, Vo Nguyen Quoc

doi:10.1587/transcom.e95.b.1873

Cited by 9 publications

(13 citation statements)

References 13 publications

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“…Figure 5 illustrates the BER performance of underlay DF cognitive networks with best relay selection with respect to the number of relays and P = 8 dB. It is shown that the analytical and simulated results are in good agreement, which verifies the validity of the proposed expression in (28). Also, the results are reasonable since the BER reduces as modulation level decreases and as the number of relays increases.…”

Section: B Special Case: Closely Located Relaysmentioning

confidence: 54%

“…Based on this assumption, (6) can be re-expressed by the following simplified representation: Based on this, the pdf of γ e2e can be obtained by taking the first derivative of F γe2e (x), which yields (27). Therefore, by substituting (27) into (11), one obtains the closed form expression as (28), at the top of the next page, where σ = a (λ 1 + λ 2 ) /P and…”

Section: Special Case: Closely Located Relaysmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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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Section: B Special Case: Closely Located Relaysmentioning

confidence: 54%

Section: Special Case: Closely Located Relaysmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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

et al. 2015

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“…Meanwhile, the BER analysis shows the real -not a limit-system performance for a given target spectral efficiency (i.e., modulation level) and hence, is of greater practical importance. Nevertheless, in spite of the undoubted usefulness of underlay DF multihop cognitive networks, only limited investigations on the BER analysis of these systems have been reported in the open technical literature 2 [27][28][29]. In addition, the works in [27,28] do not investigate the maximum transmit power constraint on unlicensees, which is strictly required in cognitive radio−based networks.…”

Section: Introductionmentioning

confidence: 99%

On The Performance of Underlay Relay Cognitive Networks

Ho-Van

Bao

2013

REV-JEC

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Abstract-The bit error rate (BER) performance of underlay relay cognitive networks in the presence of Rayleigh fading is thoroughly analyzed in this paper. New exact and asymptotic analytic expressions under consideration of both interference power constraint and maximum transmit power constraint are derived in closed-form and are extensively corroborated by Monte-Carlo simulations. These expressions facilitate in evaluating effectively the network performance behaviour in key operation parameters as well as in optimizing system parameters. A multitude of analytical results expose that underlay relay cognitive networks experience the performance saturation phenomena while their performance considerably depends on the number of hops for the linear network model. Additionally, optimum relay position is significantly dependent of maximum transmit power, maximum interference power, and licensee location. Moreover, the appropriate order of locating unlicensees with different maximum transmit power levels can dramatically improve the network performance.

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Exact outage analysis of underlay cooperative cognitive networks with maximum transmit‐and‐interference power constraints and erroneous channel information

Ho-Van

2013

Trans. Emerging Tel. Tech.

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This paper presents exact analysis of interference probability and outage probability of underlay cooperative cognitive networks under quite general conditions such as imperfect channel information, maximum transmit-and-interference power constraints, correlation among received signal-to-noise ratios and non identically distributed fading channels. In addition, asymptotic outage analysis at either large maximum transmit power or large maximum interference power is proposed to have useful insights into performance limits of underlay cooperative cognitive networks. Analytical results reveal that underlay cooperative cognitive networks dramatically suffer outage saturation phenomenon, and saturation degree significantly depends on channel estimation quality. Moreover, the performance of both licenced network in terms of interference probability and unlicenced network in terms of outage probability is considerably deteriorated by channel estimation error. Furthermore, optimum relay position is dependent of several factors such as maximum transmit power, maximum interference power, licensee position and channel estimation quality.

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Symbol Error Rate of Underlay Cognitive Relay Systems over Rayleigh Fading Channel

Cited by 9 publications

References 13 publications

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

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

On The Performance of Underlay Relay Cognitive Networks

Exact outage analysis of underlay cooperative cognitive networks with maximum transmit‐and‐interference power constraints and erroneous channel information

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