An LLR-Based Cognitive Transmission Strategy for Higher Spectrum Reutilization

Haghighi, Kasra; Ström, Erik G.; Agrell, Erik

doi:10.1109/glocom.2011.6133865

Cited by 4 publications

(8 citation statements)

References 11 publications

(12 reference statements)

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“…In [28], the authors proposed a Markov model to obtain the channel holding time, which is the period that the SU uses the free band without PU signal interruption. In [29], a new scheme is proposed to reduce the spectrum consumption: channels are grouped in clusters, only one channel must to be sensed, and the other channels are estimated using the historical states and their correlation with the sensed channel in the same cluster. The Markov model is used to model the influence of historical states on the current state in which the combination of channel correlation and the Markov model reduces the number of channels which must be sensed to improve sensing performance [19].…”

Section: ) Markov Modelsmentioning

confidence: 99%

Techniques for dealing with uncertainty in cognitive radio networks

Salahdine

Kaabouch

Ghazi

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently. However, due to multipath fading, shadowing, or varying channel conditions, uncertainty affects the cognitive cycle processes, measurements, decisions, and actions. In the observing step, measurements (i.e., information) taken by the secondary users (SUs) are uncertain. In the next step, the SUs make decisions based on what has already been observed using their knowledge bases, which may have been impacted by the uncertainty, leading to wrong decisions. In the last step, uncertainty can affect the decision of the cognitive radio system, which sometimes can lead to the wrong action. Thus, the uncertainty propagation influences the cognitive radio performance. Therefore, mitigating the uncertainty in the cognitive cycle is a necessity. This paper provides a deep overview of techniques that handle uncertainty in cognitive radio networks.

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Section: ) Markov Modelsmentioning

confidence: 99%

Techniques for dealing with uncertainty in cognitive radio networks

Salahdine

Kaabouch

Ghazi

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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“…In this strategy there are two parameters to optimize: θ e and n. These two design parameters should be chosen to maximize η, subject to the condition ρ ≤ ρ max . Moreover, we know that UR and IR are increasing functions of θ e [26], [27]. Thus, to find optimum parameters for the baseline strategy, we first fix n and find the threshold such that ρ = ρ max through bisection search.…”

Section: F Baseline Strategymentioning

confidence: 99%

“…Interference will happen whenever the CR transmits at the same time as the PU. This interference is quantified by the interference ratio (IR) ρ, defined as [26], [27] ρ Pr{u k+1 = 1|q k+1 = 1},…”

Section: E Problem Statementmentioning

confidence: 99%

“…II-C, a CR strategy considers observations when sensing is allowed and decides about the next transmission. Utilization of the spectrum occurs whenever the CR transmits in a vacant time slot, and this is measured by the spectral utilization ratio (UR), defined as [26], [27] η Pr{u k+1 = 1|q k+1 = 0},…”

Section: E Problem Statementmentioning

confidence: 99%

“…Moreover, there exist works in the prediction of the future state of the PU [18], [23]- [25]. In our previous work, we have introduced an optimum causal strategy that utilizes this PU behavior [26], [27] with low complexity. This approach not only considers the PU transmission model but also takes the causality of observations into account and thus provides a better reutilization of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Sensing or Transmission: Causal Cognitive Radio Strategies with Censorship

Haghighi

Ström²,

Agrell³

2014

IEEE Trans. Wireless Commun.

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Abstract-This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received signal-to-noise ratios (SNRs). The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of −2dB.

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