Design of Optimal Non-Uniform Quantizer in Imperfect Noisy Reporting Channels for Collaborative Spectrum Sensing

Naderipour, Masoumeh; Taherpour, Amirhossein; Taherpour, Abbas; Gazor, Saeed

doi:10.1109/tvt.2020.3015219

Cited by 11 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under Assumption 1, the detection probability in Equation (25) can be calculated as Proof See Appendix A. □…”

Section: Assumptionmentioning

confidence: 99%

See 1 more Smart Citation

Quantisation‐based target detection for cooperative multiple input multiple output radar and communications with multiple stations

Wang

2022

IET Signal Processing

View full text Add to dashboard Cite

Target detection is investigated for the emerging integrated radar and communication (IRC) systems. Consider the case, where multiple radar and communication stations are physically separated, possibly widely spaced, while working in a cooperative way. To utilise all available information for a final decision making, the radar stations that receive signals send their local measurements to a fusion centre (FC) through a wireless channel, say the backhaul network. For the purpose of decreasing communication traffic, the local measurements are quantised and then transferred to the FC. Three strategies for the quantisation and fusion are considered, where for the first method, local sensors quantise their test statistics, which are linearly fused at the FC (quantising test statistics and linear combination fusion), for the second method, the test statistics are quantised at the local sensors and optimally fused at the FC (quantising test statistics and optimal fusion), and for the third method, the received signals are quantizsd at the local sensors and optimally fused at the FC (quantising received signals and optimal fusion). The detection probability for each method is derived for the cooperative IRC system and compared with that of the non‐cooperative counterpart. It is proved that there is a detection performance gain through cooperation between the radar and communication systems and the cooperative IRC system can achieve the same performance as the non‐cooperative system with a fewer number of quantisation bits. The correctness of theoretical analysis is verified by simulations.

show abstract

“…Under Assumption 1, the detection probability in Equation (25) can be calculated as Proof See Appendix A. □…”

Section: Assumptionmentioning

confidence: 99%

“…The antennas are located on two perpendicular bands, for example a crossing road, as illustrated in Figure 3. Suppose the cell under test is centred at (150, 130)m. The potential target moves with velocity (25,20)km/h. The frequency spread single Gaussian pulse signals are adopted for radar transmission, given by s m 0 i ðtÞ ¼…”

Section: Numerical Examplesmentioning

confidence: 99%

Quantisation‐based target detection for cooperative multiple input multiple output radar and communications with multiple stations

Wang

2022

IET Signal Processing

View full text Add to dashboard Cite

show abstract

“…It can be seen that the minimum non-outage probability of CUs decreases when ϵ or γ th F I G U R E 5 Comparison the fairness of Scenario 1, the robust proposed scheme in Wang et al, 32 hybrid MRT-ZF, ZF, MRT, and Wang et al 53 schemes when ϵ ¼ 0:05, σ θ ¼ 2 ∘ , M ¼ 6, γ th ¼ 3 dB, and d ¼ 25 m increases. This is a predictable fact; according to (15) when the uncertainty in the covariance estimations of the channels between DT k and each CUs (i.e., δ ki ) increases, then γ CU i decreases. Again, as maintained above, when σ θ increases, the spatial correlation between elements of the BS antenna array, which is depending on the spatial channel between the BS and each CUs reduces, and the interference from one link to other increases so the minimum non-outage probability of CUs decreases.…”

Section: ðP3:2þmentioning

confidence: 99%

“…Unfortunately, in reality, obtaining accurate environmental information is impossible. Although many extensive methods have been proposed to deal with uncertain environment information, this issue still remains as a problem in telecommunication systems 14–16 . In real world, the CSI available at the transmitters is exposed to errors arising from estimation errors, short channel coherence time, limited channel state feedback, and so forth, which leads to severe performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Max–min fair robust beamforming design for underlay device‐to‐device communications in cellular networks

Alibeigi

Taherpour

Rahmani

2021

Int J Communication

Self Cite

View full text Add to dashboard Cite

This paper investigates the robust downlink beamforming designs based on max-min fairness and base station (BS) power consumption constraint for device-to-device (D2D) communications underlaying cellular network, under the assumption of imperfect channel state information (CSI) at the BS. Our objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) or non-outage probability of users while guaranteeing that the consumed power at the BS is less than a threshold. In particular, three max-min fairness scenarios are considered. In the first scenario, the worst-case SINR of cellular users (CUs) is maximized where the D2D SINR is guaranteed to be above a specified predetermined threshold. In the second scenario, we extend the first scenario and maximized D2D SINR while maximizing CUs' SINR. In these two scenarios, it is assumed that the errors are upper bounded in their Frobenius norms. In the third scenario, the minimum non-outage probability of users is maximized, while a probabilistic model is considered for the uncertainty of channel covariance matrices. Although such optimization problems are not convex, the semidefinite relaxation (SDR) approach is used to obtain the optimal beamforming matrix, which always complies the rank-one constraint. Simulation results show that significant improvement in terms of minimum SINR of CUs, the minimum non-outage probability of CUs, and the probability of feasibility and the fairness index can be achieved with our proposed algorithms in comparison with other beamforming schemes. K E Y W O R D Sdevice-to-device communications, imperfect channel state information, max-min fairness, robust beamforming

show abstract

“…The EGC detector is also called sum-fusion detector in the technical literature [8]. Quantized soft combining has been proposed as a compromise between soft and hard fusion [7], [9]- [11]. In this case, the CRs report quantized measurements of the energy to the FC.…”

Section: Introductionmentioning

confidence: 99%

Online Detection and SNR Estimation in Cooperative Spectrum Sensing

Pérez

Vía

Vielva

et al. 2022

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Cooperative spectrum sensing has proved to be an effective method to improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based on the soft fusion of the energy measurements at the cognitive radios (CRs). In these systems, the likelihood ratio test (LRT) is the optimal detection rule, but the sufficient statistic depends on the local signal-to-noise ratio (SNR) at the CRs, which are unknown in most practical cases. Therefore, the detection problem becomes a composite hypothesis test. The generalized LRT is the most popular approach in those cases. Unfortunately, in mobile environments, its performance is well below the LRT because the local energies are measured under varying SNRs. In this work, we present a new algorithm that jointly estimates the instantaneous SNRs and detects the presence of primary signals. Due to its adaptive nature, the algorithm is well suited for mobile scenarios where the local SNRs are timevarying. Simulation results show that its detection performance is close to the LRT in realistic conditions.

show abstract

Design of Optimal Non-Uniform Quantizer in Imperfect Noisy Reporting Channels for Collaborative Spectrum Sensing

Cited by 11 publications

References 31 publications

Quantisation‐based target detection for cooperative multiple input multiple output radar and communications with multiple stations

Quantisation‐based target detection for cooperative multiple input multiple output radar and communications with multiple stations

Max–min fair robust beamforming design for underlay device‐to‐device communications in cellular networks

Online Detection and SNR Estimation in Cooperative Spectrum Sensing

Contact Info

Product

Resources

About