arXiv subjects

Ido Nevat

Publications and source records attributed to Ido Nevat.

22 records · Page 2Linked to original sources

arXiv2011

Spectrum Sensing in Cooperative Cognitive Radio Networks with Partial CSI

We develop an efficient algorithm for cooperative spectrum sensing in a relay based cognitive radio network. We consider a stochastic model where data is sent from the Base Station (BS) of the Primary User (PU). The data is relayed by the Secondary Users (SU) to the SU BS. The SU BS has only partial CSI knowledge of the wireless channels. In order to obtain the optimal decision rule based on Likelihood Ratio Test (LRT), the marginal likelihood under each hypothesis needs to be evaluated pointwise. These, however, cannot be obtained analytically due to the intractability of the integrals. Instead, we approximate these quantities by utilising the Laplace method. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve superior results to the energy detection scheme.

cs.IT↗

arXiv2011

Blind Spectrum Sensing in Cognitive Radio over Fading Channels and Frequency Offsets

This paper deals with the challenging problem of spectrum sensing in cognitive radio. We consider a stochastic system model where the the Primary User (PU) transmits a periodic signal over fading channels. The effect of frequency offsets due to oscillator mismatch, and Doppler offset is studied. We show that for this case the Likelihood Ratio Test (LRT) cannot be evaluated poitnwise. We present a novel approach to approximate the marginilisation of the frequency offset using a single point estimate. This is obtained via a low complexity Constrained Adaptive Notch Filter (CANF) to estimate the frequency offset. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve the same performance as the near-optimal scheme, that is based on a bank of matched filters, using only a fraction of the complexity required.

cs.IT↗

arXiv2010

Channel Tracking for Relay Networks via Adaptive Particle MCMC

This paper presents a new approach for channel tracking and parameter estimation in cooperative wireless relay networks. We consider a system with multiple relay nodes operating under an amplify and forward relay function. We develop a novel algorithm to efficiently solve the challenging problem of joint channel tracking and parameters estimation of the Jakes' system model within a mobile wireless relay network. This is based on \textit{particle Markov chain Monte Carlo} (PMCMC) method. In particular, it first involves developing a Bayesian state space model, then estimating the associated high dimensional posterior using an adaptive Markov chain Monte Carlo (MCMC) sampler relying on a proposal built using a Rao-Blackwellised Sequential Monte Carlo (SMC) filter.

cs.IT↗

arXiv2010

Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference

This paper presents a general stochastic model developed for a class of cooperative wireless relay networks, in which imperfect knowledge of the channel state information at the destination node is assumed. The framework incorporates multiple relay nodes operating under general known non-linear processing functions. When a non-linear relay function is considered, the likelihood function is generally intractable resulting in the maximum likelihood and the maximum a posteriori detectors not admitting closed form solutions. We illustrate our methodology to overcome this intractability under the example of a popular optimal non-linear relay function choice and demonstrate how our algorithms are capable of solving the previously intractable detection problem. Overcoming this intractability involves development of specialised Bayesian models. We develop three novel algorithms to perform detection for this Bayesian model, these include a Markov chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol error rate (SER) performance versus signal to noise ratio (SNR) of the three detection algorithms are studied in simulated examples.

stat.AP↗