Feedforward data-aided phase noise estimation from a DCT basis expansion

This contribution deals with phase noise estimation from pilot symbols. The phase noise process is approximated by an expansion of discrete cosine transform (DCT) basis functions containing only a few terms. We propose a feedforward algorithm that estimates the DCT coefficients without requiring detailed knowledge about the phase noise statistics. We demonstrate that the resulting (linearized) mean-square phase estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise independent contribution, that results front the phase noise modeling error. We investigate the effect of the symbol sequence length, the pilot symbol positions, the number of pilot symbols, and the number of estimated DCT coefficients it the estimation accuracy and on the corresponding bit error rate (PER). We propose a pilot symbol configuration allowing to estimate any number of DCT coefficients not exceeding the number of pilot Symbols, providing a considerable Performance improvement as compared to other pilot symbol configurations. For large block sizes, the DCT-based estimation algorithm substantially outperforms algorithms that estimate only the time-average or the linear trend of the carrier phase. Copyright (C) 2009 J. Bhatti and M. Moeneclaey

State Amplification Subject To Masking Constraints

This paper considers a state dependent broadcast channel with one transmitter, Alice, and two receivers, Bob and Eve. The problem is to effectively convey ("amplify") the channel state sequence to Bob while "masking" it from Eve. The extent to which the state sequence cannot be masked from Eve is referred to as leakage. This can be viewed as a secrecy problem, where we desire that the channel state itself be minimally leaked to Eve while being communicated to Bob. The paper is aimed at characterizing the trade-off region between amplification and leakage rates for such a system. An achievable coding scheme is presented, wherein the transmitter transmits a partial state information over the channel to facilitate the amplification process. For the case when Bob observes a stronger signal than Eve, the achievable coding scheme is enhanced with secure refinement. Outer bounds on the trade-off region are also derived, and used in characterizing some special case results. In particular, the optimal amplification-leakage rate difference, called as differential amplification capacity, is characterized for the reversely degraded discrete memoryless channel, the degraded binary, and the degraded Gaussian channels. In addition, for the degraded Gaussian model, the extremal corner points of the trade-off region are characterized, and the gap between the outer bound and achievable rate-regions is shown to be less than half a bit for a wide set of channel parameters.Comment: Revised versio

Spiking Neural Networks -- Part III: Neuromorphic Communications

Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of more and more wirelessly connected devices, each with its own data, is driving efforts to export advances in machine learning (ML) from high performance computing facilities, where information is stored and processed in a single location, to distributed, privacy-minded, processing at the end user. On the other hand, ML can address algorithm and model deficits in the optimization of communication protocols. However, implementing ML models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems. First, we discuss federated learning for the distributed training of SNNs, and then describe the integration of neuromorphic sensing, SNNs, and impulse radio technologies for low-power remote inference.Comment: Submitte

Experimental study of cognitive radio test-bed using USRP

Cognitive Radio is an emerging technology that enables efficient utilization of the spectrum. As such, it has created great interests in industrial and research fields. Many people have proposed test-bed models for the performance analysis of primary and secondary users in a real-time noise environment. However, these test-beds are generally lacking in their range of capabilities as well as accurate implementation of the proposed models. In this thesis, we develop our test-bed on USRP to achieve the spectrum sensing and co-existence of primary and secondary users, while implementing the rendezvous protocols for secondary traffic coordination. We first demonstrate the spectrum sensing on the primary users using an energy detector(Average periodogram analysis) to obtain the average power of the primary channel under two different channel conditions (busy or idle). The focus is extended on developing the Markov traffic model and the Coded OFDM transceivers, while discussing the practical limitations for Markov traffic and viable solutions for reducing the burst errors for Coded OFDM. Finally, a four-node test-bed model of primary and secondary users is analyzed with the interference metrics (packet loss and error rate) for different scenarios. Also, the throughput and the interference metrics are compared for different rendezvous protocols of the secondary users

ADAPTIVE CODING TECHNIQUES TO IMPROVE BER IN OFDM SYSTEM

Adaptive modulation and diversity combining represent very important adaptive solutions for the future generations of communication systems. In order to improve the performance and the efficiency of wireless communication systems these two techniques have been recently used jointly in new schemes named joint adaptive modulation and diversity combining .The highest spectral efficiency with the lowest possible combining complexity, given the fading channel conditions and the required error rate performance. Increase the spectral efficiency with a slight increase in the average number of combined path for the low signal to noise ratio (SNR) range while maintaining compliance with the bit error rate (BER)

Multi-Antenna OFDM System Using Coded Wavelet with Weighted Beamforming

yesA major drawback in deploying beamforming scheme in orthogonal frequency division multiplexing (OFDM) is to obtain the optimal weights that are associated with information beams. Two beam weighting methods, namely co-phasing and singular vector decomposition (SVD), are considered to maximize the signal beams for such beamforming scheme. Initially the system performance with and without interleaving is investigated using coded fast Fourier transform (FFT)-OFDM and wavelet-based OFDM. The two beamforming schemes are applied to the wavelet-based OFDM as confirmed to perform better than the FFT-OFDM. It is found that the beam-weight by SVD improves the performance of the system by about 2dB at the expense of the co-phasing method. The capacity performances of the weighting methods are also compared and discussed