Performance analysis of multi-hop framed ALOHA systems with virtual antenna arrays

We consider a multi-hop virtual multiple-input-multiple-output system, which uses the framed ALOHA technique to select the radio resource at each hop. In this scenario, the source, destination and relaying nodes cooperate with neighboring devices to exploit spatial diversity by means of the concept of virtual antenna array. We investigate both the optimum number of slots per frame in the slotted structure and once the source-destination distance is fixed, the impact of the number of hops on the system performance. A comparison with deterministic, centralized re-use strategies is also presented. Outage probability, average throughput, and energy efficiency are the metrics used to evaluate the performance. Two approximated mathematical expressions are given for the outage probability, which represent lower bounds for the exact metric derived in the paper

Stochastic maximum likelihood methods for semi-blind channel equalization

In this paper, a blind stochastic maximum likelihood channel equalization algorithm is adapted to incorporate a known training sequence as part of the transmitted frame. A Hidden Markov Model formulation of the problem is introduced and the Baum-Welch algorithm is modified to provide a computationally efficient solution to the resulting optimization problem. The proposed method provides a unified framework for semi-blind channel estimation, which exploits information from both the training and the blind part of the received data record. The performance of the maximum likelihood estimator is studied, based on the evaluation of Cramer-Rao bounds. Finally, some simulation results are presented

Stochastic maximum likelihood methods for semi-blind channel estimation

In this letter, a blind stochastic maximum likelihood (ML) channel estimation algorithm is adapted to incorporate a known training sequence as part of the transmitted frame, A hidden Markov model (HMM) formulation of the problem is introduced, and the Baum-Welch algorithm is modified to provide a computationally efficient solution to the resulting optimization problem, The proposed method provides a unified framework for semiblind channel estimation, which exploits information from both the training and the blind part of the received data record, The performance of the ML estimator is studied, based on the evaluation of Cramer-Rao bounds (CRB's), Finally, some preliminary simulation results are presented

Maximum likelihood blind channel estimation in the presence of Doppler shifts

Transmitter/receiver motion in mobile radio channels may cause frequency shifts in each received path due to Doppler effects. Most blind equalization methods, however, assume time-invariant channels and may not be applicable to fading channels with severe Doppler spread. In this paper, we address the problem of simultaneously estimating the Doppler shift and channel parameters in a blind setup. Both deterministic and stochastic maximum likelihood methods are developed and iterative solutions proposed. The stochastic maximum likelihood solution is based on the modified version of the Baum-Welch algorithm, which originated in the study of hidden Markov models. The proposed methods are well suited for short data records appearing in TDMA systems. Identifiability and performance analysis issues are discussed, and Cramer-Rao bounds are derived. In addition, some illustrative simulations are presented

Maximum-likelihood estimation of FIR channels excited by convolutionally encoded inputs

If error correcting coding is present in the information symbols, the channel estimation procedure may be further complicated, since the encoder introduces a nonlinear operation on the information symbols (in the field of reals). Moreover, because of the encoder's memory, the input to the channel may not be i.i.d. Therefore classical blind channel equalization methods may not be suitable for systems with coding, In this letter, a blind stochastic maximum-likelihood channel estimation algorithm is proposed for convolutionally coded signals transmitted through a multipath channel. The performance of the estimator is explored, based on the evaluation of approximate Cramer-Rao bounds. The CRB's are used in turn to obtain approximate expressions for the probability of error. Finally, some illustrative simulations are presented

Frequency-Shift Zero-Forcing Time-Varying Equalization for Doubly Selective SIMO Channels

<p/> <p>This paper deals with the problem of designing linear time-varying (LTV) finite-impulse response zero-forcing (ZF) equalizers for time- and frequency-selective (so-called doubly selective) single-input multiple-output (SIMO) channels. Specifically, relying on a basis expansion model (BEM) of the rapidly time-varying channel impulse response, we derive the canonical frequency-domain representation of the minimal norm LTV-ZF equalizer, which allows one to implement it as a parallel bank of linear time-invariant filters having, as input signals, different frequency-shift (FRESH) versions of the received data. Moreover, on the basis of this FRESH representation, we propose a simple and effective low-complexity version of the minimal norm LTV-ZF equalizer and we discuss the relationships between the devised FRESH equalizers and a LTV-ZF equalizer recently proposed in the literature. The performance analysis, carried out by means of computer simulations, shows that the proposed FRESH-LTV-ZF equalizers significantly outperform their competitive alternative.</p