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A blind carrier phase synchronization algorithm is proposed for high-order M-ary quadrature amplitude modulation-orthogonal frequency division multiplexing (M-QAM-OFDM) systems, which has recovered residual frequency offset (RFO) in the presence of intercarrier interference (ICI). The studies have shown that the algorithm is superior to the conventional decision-directed (DD) scheme for high-order M-QAM-OFDM systems.

A cooperation strategy that adopts a peer-to-peer diffusion protocol is examined. Findings reveal that the proposed strategy is distributed, cooperative and responsive in real time to changes in the environment.

A diffusion recursive least-squares algorithm is presented for solving the problem of distributed estimation over adaptive networks. Findings reveal the stability and performance of the proposed algorithm.

A compensation scheme is proposed for the mitigation of the common phase error and intercarrier interference (ICI) caused by phase noise in orthogonal frequency division mutiplexing (OFDM)-based wireless communication systems. Results suggest the effectiveness of the scheme in mitigating the ICI caused by phase noise and improving the bit error rate (BER) of OFDM systems.

The development of an adaptive distributed strategy based on incremental techniques is discussed. The strategy is useful for addressing the problem of linear estimation in a cooperative manner.

Two-hop multisensor relay strategies are proposed that can achieve minimum mean-square-error (mse) performance subject to either local or global power constraints. Three general relay methods, including MSE strategy with no power constraints, relay schemes with local power constraints and relay schemes with global power constraints are presented.

A dynamic antenna scheduling strategy for downlink MIMO communications is developed, where a subset of the receive antennas at certain users is selectively disabled. The results have shown that increasing the number of antennas at some users can degrade the signal-to-leakage-plus-noise (SLNR) performance at other users.

The mean-square performance of a convex combination of two transversal filters is illustrated, where combination approaches provide an interesting way to improve adaptive filter performance. Using energy conservation relations, the results show a combination of least mean-square filters operating both in stationary and in nonstationary scenarios revealing that the universality of the scheme can be exploited to design filters with improved tracking performance.

Dynamic rate and power control algorithms for distributed wireless networks that also account for the congestion levels in a network with three schemes of algorithms adaptive scheme, quadratic control scheme and robust scheme is proposed. The algorithm for quadratic control and robust solutions are model-dependent and hence they reveal improved performance over the adaptive solution.

The effect of in-phase and quadrature-phase (IQ) imbalances on multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is studied, and a framework for combating such distortions through digital signal processing is developed. Simulation results show significant improvement in the achievable BER of the proposed MIMO receivers for space-time block-coded OFDM systems in the presence of IQ imbalances.

The effect of In-phase and Quadrature-phase (IQ) imbalances on orthogonal frequency division multiplexing (OFDM) receivers is studied, and system-level algorithms to compensate for the distortions are proposed. The algorithms include post-fast Fourier transform (FFT) least-squares, least mean squares (LMS) equalization and pre-FFT correction.

A unified treatment of the mean square error, tracking, and transient performances of a family of affine projection algorithms is discussed. The treatment relies on energy conservation arguments and does not restrict the regressors to specific models or to a Guassian distribution.

Low-complexity adaptive receivers for space-time block-coded (STBC) transmissions over frequency-selective fading channels are described and discussed.

An unified approach is proposed for transient analysis of adaptive filters with error nonlinearities

An approach is presented for transient analysis of adaptive filters with data normalization.

The tracking performance of adaptive filters operating in the presence of two sources of nonstationarities is investigated. The sources are carrier frequency offsets and random channel variations.

A receiver structure is described, for tracking and equalizing a multi-input multi-output (MIMO) frequency-selective fading channel. A Kalman filter is used to track the channel, which relies on a low-order autoregressive model to fit the true statistics of the channel variation.

It is shown that fast fixed-order recursive least squares (RLS) algorithms are not limited to tapped-delay-line data structures, as has been suggested in the literature. It is shown that for more general data structures, fast filters in both array and explicit forms can be derived.

Lattice structures for RLS Laguerre adaptive filtering are proposed, including a posteriori- and a priori-based filters, with error-feedback, array-based lattice filters, and normalized lattice filters. Simulations illustrate performance characteristics.

A new feedback approach to analysis of steady-state performance of fractionally spaced blind adaptive equalizers is suggested. It is for constant modulus algorithms (CMA). Bypassing working directly with the weight error covariance matrix is a major feature of the proposed approach. Approximate expressions for the steady-state mean-square error of several CM algorithms are derived, and comparison of various algorithms and simulation results given. CMSA2-2 is shown to be superior.

The authors examine the use of leakage-based adaptive filters to resolve the problem of drift. Topics include stability theories, adaptive algorithms, and bias problems.

The one-to-correspondence between the recursive least-squares (RLS) and Kalman variables are used to develop extended forms of the RLS algorithm. Two forms of the extended RLS algorithm are examined, one concerning system identification assuming a first-order Markov model and another on tracking a chirped sinusoid in additive noise. Results show that the extended RLS algorithm performs better than the least-mean-square algorithm.

The error bounds show the robustness behavior of instantaneous gradient adaptive algorithms in a step by step manner. The convergence analysis reveals specific conditions on data and noise sequences, which show that the weight vector converges with the true weight vector. The time domain analysis employs Cauchy-Schwarz inequality for vectors in a purely deterministic framework. The gradient approximations are actually min-max filters.

Time-domain feedback analysis for digital signal processing can use gradient-based adaptive schemes. This reveals the robustness and convergence performance of adaptive digital filters under disturbances and modeling uncertainties. The analysis is performed in a purely deterministic framework and extends the transfer function approach to a general system that is time-variant.

The least-man squares (LMS) adaptive algorithm is a minimax filter and is H(super infinity) optimal. The LMS adaptive algorithm reduces the maximum energy gain from the disturbances to the predicted errors while the normalized LMS algorithm reduces energy gain from the disturbances to the filtered errors. LMS algorithms are H(super infinity) filters and reduce exponential cost function and have optimum risk-sensitivity.

A displacement structure algorithm was extended to encompass time-variant matrices to propagate the Cholesky factor in matrices that revealed structured time-variations. A modular triangular array associated with processing elements was used to implement the algorithm. This algorithm, which generated a time-variant lattice form, was transformed into a time-variant Schur algorithm when certain parameters were considered.