A novel approach to adaptive transmit multi-beamforming is developed for frequency division duplex (FDD) multiple-input multiple-output (MIMO) systems. The method adapts each beam by computing at the receiver the steepest-ascent adaptation direction and feeding the quantized direction vector back to the transmitter; we call it the quantized-feedback optimal adaptive multi-beamforming algorithm. The method is widely flexible in the number of quantization bits and offers an adaptive diversity technique that allows for transmission of multiple data streams on orthogonal beams, thereby substantially increasing the system capacity or providing a new radio link for a new service. The convergence speed is maximized by computing the optimal increment vector which points in the direction of largest directional derivative of the objective function. We show that the algorithm converges globally irrespective of quantization error: the quantized increment vector always provides positive directional derivative at any non-stationary point in the solution space containing no local maxima. Simulation results are presented to illustrate the performance of the method.

A novel approach to adaptive transmit multi-beam forming is developed for multiple-input multiple output (MIMO) systems. Since the method adapts the transmit beams by computing and quantizing the optimal direction of adaptation for each beam, we call it the quantized-feedback optimal adaptive multi-beam forming (QFOA-MUB) algorithm. The number of feedback bits for each beam is arbitrary: it can 1 bit, or as large as affordable. This algorithm allows transmission in orthogonal beam spaces, therefore substantially increasing the system capacity. Since the increment vector points in the direction that yields the largest directional derivative, the convergence speed is maximized. We show analytically that the algorithm has global convergence, irrespective of quantization error.

A new approach to adaptive beamforming is developed for frequency division duplex (FDD) systems. Since the method adapts the transmit beamforming vector by computing the optimal direction of adaptation and feeds its quantized version back to the transmitter, we call it the quantized-feedback optimal adaptive (QFOA) beamforming algorithm. This algorithm offers a very fast-convergence and highly adaptive diversity technique. Fast convergence improves the system performance during the start-up period and helps remove the need to continuously tracking the antenna weights during intermittent periods where the transmitter does not transmit to a particular mobile user, or while the connection is temporarily suspended or idle. High convergence speed also provides a fast tracking capability demanded by high mobility applications. We also propose several quantization methods and prove analytically that they guarantee global convergence, irrespective of quantization error, since the gradient is always positive at any nonstationary point in the solution space.

A blind maximum-likelihood equalization algorithm is described and its convergence behavior is analyzed. Since the algorithm employs the Viterbi algorithm (VA) to execute the expectation step of the expectation-maximization (EM) iteration, we call it the expectation-maximization Viterbi algorithm (EMVA). An EMVA-based blind channel-acquisition technique which achieves a high global convergence probability is developed. The performance of the method is evaluated via numerical simulations under static and fading channel conditions.

We propose a blind maximum-likelihood (ML) equalization method for synchronous and asynchronous spread-spectrum code-division multiple access (CDMA) signals in intersymbol interference channels. Our method employs an expectation-maximization Viterbi algorithm (EMVA) capable of joint channel identification and signal estimation for transmission systems that admit a hidden Markov model description. By modelling the observed signal as a finite-state hidden Markov process, it is possible to use the Viterbi algorithm (VA) to execute the M-phase of the expectation-maximization iteration. When the parameter estimate converges to a stationary point, the best survivor of the VA is selected as the ML estimate of the transmitted sequence. Simulations show that the EMVA performs almost as well as the ML detector based on exact channel knowledge and that it is near-far resistant in the presence of large power disparities among users. We then devise an EMVA-based quasi-maximum-likelihood successive cancellation (QML-SC) method which reduces the trellis complexity by first ignoring weak users, equalizing the stronger users, subtracting off their contributions from the observed signal, equalizing and cancelling the weak users, and repeating the procedure until convergence. Though not near-far resistant, the QML-SC method tends to resist the near-far effect for a wide range of power disparities.

We propose a blind maximum-likelihood (ML) detection algorithm for continuous-phase modulated (CPM) signals transmitted over a noisy linear FIR channel. This is referred to as the Expectation-Maximization-Viterbi algorithm (EMVA) [1, 2]. The EMVA is a blind algorithm capable of simultaneously performing system identification and signal estimation whenever the transmission system can be modeled as a finite-state machine with unknown parameters, a scenario frequently encountered in signal processing for communications. We specifically focus on CPM, but t he algorithm is equally applicable to any other modulation type, linear or nonlinear. The channel estimate obtained via the EMVA is shown via simulations to, asymptotically (as the SNR increases) attain the most optimistic Cramér-Rao bo und and to have an error performance close to that of the true channel; a 0.5 dB difference is seen at BER equal to 10−4.