Channel estimation and tracking for closed loop EO-STBC with differentially encoding feedback

Extended orthogonal space time block coding (EO-STBC) can achieve high transmit diversity over a multiple-input multiple-output (MIMO) channel. To do so, it requires channel state information on the transmitter side, which needs to be estimated and fed back from the receiver. Therefore, this paper explores an estimation and tracking scheme by means of a Kalman filter, which is integrated with EO-STBC detection and exploits the smooth evolution of the channel coefficients by applying differential feedback. For slow fading, we propose the inclusion of a drift vector in the Kalman model, which is motivated by a second order approximation of the underlying channel model and can be shown to offer advantages in terms of temporal smoothness when addressing channels whose coefficient trajectories evolve smoothly

Low-complexity blind maximum-likelihood detection for SIMO systems with general constellations

The demand for high data rate reliable communications poses great challenges to the next generation wireless systems in highly dynamic mobile environments. In this paper, we investigate the joint maximum-likelihood (ML) channel estimation and signal detection problem for single-input multiple-output (SIMO) wireless systems with general modulation constellations and propose an efficient sequential decoder for finding the exact joint ML solution. Unlike other known methods, the new decoder can even efficiently find the joint ML solution under high spectral efficiency non-constant- modulus modulation constellations. In particular, the new algorithm does not need such preprocessing steps as Cholesky or QR decomposition in the traditional sphere decoders for joint ML channel estimation and data detection. The elimination of such preprocessing not only reduces the number of floating point computations, but also will potentially lead to smaller size and power consumption in VLSI implementations while providing better numerical stability

Distributed closed-loop EO-STBC for a time-varying relay channel based on kalman tracking

This paper considers distributed closed-loop extended orthogonal space-time block coding (EO-STBC) for amplify-forward relaying over time-varying channels. In between periodically injected pilot symbols for training, the smooth variation of the fading channel coefficients is exploited by Kalman tracking. We show in this paper that the joint variation of both relay channels still motivates the use of a higher-order auto-regressive model for the a priori prediction step within a decision-feedback system, compared to a first-order standard Kalman model. Simulations results compare these two case and highlight the benefits of the proposed higher-order Kalman filter, which offer joint decoding and tracking

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Coded DS-CDMA Systems with Iterative Channel Estimation and no Pilot Symbols

In this paper, we describe direct-sequence code-division multiple-access (DS-CDMA) systems with quadriphase-shift keying in which channel estimation, coherent demodulation, and decoding are iteratively performed without the use of any training or pilot symbols. An expectation-maximization channel-estimation algorithm for the fading amplitude, phase, and the interference power spectral density (PSD) due to the combined interference and thermal noise is proposed for DS-CDMA systems with irregular repeat-accumulate codes. After initial estimates of the fading amplitude, phase, and interference PSD are obtained from the received symbols, subsequent values of these parameters are iteratively updated by using the soft feedback from the channel decoder. The updated estimates are combined with the received symbols and iteratively passed to the decoder. The elimination of pilot symbols simplifies the system design and allows either an enhanced information throughput, an improved bit error rate, or greater spectral efficiency. The interference-PSD estimation enables DS-CDMA systems to significantly suppress interference.Comment: To appear, IEEE Transactions on Wireless Communication