This paper describes a feedback assisted stochastic gradient algorithm for transmission tracking of the dominant channel subspaces for multiple-input-multiple-output (MIMO) communications systems. Subspace tracking is introduced as a means of tracking multiple transmission weights, being the MIMO generalization of beam steering in the familiar multiple-input-single-output case. The subspace solution approximates that of water filling (WF) in some cases, without the complete rate/power allocation required by WE The gain of subspace tracking in low rank systems is demonstrated, particularly, in the case where the number of transmit antennas exceeds the number of receive antennas. Simulations of ergodic capacity show the utility of both subspace tracking in general and of the specific adaptation algorithm, and simulations of frame-error rates show the utility in a specific coding example.

In this paper, a simple algorithm for adaptation of the complex baseband weights of a transmit antenna array using feedback from the receiver is proposed and analyzed. The system utilizes stochastic gradient adaptation to maximize the power delivered to the receiver for a constrained transmission power, which provides both fading diversity and beam steering gain. Dual perturbed transmission weight vectors are time multiplexed onto the pilot signal, and the receiver generates feedback selecting the perturbed weight vector which delivers greater power. This feedback is used to provide weight adaptation at the transmitter, and this adaptation is shown to be an update by a coarse estimate of the gradient of the delivered power. The performance of the algorithm is analyzed in terms of convergence and tracking of an AR1 fading channel, with simulations confirming the analysis. Bit error rate (BER) simulations in a dynamic fading channel show that the algorithm outperforms previously proposed vector selection feedback, and in slower fading, the algorithm substantially outperforms diversity space time coding.

Future time-division-duplex (TDD) systems operating over small wireless networks will utilize intelligent base station (BS)-coordinated dynamic channel-allocation algorithms in order to support high-bandwidth asymmetric traffic in adjacent cells. In this paper, we use extensive measurements of wireless Internet traffic from a large 802.11b network to create two random traffic models. One model, called “binomial,” is memoryless and the other, called “dynamic,” is based on an event-driven Markov state model with bidirectional flows and deterministic residence times. We then develop a two-BS two-zone wireless TDD interference model that describes the spatial features of interference between cochannel mobile stations (MSs) in adjacent BSs. This is a simplified precursor to more sophisticated models for multiple BSs and/or multisector BSs. We present a set of candidate TDD channel-allocation algorithms, which vary in their level of time-slot coordination and intelligent allocation between BSs. Lastly, we combine the three components (i.e., traffic models, interference models, and channel-allocation algorithms) to demonstrate the capacity for evaluating dynamic channel-allocation algorithms in realistic interference and Internet traffic scenarios. The results show that, for active MSs, the dynamic traffic model has a higher number of packet requests per time frame than the binomial traffic model, given the same mobile activity factor. Additionally, fixed channel-allocation algorithms generally perform much worse than pseudorandom and intelligent BS-coordinated algorithms, especially for asymmetric BSs. The pseudorandom algorithm performs well at low traffic, but suffers from severe interference blocking at high traffic. The intelligent BS-coordinated algorithm performs best, as it avoids MS-to-MS interference blocking from nearby users in adjacent cells and maximizes the overall throughput by attempting to allocate up- and downlink packet requests in corresponding time slots matched to the incoming uplink–downlink traffic demand for each time frame.

The statistical gain differences between two common spatial combining algorithms: optimum combining (OC) and maximal ratio combining (MRC) are analyzed using a gain ratio method. Using the receive carrier-to-interference plus noise ratio (CINR), the gain ratio CINROC/CINRMRC is evaluated in a flat Rayeligh fading communications system with multiple interferers. Exact analytical solutions are derived for the probability density function (PDF) and the average gain ratio with one interferer. When more than one interferer is present, the PDF of the gain ratio is illustrated using Monte Carlo simulations and its mean value is shown in basic integral form. An upper bound to the gain ratio is derived providing a simple means to determine when OC will exhibit significant gains over MRC.

This paper analyzes the performance of systems with dual-polarized antennas in correlated Nakagami-m fading channels as a function of envelope correlation and cross-polarization discrimination by means of the characteristic function of the instantaneous post-maximal ratio combining (MRC) signal-to-noise ratio (SNR). Systems of interest include systems with receive polarization diversity and systems with transmit and receive polarization diversity employing Alamouti space-time code. The expressions for the average symbol error probability as a function of SNR assuming no power control, and the expressions for the average required transmit power to achieve the constant desired post-MRC SNR assuming perfect fast power control, are derived. Finally, a comparison between analytical and simulation results is used to validate the analysis.

The bit error rate (BER) performance of a two-dimensional (2-D) RAKE receiver, in combination with transmit diversity on the downlink of a wide-band CDMA (W-CDMA) system, is presented. The analyses assume correlated fading between receive antenna array elements, and an arbitrary number of independent but nonidentical resolvable multipaths combined by the RAKE receiver in the general Nakagami-m fading channel framework. The impact of the array configuration (e.g., the number of transmit antennas and receive antennas, the antenna element separation) and the operating environment parameters (such as the fading severity, angular spread and path delay profile) on the overall space-path diversity gain can be directly evaluated. In addition, the exact pairwise error probability of a convolutional coded system is obtained, and the coding gain of a space-path diversity receiver is quantified.

Tracking of highly maneuvering targets with unknown behavior is a difficult problem in sequential state estimation. The performance of predictive-model-based Bayesian state estimators deteriorates quickly when their models are no longer accurate or their process not. se is large. A data-driven approach to tracking, the segmenting track identifier (STI), is presented as an algorithm that operates well in environments where the measurement system is well understood but target motion is either or both highly unpredictable or poorly characterized. The STI achieves improved state estimates by the least-squares fitting of a motion model to a segment of data that has been partitioned from the total track such that it represents a single maneuver. Real-world STI tracking performance is demonstrated using sonar data collected from free-swimming fish, where the STI is shown to be effective at tracking highly maneuvering targets while relatively insensitive to its tuning parameters. Additionally, an extension of the STI to. allow its use in the most common multiple target and cluttered environment data association frameworks is presented, and an STI-based joint probabilistic data association filter (STIJPDAF) is derived as a specific example. The STIJPDAF is shown by simulation to be effective at tracking a single fish in clutter and through empirical results from video data to be effective at simultaneously tracking multiple free-swimming fish.