Srinivas R. Kadaba

Channel quality estimation and rate adaptation for cellular mobile radio

We propose a technique to measure channel quality in terms of signal-to-interference plus noise ratio (SINR) for the transmission of signals over fading channels. The Euclidean distance (ED) metric, associated with the decoded information sequence or a suitable modification thereof, is used as a channel quality measure. Simulations show that the filtered or averaged metric is a reliable channel quality measure which remains consistent across different coded modulation schemes and at different mobile speeds. The average scaled ED metric can be mapped to the SINR per symbol. We propose the use of this SINR estimate for data rate adaptation, in addition to mobile assisted handoff (MAHO) and power control. We particularly focus on data rate adaptation and propose a set of coded modulation schemes which utilize the SINR estimate to adapt between modulations, thus improving the data throughput. Simulation results show that the proposed metric works well across the entire range of Dopplers to provide near-optimal rate adaptation to average SINR. This method of adaptation averages out short-term variations due to Rayleigh fading and adapts to the long-term effects such as shadowing. At low Dopplers, the metric can track Rayleigh fading and match the rate to a short-term average of the SINR, thus further increasing throughput.

Downlink scheduling in CDMA data networks

Packet data is expected to dominate third generation wireless networks, unlike current generation voice networks. This opens up new and interesting problems. Physical and link layer issues have been studied extensively, while resource allocation and scheduling issues have not been addressed satisfactorily. In this work, we address resource management on the downlink of CDMA packet data networks. Network performance (for example, capacity) has been addressed, but user centric performance has not received much attention. Recently, various non-traditional scheduling schemes based on new metrics have been proposed, and target user performance (mostly without reference to wireless). We adapt these metrics to the CDMA context, and establish some new results for the offline scheduling problem. In addition, we modify a large class of online algorithms to work in our setup and conduct a wide range of experiments. Based on detailed simulations, we infer that:Algorithms which exploit “request sizes” seem to outperform those that do not. Among these, algorithms that also exploit channel conditions provide significantly higher network throughput. Depending on continuous or discretized bandwidth conditions, either pure time multiplexing or a combination of time and code multiplexing strikes an excellent balance between user satisfaction and network performance. Discrete bandwidth conditions can lead to degraded user level performance without much impact on network performance. We argue that the discretization needs to be fine tuned to address this shortcoming.

Recursive estimation of images using non-Gaussian autoregressive models

We consider recursive estimation of images modeled by non-Gaussian autoregressive (AR) models and corrupted by spatially white Gaussian noise. The goal is to find a recursive algorithm to compute a near minimum mean square error (MMSE) estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. Our method is based on a simple approximation that makes possible the development of a useful suboptimal nonlinear estimator. The algorithm is first developed for a non-Gaussian AR time-series and then generalized to two dimensions. In the process, we draw on the well-known reduced update Kalman filter (KF) technique of Woods and Radewan to circumvent computational load problems. Several examples demonstrate the non-Gaussian nature of residuals for AR image models and that our algorithm compares favorably with the Kalman filtering techniques in such cases.

Soft output interference suppression in TDMA wireless communications

We present a novel symbol-by-symbol Bayesian multiuser detector for cochannel interference cancellation in TDMA cellular communications. To begin with, we present the usual composite signal model consisting of the desired signal, interferer, channel parameters and channel noise. Then, we derive the soft output interference cancellation algorithm (SICA) to compute fixed-lag maximum a posteriori (MAP) estimates of the data symbols of the desired user, using a fixed lookahead of D observations. In the process, the interferer symbols are treated as nuisance parameters and averaged out. The complexity of the SICA is exponential in the length of the combined channel at the receiver. Subsequently, we present a simple, yet effective, technique for complexity reduction without significant performance degradation. We apply the algorithm to some typical scenarios, using the IS-136 TDMA standard, to demonstrate its interference suppression capabilities. In this paper, we assume that all relevant channel parameters are known. Hence, our results should be viewed as representing the best possible performance that can be obtained with the SICA in the scenarios considered.