Opportunistic selection and rate adaptation play a vital role in improving the spectral and power efficiency of current multi-node wireless systems. However, time-variations in wireless channels affect the performance of opportunistic selection and rate-adaptation in the following ways. Firstly, the selected node can become sub-optimal by the time data transmission commences. Secondly, the choice of transmission parameters such as rate and power for the selected node become sub-optimal. Lastly, the channel changes during data transmission. In this thesis, we develop a comprehensive and tractable analytical framework that accurately accounts for these effects. It differs from the extensive existing literature that primarily focuses on time-variations until the data transmission starts. Firstly, we develop a novel concept of a time-invariant effective signal-to-noise ratio (TIESNR), which tractably and accurately captures the time-variations during the data transmission phase with partial channel state information available at the receiver. Secondly, we model the joint distribution of the signal-to-noise ratio at the time of selection and TIESNR during the data transmission using generalized bivariate gamma distribution. The above analytical steps facilitate the analysis of the outage probability and average packet error rate (PER) for a given modulation and coding scheme and average throughput with rate adaptation. We also present extensive numerical results to verify the accuracy of each step of our approach and show that ignoring the correlated time variations during the data transmission phase can significantly underestimate the outage probability and average PER, whereas it overestimates the average throughput even for packet durations as low as 1 msec.

Opportunistic selection algorithms have grown in importance as next generation wireless systems strive towards higher data rates and spectral efficiencies. For example, in orthogonal frequency division multiple access(OFDMA), the system bandwidth is divided into many sub channels. For each sub channel, the user with the highest channel gain is opportunistically assigned to it. .Likewise, in a multi-source, multi-destination (MSD) cooperative relay system, a relay node must be assigned for every source-destination (SD) pair. The assignment decisions are based on local channel knowledge and must be fast so as to maximize the time available for data transmission. We develop novel multiple access based splitting-based selection algorithms for OFDMA and MSD systems. These systems are unique in that the same user and relay can be the most suitable one for multiple sub channels and multiple SD pairs, respectively. For OFDMA systems, we propose an algorithm called Split Select that assigns for every sub channel the user with the highest channel gain over it. For MSD systems, we propose a contention-based en masse assignment (CBEA) algorithm that assigns to each SD pair a relay that is capable of aiding it. Both Split Select and CBEA are fast and scale well with the number of nodes. For example, Split Select requires just 2.2 slots, on average, to assign a sub channel to its best user even when there are an asymptotically large number of contending users. Likewise, CBEA often takes far less than one slot, on average, to assign a relay to each SD pair.