Signal-scale cooperation is a class of techniques designed to harness the same gains offered by multi-antenna communication in scenarios where devices are too small to contain an array of antennas. While the potential improvements in reliability at the physical layer are well known, three key challenges must be addressed to harness these gains at the medium access layer: (a) the distributed synchronization and coordination of devices to enable cooperative behavior, (b) the conservation of energy for devices cooperating to help others, and (c) the management of increased inter-device interference caused by multiple spatially separate transmissions in a cooperative network. In this thesis, we offer three contributions that respectively answer the above three challenges. First, we present two novel cooperative medium access control protocols: Distributed On-demand Cooperation (DOC) and Power-controlled Distributed On-demand Cooperation (PDOC). These protocols utilize negative acknowledgments to synchronize and trigger cooperative relay transmissions in a completely distributed manner. Furthermore, they avoid cooperative transmissions that would likely be unhelpful to the source of the traffic. Second, we present an energy conservation algorithm known as Distributed Energy-Conserving Cooperation (DECC). DECC allows devices to alter their cooperative behavior based on measured changes to their own energy efficiency. With DECC, devices become self-aware of the impact of signal-scale cooperation -- they explicitly monitor their own performance and scale the degree to which they cooperate with others accordingly. Third and finally, we present a series of protocols to combat the challenge of inter-device interference. Whereas energy efficiency can be addressed by a self-aware device monitoring its own performance, inter-device interference requires devices with network awareness that understand the impact of their behavior on the devices around them. We investigate and quantify the impact of incomplete network awareness by proposing a modeling approximation to derive relaying policy behaviors. We then map these policies to protocols for wireless channels.

In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well. Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.