Online learning for exploiting diversity in adaptive wireless networks

Nikhil Gulati

doi:10.17918/etd-6347

The landscape for wireless systems and networks is changing rapidly with new emerging communication paradigms such as machine-to-machine communication (M2M), heterogeneous cellular network (HetNets), cognitive radio, and new WiFi technologies. As a result of this shift, there is a significant focus on making wireless networks self aware, self-reliant and adaptive, both at the edge and at the core. Fundamentally, wireless communication is still limited by noise, attenuation and interference. As the networks become dense, continuos evolution of current wireless infrastructure and technologies is required. While, the information theorists try to understand the fundamental capacity limits of these complex networks, the wireless engineers, try to achieve these limits and extract every bit of performance from all the layers of the network stack. In this dissertation, we focus on the role of diversity provided by modern antenna systems, in enabling an adaptive wireless system. Specifically, we focus on algorithms for exploiting the diversity offered by reconfigurable antenna systems tightly integrated with an agile wireless device. With the introduction of reconfigurable antennas, there was a departure from the notion that a wireless device has no control over the wireless channel. Reconfigurable antenna systems are capable of operating under multiple radiation states which provide multiple channels, potentially providing an opportunity to select a state for optimizing a communication link and/or a network state. This additional degree of freedom comes with an overhead to acquire information about the state of all the channels, a need for a strategy to select the optimal state, and most importantly an ability to learn the changes in the channel state in order to adapt. Traditionally techniques rely on prior knowledge of the channel which is often not available or heuristics which don't scale well. With these goals in mind, we utilize online learning based on multi-armed bandit theory to design algorithms to control and adapt the state of a reconfigurable antenna system. We investigate the trade-off between the amount and the frequency with which the channel state information is collected and its effect on the system performance. We demonstrate the effectiveness of an online sequential learning algorithm to select an optimal antenna state for throughput optimization in a single user wireless system similar to 802.11x WiFi devices. Further, we develop online learning algorithms for channel selection in a distributed multi-user network for enhancing interference management techniques. For both these network settings, we analyze the cost of learning under an unknown statistical model of the channel and compare it with an oracle with full prior knowledge. We characterize the performance of the proposed algorithms with link quality metrics derived from the channel information. We show promising results with improved performance in key metrics such as signal-to-noise ratio (SNR), link throughput, and network sum rate. Finally, we leverage a software defined radio platform to experimentally evaluate the usefulness of these algorithms in real-world scenarios. Finally, we also develop an online model learning technique using Gaussian Mixture Model to identify intruders in the network. We formulate the problem as a physical layer authentication problem where device signatures are generated using the pattern diversity offered by the reconfigurable antennas. The proposed algorithm learns the distribution of channel-based device signatures and distinguishes between an intruder and a legitimate user. We successfully show that the proposed technique can reduce the false alarm rates while achieving very low miss detection rate.

Online learning for exploiting diversity in adaptive wireless networks

Files and links (1)

Abstract

Metrics

Details

Online learning for exploiting diversity in adaptive wireless networks

Files and links (1)

Abstract

Metrics

Details

Drexel University Social media