One of the fundamental assumptions made in the design of wireless networks is that the wireless devices have to be half-duplex, i.e., they cannot simultaneously transmit and receive in the same frequency band. The key deterrent in implementing a full-duplex wireless device, which can simultaneously transmit and receive in the same frequency band, is the large power differential between the self-interference from a device's own transmissions and the signal of interest coming from a distant source. In this thesis, we revisit this basic assumption and propose a full-duplex radio design. The design suppresses the self-interference signal by employing a combination of passive suppression, and active analog and digital cancellation mechanisms. The active cancellations are designed for wideband, multiple subcarrier (OFDM), and multiple antenna (MIMO) wireless communications systems. We then implement our design as a 20 MHz MIMO OFDM system with a 2.4 GHz center frequency, suitable for Wi-Fi systems. We perform extensive over-the-air tests to characterize our implementation. Our main contributions are the following: (a) the average amount of active cancellation increases as the received self-interference power increases and as a result, the rate of a full-duplex link increases as the transmit power of communicating devices increases, (b) applying digital cancellation after analog cancellation can sometimes increase the self-interference and the effectiveness of digital cancellation in a full-duplex system will depend on the performance of the cancellation stages that precede it, (c) our full-duplex device design achieves an average of 85 dB of self-interference cancellation over a 20 MHz bandwidth at 2.4 GHz, which is the best cancellation performance reported to date, (d) our full-duplex device design achieves 30-84% higher ergodic rates than its half-duplex counterpart for received powers in the range of [-75, -60] dBm. As a result, our design is the first one to achieve Wi-Fi ranges; in comparison, no implementation to date has achieved Wi-Fi ranges. Consequently, we have conclusively demonstrated that Wi-Fi full-duplex is practically feasible and hence shown that one of the commonly made assumptions in wireless networks is not fundamental.

In this thesis, we study a three-node full-duplex network, where the infrastructure node has simultaneous up- and downlink communication in the same frequency band with two half-duplex nodes. In addition to self-interference at the full-duplex infrastructure node, the three-node network has to contend with the inter-node interference between the two half-duplex nodes. The two forms of interferences differ in one important aspect that the self-interference is known at the interfered receiver. Therefore, we propose to leverage a wireless side-channel to manage the inter-node interference. We characterize the impact of inter-node interference on the network achievable rate region with and without a side-channel between the nodes. We present four distributed full-duplex inter-node interference cancellation schemes, which leverage the device-to-device wireless side-channel for improved interference cancellation. Of the four, bin-and-cancel is asymptotically optimal in high signal-to-noise ratio limit which uses Han-Kobayashi common-private message splitting and achieves within 1 bits/s/Hz of the capacity region for all values of channel parameters. The other three schemes are simpler compared to bin-and-cancel but achieve the near-optimal performance only in certain regimes of channel values. Asymptotic multiplexing gains of all proposed schemes are derived to show analytically that leveraging the side channel can be highly beneficial in increasing the multiplexing gain of the system exactly in those regimes where inter-node interference has the highest impact.

Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications in the Internet (i.e., online gaming and VoIP), latency becomes a major issue for the development of wireless technology. Taking advantage of the significant decline in memory prices, industrialists equip the network devices with larger buffering capacities to improve the network throughput by limiting packets drops. Over-buffering results in increasing the time that packets spend in the queues and, thus, introducing more latency in networks. This phenomenon is known as “bufferbloat”. While throughput is the dominant performance metric, latency also has a huge impact on user experience not only for real-time applications but also for common applications like web browsing, which is sensitive to latencies in order of hundreds of milliseconds. Concerns have arisen about designing sophisticated queue management schemes to mitigate the effects of such phenomenon. My thesis research aims to solve bufferbloat problem in both traditional half-duplex and cutting-edge full-duplex wireless systems by reducing delay while maximizing wireless links utilization and fairness. Our work shed lights on buffer management algorithms behavior in wireless networks and their ability to reduce latency resulting from excessive queuing delays inside oversized static network buffers without a significant loss in other network metrics. First of all, we address the problem of buffer management in wireless full-duplex networks by using Wireless Queue Management (WQM), which is an active queue management technique for wireless networks. Our solution is based on Relay Full-Duplex MAC (RFD-MAC), an asynchronous media access control protocol designed for relay full-duplexing. Compared to the default case, our solution reduces the end-to-end delay by two orders of magnitude while achieving similar throughput in most of the cases. In the second part of this thesis, we propose a novel design called “LearnQueue” based on reinforcement learning that can effectively control the latency in wireless networks. LearnQueue adapts quickly and intelligently to changes in the wireless environment using a sophisticated reward structure. Testbed results prove that LearnQueue can guarantee low latency while preserving throughput.