Wireless Communication Networks:

PhD Dissertation

The use of wireless technology is rapidly growing. The demand is so huge that the limited supply of resources is becoming the bottleneck. Thus, network designs need to be rethought. Most of the analyses to date consider complete network information, perfect knowledge of channel state at the receivers, perfect knowledge of location of destination or perfect feedback link. This is an idealization and new design strategies accounting for the imperfect or incomplete information are needed. In this thesis, we will consider the effect of various forms of incomplete and imperfect knowledge motivated by practical protocol designs. The basic theme of the results is an old adage ``If we know more, we can achieve more." This thesis applies this adage to networks, where more information about the network translates into higher throughput or diversity.


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Full-Duplex Wireless Communications:

The graphic alongside depicts a design of a full-duplex radio design that can be used for self-interference cancellation. I worked on some innovations at the physical and MAC layers for full-duplex communication systems for applications in indoor as well as outdoor environments


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Communication with Energy Harvesting Devices:

The utilization of renewable energy is an important characteristic of the green wireless communication, making the flexible deployment of the transmitters and reducing the consumption of the fossil energy. If the devices are powered with renewable energy sources, what are the best approaches for communication accounting for the limited battery capacity. The figure alongside gives a pictorial representation of a energy harvesting device with battery. I investigated new scheduling approaches with renewable energy. In the presence of non-causal information for energy harvesting and the channel state information, we give a dynamic water-filling algorithm that is optimal. Since the non-causal channel state information may be less practical, I consider a reverse water-filling algorithm with causal information of the channel states. I also gave an approximately optimal solution that works with complete causal information. Further, the results have been extended to multiple users. Surprisingly, for the case of multiple interfering links, we find that frequency splitting is essential with finite battery capacity which is not the case with just average power constraint.


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LTE-Advanced Networks and Beyond:

The graphic alongside depicts a heterogenous network with a macro cell, and a small cell. The region within the two circles is the cell range extension region. I worked on innovations on LTE-Advanced (LTE-A) macro-cell deployments to improve system performance using optimized CA and eICIC techniques. I further propose an outage mitigation framework for LTE-A wireless networks, where we perform a dual optimization of the transmission power and beamforming weight parameters at each neighbor cell sector of the outage eNBs, while taking into account both the channel characteristics and residual eNB resources, after serving its current traffic load. Our interest lies in recent research in wireless, including but not limited to massive MIMO and spectrum sharing.


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Feedback and Cooperation in Interference Channels:

I analyzed the role of feedback in the simple two-user interference channel shown in the graphic on the right. As a rule, the sum capacity of a network grows with the number of feedback links, but in this case I proved that dedicated feedback along a single direct link gives the same sum capacity as dedicated feedback along all four reverse links. I also looked at two-way communication and showed that when the interference is strong, it is optimal to operate the forward and feedback channels independently. Further, we look at general MIMO Interference Channels with feedback, and receiver cooperation to characterize the approximate capacity and GDoF regions. In this case, we give an extension of the concept of private and public power levels studied for SISO case in Gaussian Han-Kobayashi region to a general MIMO channel.


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Local Algorithms that meet Global Performance Objectives:

The graphic alongside depicts a simple wireless network where each node is only aware of channel gains at nodes in its immediate neighborhood. Given only this local knowledge, I showed that there is no distributed algorithm that achieves the same sum capacity as the centralized algorithm. However, there exists a distributed algorithm that achieves the same sum capacity as the centralized algorithm if the top transmitter and receiver is given knowledge of two more hops. I used graph theory to explore the existence of a distributed optimal strategy for any topology with local knowledge at all the nodes. I also analyzed the effect of side information such as source arrival statistics and instantaneous queue state on the stable throughput region for a multiple access channel.


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Multi-round Protocols for Two-way Fading Channels:

Most communication systems use some form of feedback, often related to channel state information. It is standard to assume perfect channel state information at the receiver or noiseless feedback links. With more realistic assumptions, the picture of what rates are possible changes dramatically. I was able to determine a diversity-multiplexing tradeoff that properly accounts for the errors in training the receiver and the errors in the feedback link for FDD (Frequency Division Duplexed) systems, where the forward and the feedback links are independent MIMO channels and for TDD (Time Division Duplexed) systems, where the forward and the feedback links have reciprocity. We break with the tradition in all current channel state based protocols by using multiple rounds of conferencing to extract more bits about the actual channel. This iterative refinement of the channel increases the diversity order with every round of communication. The protocols are on-demand in nature, using high powers for training and feedback only when the channel is in poor states. I showed for FDD systems that the diversity multiplexing tradeoff with perfect training and K levels of perfect feedback can be achieved, even when there are errors in training the receiver and errors in the feedback link, with a multi-round protocol which has K rounds of training and K-1 rounds of binary feedback. For TDD systems, I developed new achievable strategies with multiple rounds of communication between the transmitter and the receiver, which use the reciprocity of the forward and the feedback channel. The multi-round TDD protocol achieves a diversity-multiplexing tradeoff which uniformly dominates its FDD counterparts, where no channel reciprocity is available.


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Relay Channels:

My work on wireless relay channels was partly sponsored by Siemens who were interested in how to deploy relays to provide a target rate to a given coverage area. I evaluated the coding strategies introduced by Cover and El Gamal that are known as Decode and Forward (DF) and Compress and Forward (CF). I showed that if the relay is able to decode, then DF is uniformly superior in that it provides coverage at any point served by CF. Further for any reasonable power ratio between the source and the relay, DF is superior. With Siemens, I also developed algorithms that enabled cooperative communication between Base Stations in two adjacent cells and a relay or a mobile station at the boundary of the two cells. I showed that phase-faded dirty-tape techniques (a causal modification of dirty-paper) have the potential to dramatically increase throughput, but are sensitive to the accuracy of the partial CSI available to the transmitter.


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Secrecy Capacity:

I have analyzed the physical layer security provided by a relay network where the relay and destination receive signals on two orthogonal channels, and the eavesdropper can overhear either one or both of these channels. I proved that a partial decode and forward strategy is able to achieve the secrecy capacity. I have also evaluated the impact of an active eavesdropper who actively corrupts some of the bits in the channel from source to destination.


Work in Undergrad:

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