We study Social Groups consisting of self-interested inter-connected nodes looking for common content. We can observe Social Groups in various socio-technological networks, such as Cellular Network assisted Device-to-Device communications, Cloud assisted Peer-to-Peer Networks, hybrid Peer-to-Peer Content Distribution Networks and Direct Connect Networks. Each node wants to acquire a universe of segments at least cost. Nodes can either access an expensive link to the content distributor for downloading data segments, or use the well-connected low cost inter-node network for exchanging segments among themselves. Activation of an inter-node link requires cooperation among the participating nodes and reduces the cost of downloading for the nodes. However, due to uploading costs, Non-Reciprocating Nodes are reluctant to upload segments, in spite of their interest in downloading segments from others. We define the Give-and-Take (GT) criterion, which prohibits non-reciprocating behaviour in Social Groups for all nodes at all instants. In the “Full Exchange” case studied, two nodes can exchange copies of their entire segment sets, if each node gains at least one new segment from the other. Incorporating the GT criterion in the Social Group, we study the problem of downloading the universe at least cost, from the perspective of a new node having no data segments. We analyze this NP-hard problem, and propose algorithms for choosing the initial segments to be downloaded from the content distributor and the sequence of nodes for exchange. We compare the performance of these algorithms with a few existing P2P downloading strategies in terms of cost and running time. In the second problem, we attempt to reduce the load on the content distributor by choosing a schedule of inter-node link activations such that the number of nodes with the universe is maximized. Link activation decisions are taken by a central entity, the facilitator, for achieving the social optimum. We present the asymptotically optimal Randomized algorithm. We also present other algorithms, such as the Greedy Links algorithm and the Polygon algorithm, which are optimal under special scenarios of interest. We compare the performances of all proposed algorithms with the optimal value of the objective. We observe that computationally intensive algorithms exhibit better performance. Further, we consider the problem of decentralized scheduling of links. The decisions of link activations are made by the participating nodes in a distributed manner. While conforming to the GT criterion for inter-node exchanges, each node's objective is to maximize its utility. Each node tries to find a pairing partner by preferentially exploring nodes for link formation. Unpaired nodes choose to download a segment using the expensive link with Segment Aggressiveness Probability (SAP). We present linear complexity decentralized algorithms for nodes to choose their best strategy. We present a decentralized randomized algorithm that works in the absence of the facilitator and performs close to optimal for large number of nodes. We define the Price of Choice to benchmark performance of Social Groups (consisting of non-aggressive nodes) with the optimal. We evaluate the performance of various algorithms and characterize the behavioural regime that will yield best results for node and Social Group as well.

Resource allocation in wireless networks is one of the most studied class of problems. Generally, these problems are formulated as utility maximization problems under relevant constraints. The challenges posed by these problems vary widely depending on the nature of the utility function under consideration. Recently, the widespread prevalence of wireless devices prompted researchers and engineers to delve into the security issues of wireless communication. As compared to the wired medium, ensuring security for the wireless medium is more challenging mainly due to the broadcast nature of the transmission. But the ongoing research on physical layer security promises robust and reliable security schemes for wireless communication. Contrary to conventional cryptographic schemes, physical layer security techniques are impregnable as the security is ensured by the inherent randomness present in the wireless medium. In this thesis, we consider several wireless scenarios and propose secrecy enhancing resource allocation schemes for them in the first few chapters. We initially address the problem of secure transmission by following the conventional approach in the secrecy literature|secrecy rate maximization. Needless to say, in these chapters, secrecy rate is the utility function and the constraints are posed by the available power budget. Then we consider a pragmatic approach where we target the signal-to-noise ratio (SNR) of participating nodes and ensure information secrecy by appropriately constraining the SNRs of those nodes. In those SNR based formulations, SNR at the destination is the utility function and we are interested in maximizing it. In the last two chapters, we study two scenarios in a non-secrecy setting. In one of them, end-to-end data rate is the utility, whereas, in the other one, two utility functions|based on revenue generated|are defined for two rational agents in a game-theoretic setting. In the second chapter, we study parallel independent Gaussian channels with imperfect channel state information (CSI) for the eavesdropper. Firstly, we evaluate the probability of zero secrecy rate in this system for (i) given instantaneous channel conditions and (ii) a Rayleigh fading scenario. Secondly, when non-zero secrecy is achievable in the low SNR regime, we aim to solve a robust power allocation problem which minimizes the outage probability at a target secrecy rate. In the third, fourth and fifth chapters, we consider scenarios where the source node transmits a message to the destination using M parallel amplify-and-forward (AF) relays in the presence of a single or multiple eavesdroppers. The third chapter addresses the problem of the maximum achievable secrecy rate for two specific network models: (a) degraded eavesdropper channel with complex channel gain and (b) scaled eavesdropper channel with real-valued channel gains. In the fourth chapter, we consider the SNR based approach and address two problems: (i) SNR maximization at the destination and (ii) Total relay power minimization. In the fifth chapter, we assume that the relay nodes are untrusted and to counter them, we deliberately introduce artificial noise in the source message. For this model, we propose and solve SNR maximization problems for the following two scenarios: (i) Total power constraint on all the relay nodes and (ii) Individual power constraints on each of the relay nodes. In the sixth chapter, we address the problem of passive eavesdroppers in multi-hop wire-less networks using the technique of friendly jamming. Assuming decode-and-forward (DF) relaying, we consider a scheduling and power allocation (PA) problem for a multiple-source multiple-sink scenario so that eavesdroppers are jammed, and source-destination throughput targets are met. When channel state information (CSI) of all the node are available, we intend to minimize the total power consumption of all the transmitting nodes. In the absence of eavesdroppers CSI, we minimize vulnerability region of the network. In chapter seven, the problem of cooperative beamforming for maximizing the achievable data rate of two-hop amplify-and-forward (AF) network (in the absence of eavesdropper(s)) is considered. Along with an individual power constraint on each of the relay nodes, we consider a weighted sum power constraint. To solve this problem, we propose a novel algorithm based on the Quadratic Eigenvalue Problem (QEP) and discuss its convergence. In chapter eight, we study a Stackelberg game between a base station and a multi-antenna power beacon for wireless energy harvesting in a multiple sensor node scenario. Assuming imperfect CSI between the sensor nodes and the power beacon, we propose a utility function that is based on throughput non-outage probability at the base station. We find the optimal strategies for the base station and the power beacon that maximize their respective utility functions.

Cognitive Radios are emerging communication systems which efficiently utilize the unused licensed radio spectrum called spectral holes. They run Spectrum sensing algorithms to identify these spectral holes. These holes need to be identified at very low SNR (<=-20 dB) under multipath fading, unknown channel gains and noise power. Cooperative spectrum sensing which exploits spatial diversity has been found to be particularly effective in this rather daunting endeavor. However despite many recent studies, several open issues need to be addressed for such algorithms. In this thesis we provide some novel cooperative distributed algorithms and study their performance. We develop an energy efficient detector with low detection delay using decentralized sequential hypothesis testing. Our algorithm at the Cognitive Radios employ an asynchronous transmission scheme which takes into account the noise at the fusion center. We have developed a distributed algorithm, DualSPRT, in which Cognitive Radios (secondary users) sequentially collect the observations, make local decisions and send them to the fusion center. The fusion center sequentially processes these received local decisions corrupted by Gaussian noise to arrive at a final decision. Asymptotically, this algorithm is shown to achieve the performance of the optimal centralized test, which does not consider fusion center noise. We also theoretically analyze its probability of error and average detection delay. Even though DualSPRT performs asymptotically well, a modification at the fusion node provides more control over the design of the algorithm parameters which then performs better at the usual operating probabilities of error in Cognitive Radio systems. We also analyze the modified algorithm theoretically. DualSPRT requires full knowledge of channel gains. Thus we extend the algorithm to take care the imperfections in channel gain estimates. We also consider the case when the knowledge about the noise power and channel gain statistic is not available at the Cognitive Radios. This problem is framed as a universal sequential hypothesis testing problem. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. Asymptotic performance of the algorithm is presented. A cooperative algorithm is also designed for such a scenario. Finally, decentralized multihypothesis sequential tests, which are relevant when the interest is to detect not only the presence of primary users but also their identity among multiple primary users, are also considered. Using the insight gained from binary hypothesis case, two new algorithms are proposed.

We consider a wireless sensor network, in which end users are interested in maximizing the useful information supplied by the network till network partition due to inevitable node deaths. Neither throughput maximization nor network lifetime maximization achieves the objective: A network with high throughput provides information at a high rate, but can exhaust the nodes of their energies quickly; similarly, a network can achieve a long lifetime by remaining idle for most of the time. We propose and seek to maximize a new metric: “Aggregate bit transported before network partition” (a product of throughput and lifetime), which precisely captures the usefulness of sensor networks. We model the links in the wireless sensor network as wired links with reduced equivalent capacities, formulate and solve the problem of maximizing bits transported before network partition on arbitrary networks. To assess the benefits that network coding can yield for the same objective, we study a scenario where the coding-capable nodes are placed on a regular grid. We propose an optimal algorithm to choose the minimum number of coding points in the grid to ensure energy efficiency. Our results show that, even with simple XOR coding, the bits transported can increase up to 83 % of that without coding. Further, we study the problem of in-network data aggregation in a wireless sensor network to achieve minimum delay. The nodes in the network compute and forward data as per a query graph, which allows operations belonging to a general class of functions. We aim to extract the best sub-network that achieves the minimum delay. We design an algorithm to schedule the sub-network such that the computed data reaches sink at the earliest. We consider directed acyclic query graphs as opposed to the existing work which considers tree query graphs only.

This thesis deals with generalizations of the analytic signal (AS) construction proposed by Gabor. Functional extensions of the fractional Hilbert Transform (FrHT) are proposed using which families of analytic signals are obtained. The construction is further applied in the design of a secure communication scheme. A demodulation scheme is developed based on the generalized AS, motivated by perceptual experiments in binaural hearing. Demodulation is achieved using a signal and its arbitrary phase-shifted version which, in turn translated to demodulation using a pair of flat-top bandpass filters that form an FrHT parir. A new family of wavelets based on the popular Gammatone auditory model is proposed and is shown to lead to a good characterization of singularities/transients in a signal. Allied problems of computing smooth amplitude, phase, and frequency modulations from the AS. Construction of FrHT pair of wavelets, and temporal envelope fit of transient audio signals are also addressed.

Space-Time Block Coding is an efficient communication technique used in multiple-input multiple-output wireless systems. The complexity with which a Space-Time Block Code (STBC) can be decoded is important from an implementation point of view since it directly affects the receiver complexity and speed. In this thesis, we address the problem of designing low complexity decoding techniques for STBCs, and constructing STBCs that achieve high rate and full-diversity with these decoders. This thesis is divided into two parts; the first is concerned with the optimal decoder, viz. the maximum-likelihood (ML) decoder, and the second with non-ML decoders. An STBC is said to be multigroup ML decodable if the information symbols encoded by it can be partitioned into several groups such that each symbol group can be ML decoded independently of the others, and thereby admitting low complexity ML decoding. In this thesis, we first give a new framework for constructing low ML decoding complexity STBCs using codes over the Klein group, and show that almost all known low ML decoding complexity STBCs can be obtained by this method. Using this framework we then construct new full-diversity STBCs that have the least known ML decoding complexity for a large set of choices of number of transmit antennas and rate. We then introduce the notion of Asymptotically-Good (AG) multigroup ML decodable codes, which are families of multigroup ML decodable codes whose rate increases linearly with the number of transmit antennas. We give constructions for full-diversity AG multigroup ML decodable codes for each number of groups g > 1. For g > 2, these are the first instances of g-group ML decodable codes that are AG or have rate more than 1. For g = 2 and identical delay, the new codes match the known families of AG codes in terms of rate. In the final section of the first part we show that the upper triangular matrix R encountered during the sphere-decoding of STBCs can be rank-deficient, thus leading to higher sphere-decoding complexity, even when the rate is less than the minimum of the number of transmit antennas and the number receive antennas. We show that all known AG multigroup ML decodable codes suffer from such rank-deficiency, and we explicitly derive the sphere-decoding complexities of most known AG multigroup ML decodable codes. In the second part of this thesis we first study a low complexity non-ML decoder introduced by Guo and Xia called Partial Interference Cancellation (PIC) decoder. We give a new full-diversity criterion for PIC decoding of STBCs which is equivalent to the criterion of Guo and Xia, and is easier to check. We then show that Distributed STBCs (DSTBCs) used in wireless relay networks can be full-diversity PIC decoded, and we give a full-diversity criterion for the same. We then construct full-diversity PIC decodable STBCs and DSTBCs which give higher rate and better error performance than known multigroup ML decodable codes for similar decoding complexity, and which include other known full-diversity PIC decodable codes as special cases. Finally, inspired by a low complexity essentially-ML decoder given by Sirianunpiboon et al. for the two and three antenna Perfect codes, we introduce a new non-ML decoder called Adaptive Conditional Zero-Forcing (ACZF) decoder which includes the technique of Sirianunpiboon et al. as a special case. We give a full-diversity criterion for ACZF decoding, and show that the Perfect codes for two, three and four antennas, the Threaded Algebraic Space-Time code, and the 4 antenna rate 2 code of Srinath and Rajan satisfy this criterion. Simulation results show that the proposed decoder performs identical to ML decoding for these five codes. These STBCs along with ACZF decoding have the best error performance with least complexity among all known STBCs for four or less transmit antennas.

Advances in wireless communication and microelectronics have led to the development of low-power compact sensor nodes (popularly called motes) that are capable of sensing, computing, and communication. A large number of these nodes can be deployed over some area of interest to form a multi-hop network, commonly referred to as a wireless sensor network (WSN). Typical applications of WSNs include, environment and process monitoring in industrial installations, forest fire detection, structural health monitoring, etc. In such applications where the variables to be measured are slowly varying, or the events to be monitored are rare, continuous sensing is unnecessary. Instead, the nodes, in order to conserve their battery power, can sleep-wake cycle whereby each node is allowed to independently alternate between an ON state and a low power OFF state. Sleep-wake cycling, while increasing the network lifetime, renders the network disconnected a large fraction of the time; however, connectivity can be established over time by transporting packets in a store-and-forward manner, whereby packets are held by a forwarding node until a suitable node wakes up in its neighborhood that can serve to forward the packet towards the destination. We are concerned with sleep-wake cycling multi-hop wireless networks whose main task is to carry sporadic alarms packets from sensing nodes to a sink node. Our objective is to design simple local-information based routing solutions for such networks. With this in mind, we propose a relay selection problem that arises at a forwarding node (which is currently holding the alarm packet) while choosing a next-hop relay node. The forwarder, as and when the relays wake-up, evaluating the goodness of a relay based on a “reward” metric (e.g., a function of the relay’s progress towards sink, and the power required to get the packet across), has to decide whether to forward to this relay or to wait for future ones (i.e., to stop or continue). The forwarder’s objective is to choose a relay so as to minimize a combination of the average delay incurred and the average reward achieved. A basic version of our relay selection problem is equivalent to the basic asset selling problem studied in the operations research literature. After reviewing the solution to the basic problem we will proceed to study a model with full information, referred to as the completely observable (CO) model, where the number of relays is exactly known to the forwarder. Formulating the problem as a Markov decision process (MDP) we will characterize the solution to the CO model in terms of recursively-computable threshold functions. Next, we consider the partially observable (PO) model where only a belief (probability mass function) on the number of relays is known. Hence, the PO model falls within the realm of partially observable MDPs. After incorporating our model into this framework we will characterize the solution in terms of stopping sets, which is the set of all belief states where it is optimal to stop. Our main contribution here is to obtain inner and outer bounds for the stopping sets. We next propose a variant where the relays, upon waking up, do not reveal their rewards immediately, but instead the forwarder can choose to probe the relay to know its reward, incurring a probing cost. Thus, to the existing set of stop and continue actions, we have added a new probe action. This model is motivated by the efforts required to learn the channel gains (by probing) in a wireless system. A key result we prove here is that the solution is characterized in terms of stage independent thresholds. Finally, we study a model comprising two forwarders which are competing against each other to choose a next-hop relay (one for each). Here, a relay is allowed to offer possibly different reward to each forwarder. We will first consider a complete information case where the reward pair of a relay is known to both the forwarders. Using stochastic game theory we will characterize the solution to this model in terms of Nash equilibrium policy pairs (NEPPs). We obtain results illustrating the structure of NEPPs. Next, we study a partial information model where each forwarder gets to observe only its reward value. Towards obtaining the solution for this model, we will first formulate a Bayesian game which is effectively played by both the forwarders at each stage. Next, for this Bayesian game we prove the existence of Nash equilibrium strategies within the class of threshold strategies. This result will enable us to construct NEPPs for the partial information model. Although our primary contribution from the thesis is the theoretical study of the above mentioned variants of the basic relay selection model, we have also conducted extensive simulations to study the end-to-end performance obtained by applying the solution to these models at each hop en-route to the sink in a sleep-wake cycling WSN.

This thesis presents the development of a Sensor Tower Platform (STP) comprised of an array of Passive Infra-Red (PIR) sensors along with a classification algorithm that enables the STP to distinguish between human intrusion, animal intrusion and clutter arising from wind-blown vegetative movement in an outdoor environment. The research was motivated by the aim of exploring the potential use of wireless sensor networks (WSNs) as an early-warning system to help mitigate human-wildlife conflicts occurring at the edge of a forest. While PIR sensors are in commonplace use in indoor settings, their use in an outdoor environment is hampered by the fact that they are prone to false alarms arising from wind-blown vegetation. Every PIR sensor is made up of one or more pairs of pyroelectric pixels arranged in a plane, and the orientation of interest in this thesis is one in which this plane is a vertical plane, i.e., a plane perpendicular to the ground plane. The intersection of the Field Of View (FOV) of the PIR sensor with a second vertical plane that lies within the FOV of the PIR sensor, is called the virtual pixel array (VPA). The structure of the VPA corresponding to the plane along which intruder motion takes place determines the form of the signal generated by the PIR sensor. The STP developed in this thesis employs an array of PIR sensors designed so as to result in a VPA that makes it easier to discriminate between human and animal intrusion while keeping to a small level false alarms arising from vegetative motion. The design was carried out in iterative fashion, with each successive iteration separated by a lengthy testing phase. There were a total of 5 design iterations spanning a total period of 14 months. Given the inherent challenges involved in gathering data corresponding to animal intrusion, the testing of the SP was carried out both using real-world data and through simulation. Simulation was carried out by developing a tool that employed animation software to simulate intruder and animal motion as well as some limited models of wind-blown vegetation. More specifically, the simulation tool employed 3-dimensional models of intruder and shrub motion that were developed using the popular animation software Blender. The simulated output signal of the PIR sensor was then generated by calculating the area of the 3-dimensional intruder when projected onto the VPA of the STP. An algorithm for efficiently calculating this to a good degree of approximation was implemented in Open Graphics Library (OpenGL). The simulation tool was useful both for evaluating various competing design alternatives as well as for developing an intuition for the kind of signals the SP would generate without the need for time-consuming and challenging animal-motion data collection. Real-world data corresponding to human motion was gathered on the campus of the Indian Institute of Science (IISc), while animal data was recorded at a dog-trainer facility in Kengeri as well as the Bannerghatta Biological Park, both located in the outskirts of Bengaluru. The array of PIR sensors was designed so as to result in a VPA that had good spatial resolution. The spatial resolution capabilities of the STP permitted distinguishing between human and animal motion with good accuracy based on low-complexity, signal-energy computations. Rejecting false alarms arising from vegetative movement proved to be more challenging. While the inherent spatial resolution of the STP was very helpful, an alternative approach turned out to have much higher accuracy, although it is computationally more intensive. Under this approach, the intruder signal, either human or animal, was modelled as a chirp waveform. When the intruder moves along a circular arc surrounding the STP, the resulting signal is periodic with constant frequency. However, when the intruder moves along a more likely straight-line path, the resultant signal has a strong chirp component. Clutter signals arising from vegetative motion does not exhibit this chirp behavior and an algorithm that exploited this difference turned in a classification accuracy in excess of 97%.

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.

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.