A stochastic approximation algorithm for stochastic semidefinite programming

Motivated by applications to multi-antenna wireless networks, we propose a distributed and asynchronous algorithm for stochastic semidefinite programming. This algorithm is a stochastic approximation of a continous- time matrix exponential scheme regularized by the addition of an entropy-like term to the problem's objective function. We show that the resulting algorithm converges almost surely to an $\varepsilon$-approximation of the optimal solution requiring only an unbiased estimate of the gradient of the problem's stochastic objective. When applied to throughput maximization in wireless multiple-input and multiple-output (MIMO) systems, the proposed algorithm retains its convergence properties under a wide array of mobility impediments such as user update asynchronicities, random delays and/or ergodically changing channels. Our theoretical analysis is complemented by extensive numerical simulations which illustrate the robustness and scalability of the proposed method in realistic network conditions.Comment: 25 pages, 4 figure

Beam alignment for millimeter wave vehicular communications

Millimeter wave (mmWave) has the potential to provide vehicles with high data rate communications that will enable a whole new range of applications. Its use, however, is not straightforward due to its challenging propagation characteristics. One approach to overcome the propagation challenge is the use of directional beams, but it requires a proper alignment and presents a challenging engineering problem, especially under the high vehicular mobility. In this dissertation, fast and efficient beam alignment solutions suitable for vehicular applications are developed. To better quantify the problem, first the impact of directional beams on the temporal variation of the channels is investigated theoretically. The proposed model includes both the Doppler effect and the pointing error due to mobility. The channel coherence time is derived, and a new concept called the beam coherence time is proposed for capturing the overhead of mmWave beam alignment. Next, an efficient learning-based beam alignment framework is proposed. The core of this framework is the beam pair selection methods that use side information (position in this case) and past beam measurements to identify promising beam directions and eliminate unnecessary beam training. Three offline learning methods for beam pair selection are proposed: two statistics-based and one machine learning-based methods. The two statistical learning methods consist of a heuristic and an optimal selection that minimizes the misalignment probability. The third one uses a learning-to-rank approach from the recommender system literature. The proposed approach shows an order of magnitude lower overhead than existing standard (IEEE 802.11ad) enabling it to support large arrays at high speed. Finally, an online version of the optimal statistical learning method is developed. The solution is based on the upper confidence bound algorithm with a newly introduced risk-aware feature that helps avoid severe misalignment during the learning. Along with the online beam pair selection, an online beam pair refinement is also proposed for learning to adapt the codebook to the environment to further maximize the beamforming gain. The combined solution shows a fast learning behavior that can quickly achieve positive gain over the exhaustive search on the original (and unrefined) codebook. The results show that side information can help reduce mmWave link configuration overhead.Electrical and Computer Engineerin

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Interference Mitigation in Frequency Hopping Ad Hoc Networks

Radio systems today exhibit a degree of flexibility that was unheard of only a few years ago. Software-defined radio architectures have emerged that are able to service large swathes of spectrum, covering up to several GHz in the UHF bands. This dissertation investigates interference mitigation techniques in frequency hopping ad hoc networks that are capable of exploiting the frequency agility of software-defined radio platforms

Noise-based Transmit Reference Modulation:A Feasibility Analysis

Wireless sensor networks (WSNs) receive huge research interest for a multitude of applications, ranging from remote monitoring applications, such as monitoring of potential forest fires, floods and air pollution, to domestic and industrial monitoring of temperature, humidity, vibration, stress, etc. In the former set of applications, a large number of nodes can be involved which are usually deployed in remote or inaccessible environments. Due to logistic and cost reasons, battery replacement is undesired. Energy-efficient radios are needed, with a power-demand so little that batteries can last the lifetime of the node or that the energy can be harvested from the environment. Coherent direct-sequence spread spectrum (DSSS) based radios are widely employed in monitoring applications, due to their overall resilience to channel impairments and robustness against interference. However, a DSSS rake receiver has stringent requirements on precise synchronization and accurate channel knowledge. To obviate the complexity of a coherent DSSS receiver, particularly for low data rate sensor networks, a DSSS scheme that has fast synchronization and possibly low power consumption, is much desired. In this regard, this thesis studies a noncoherent DSSS scheme called transmit reference (TR), which promises a simple receiver architecture and fast synchronization. In traditional TR, the modulated information signal is sent along an unmodulated reference signal, with a small time offset between them. In this thesis, we present and investigate a variant of TR, called noise-based frequency offset modulation (N-FOM), which uses pure noise as the spreading signal and a small frequency offset (instead of a time offset) to separate the information and reference signals. The detection is based on correlation of the received signal with a frequency-shifted version of itself, which collects the transmitted energy without compromising the receiver simplicity. Analytical expressions on performance metrics, supplemented by simulation results, improve understanding of the underlying mechanisms and provide insights into utility of N-FOM in low-power WSNs. In point-to-point line-of-sight (LOS) communication, it was observed that the communication scheme has a minimal utility. The energy-detector type of receiver mixes all in-band signals, which magnifies the overall noise. Particularly, the self-mixing of the transmitted signal also elevates the noise level, which increases with a further increase in the received signal energy. Therefore, for a fixed set of system parameters, the performance attains an asymptote with increasing transmission power. The phenomenon also establishes a non-monotonic relation between performance and the spreading factor. It was observed that a higher spreading factor in N-FOM is beneficial only in a high-SNR regime. After developing an understanding of the performance degrading mechanisms, few design considerations are listed. It is found that a suitable choice of the receiver front-end filter can maximize the SNR. However, the optimal filter depends on received signal and noise levels. A practically feasible – albeit suboptimal – filter is presented which gives close to the optimal performance. Next, timing synchronization is considered. The implications of synchronization errors are analyzed, and a synchronization strategy is devised. The proposed synchronization strategy has little overhead and can be easily implemented for symbol-level synchronization. The N-FOM LOS link model is extended to assess the performance degradation due to interference. Performance metrics are derived which quantify the effects of multiple-user interference, as well as that from external interferers, such as WiFi. Since the correlation operation mixes all in-band signals, the total interfering entities are quadratically increased. The research shows the vulnerability of N-FOM to interference, which makes it optimistic to operate in a crowded shared spectrum (such as the ISM 2.4\,GHz band). We also observe an upper limit on the number of mutually interfering links in a multiple access (MA) network, that can be established with an acceptable quality. The scheme is further investigated for its resilience against impairments introduced by a dense multipath environment. It is observed that despite the noise enhancement, the N-FOM system performs reasonably well in a non-line-of-sight (NLOS) communication. The detection mechanism exploits the multipath channel diversity and leads to an improved performance in a rich scattering environment. An analytical expression for outage probability is also derived. The results indicate that a healthy N-FOM link with very low outage probability can be established at a nominal value of the received bit SNR. It is also found that the choice of the frequency offset is central to the system design. Due to multiple practical implications associated with this parameter, the maximum data rate and the number of usable frequency offsets are limited, particularly in a MA NLOS communication scenario. The analysis evolves into a rule-of-thumb criterion for the data rate and the frequency offset. It is deduced that, due to its limited capability to coexist in a shared spectrum, N-FOM is not a replacement for coherent DSSS systems. The scheme is mainly suited to a low data rate network with low overall traffic, operating in an interference-free rich scattering environment. Such a niche of sensor applications could benefit from N-FOM where the design goal requires a simple detection mechanism and immunity to multipath fading

Distributed Algorithms for the Optimal Design of Wireless Networks

This thesis studies the problem of optimal design of wireless networks whose operating points such as powers, routes and channel capacities are solutions for an optimization problem. Different from existing work that rely on global channel state information (CSI), we focus on distributed algorithms for the optimal wireless networks where terminals only have access to locally available CSI. To begin with, we study random access channels where terminals acquire instantaneous local CSI but do not know the probability distribution of the channel. We develop adaptive scheduling and power control algorithms and show that the proposed algorithm almost surely maximizes a proportional fair utility while adhering to instantaneous and average power constraints. Then, these results are extended to random access multihop wireless networks. In this case, the associated optimization problem is neither convex nor amenable to distributed implementation, so a problem approximation is introduced which allows us to decompose it into local subproblems in the dual domain. The solution method based on stochastic subgradient descent leads to an architecture composed of layers and layer interfaces. With limited amount of message passing among terminals and small computational cost, the proposed algorithm converges almost surely in an ergodic sense. Next, we study the optimal transmission over wireless channels with imperfect CSI available at the transmitter side. To reduce the likelihood of packet losses due to the mismatch between channel estimates and actual channel values, a backoff function is introduced to enforce the selection of more conservative coding modes. Joint determination of optimal power allocations and backoff functions is a nonconvex stochastic optimization problem with infinitely many variables. Exploiting the resulting equivalence between primal and dual problems, we show that optimal power allocations and channel backoff functions are uniquely determined by optimal dual variables and develop algorithms to find the optimal solution. Finally, we study the optimal design of wireless network from a game theoretical perspective. In particular, we formulate the problem as a Bayesian game in which each terminal maximizes the expected utility based on its belief about the network state. We show that optimal solutions for two special cases, namely FDMA and RA, are equilibrium points of the game. Therefore, the proposed game theoretic formulation can be regarded as general framework for optimal design of wireless networks. Furthermore, cognitive access algorithms are developed to find solutions to the game approximately