Federated learning (FL) is a leading approach for iterative learning using possibly private data available at edge devices. The federated operation gives rise to challenges in privacy leakage, which accumulates in learning, and communication latency. These limitations are often individually mitigated by the introduction of privacy preserving noise and user-selection policies, typically at the cost of accuracy. In this work, we propose Privacy-aware Active User SElection (PAUSE), which balances the trade-off between privacy accumulation, communication latency, and optimization of the learned model, via dedicated user selection. This triplet is used to construct a reward (cost function), according to which a multi-armed bandit (MAB)-based algorithm dynamically chooses a subset of users in each round, while guaranteeing bounded accumulated privacy leakage. We establish a theoretical analysis, systematically showing that the reward growth rate of PAUSE follows the best-known rate in MAB literature. While the privacy guarantees hold by the construction of PAUSE, we numerically validate its associated improved latency and accuracy gains in different experimental settings of FL.

In the fast-evolving world of wireless networks, achieving high throughput with low latency is essential for future communication systems. Although low-complexity OSPF-type solutions are effective in lightly-loaded networks, their performance tends to degrade as congestion increases. Recent methods have proposed using backpressure and deep learning for route optimization, but these approaches face challenges due to their high implementation and computational complexity, which may exceed the capabilities of networks with limited hardware resources. A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this paper, we address this challenge by developing a novel approach, dubbed Regularized Routing Optimization (RRO). The RRO algorithm offers both distributed and centralized implementations with low complexity, making it suitable for integration into 5G and beyond tech-nologies, where no significant changes to the existing protocols are needed. It increases throughput while ensuring latency remains sufficiently low through regularized optimization. We analyze the computational complexity of RRO and prove that it converges with a level of complexity comparable to OSPF. Extensive simulation results across diverse network topologies demonstrate that RRO significantly outperforms existing methods.

In the rapidly evolving landscape of wireless networks, achieving enhanced throughput with low latency for data transmission is crucial for future communication systems. While low complexity OSPF-type solutions have shown effectiveness in lightly-loaded networks, they often falter in the face of increasing congestion. Recent approaches have suggested utilizing backpressure and deep learning techniques for route optimization. However, these approaches face challenges due to their high implementation and computational complexity, surpassing the capabilities of networks with limited hardware devices. A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this collaborative research between Ben-Gurion University and Ceragon Networks Ltd., we address this challenge by developing a novel approach, dubbed Regularized Routing Optimization (RRO). The RRO algorithm offers both distributed and centralized implementations with low complexity, making it suitable for integration into 5G and beyond technologies, where no significant changes to the existing protocols are needed. It increases throughput while ensuring latency remains sufficiently low through regularized optimization. We analyze the computational complexity of RRO and prove that it converges with a level of complexity comparable to OSPF. Extensive simulation results across diverse network topologies demonstrate that RRO significantly outperforms existing methods.

We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) measure under a specified target quality of service (QoS)-SINR for each network. The shared bandwidth is partitioned into K channels with frequency separation. In contrast to the majority of existing studies that assume perfect orthogonality or a one-to-one user-channel allocation mapping, this paper focuses on practical systems experiencing inter-carrier interference (ICI) and channel reuse by multiple large-scale networks. This realistic scenario significantly increases the problem dimension, rendering existing algorithms inefficient. We propose a novel multi-agent reinforcement learning (RL) framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARLTON). The CARLTON framework is based on the Centralized Training with Decentralized Execution (CTDE) paradigm, utilizing the DeepMellow value-based RL algorithm. To ensure robust performance in the interference-laden environment we address, CARLTON employs a low-dimensional representation of observations, generating a QoS-type measure while maximizing a global SINR measure and ensuring the target QoS-SINR for each network. Our results demonstrate exceptional performance and robust generalization, showcasing superior efficiency compared to alternative state-of-the-art methods, while achieving a marginally diminished performance relative to a fully centralized approach.

We address the problem of detecting an anomalous process among a large number of processes. At each time t, normal processes are in state zero (normal state), whereas the abnormal process may exist in either state zero (normal state) or state one (abnormal state), with these states remaining hidden. The transitions between states for the abnormal process follow a Markov chain over time. During each time step, observations can be drawn from a selected subset of processes. Each probed process generates an observation based on its hidden state, following a typical distribution under state zero or an abnormal distribution under state one. The objective is to design a sequential search strategy that minimizes the expected detection time, subject to an error probability constraint. In contrast to prior studies on related models that focused on i.i.d. observations, the new model leads to the detection of a hidden Markov model (HMM) of anomaly, introducing significant challenges in both algorithm design and theoretical analysis. We introduce a novel sequential search strat-egy, referred to as the Anomaly Detection under Hidden Markov (ADHM) algorithm, and show that ADHM is asymptotically optimal as the error probability approaches zero. Simulation results demonstrate the superior performance of ADHM over existing methods within a finite regime.

We consider the problem of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between links along their routes, resulting in reduced link capacities. The objective is to develop a multi-flow transmission strategy that routes flows across the wireless interference network to maximize the network utility. However, obtaining an optimal solution is computationally expensive due to the large state and action spaces involved. To tackle this challenge, we introduce a novel algorithm called Dual-stage Interference-Aware Multi-flow Optimization of Network Data-signals (DIAMOND). The design of DIAMOND allows for a hybrid centralized-distributed implementation, which is a characteristic of 5G and beyond technologies with centralized unit deployments. A centralized stage computes the multi-flow transmission strategy using a novel design of graph neural network (GNN) reinforcement learning (RL) routing agent. Then, a distributed stage improves the performance based on a novel design of distributed learning updates. We provide a theoretical analysis of DIAMOND and prove that it converges to the optimal multi-flow transmission strategy as time increases. We also present extensive simulation results over various network topologies (random deployment, NSFNET, GEANT2), demonstrating the superior performance of DIAMOND compared to existing methods.

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy considerations, and communication resources. In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. The server updates the global model and broadcasts it back to the clients. The client selection (CS) problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for CS to minimize the training latency without harming the ability of the model to generalize, i.e., to give reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). We analyze BSFL theoretically, and show that it achieves a logarithmic regret, defined as the loss of BSFL as compared to a genie that has complete knowledge about the latency means of all clients. Furthermore, simulation results using synthetic and real datasets demonstrate that BSFL is superior to existing methods.

We consider a distributed learning problem in a communication network, consisting of N distributed nodes and a central parameter server (PS). The computation is made by the PS and is based on received data from the nodes which transmit over a multiple access channel (MAC). The objective function is a sum of the nodes’ local loss functions. This problem has attracted a growing interest in distributed sensing systems, and more recently in federated learning (FL). However, existing methods rely on the assumption that the loss functions are continuously differentiable. In this paper, we first tackle the problem when this assumption does not necessarily hold. We develop a novel algorithm, dubbed Sub-Gradient descent Multiple Access (SGMA), to solve the learning problem over MAC. In SGMA, each node transmits an analog shaped waveform of its local subgradient over MAC and the PS receives a superposition of the noisy analog signals, resulting in a bandwidth-efficient over-the-air (OTA) computation used to update the learned model. We analyze the performance of SGMA, and prove that it approaches the convergence rate of the centralized subgradient algorithm in large networks. Simulation results using real datasets demonstrate the efficiency of SGMA.

We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users (PUs) and secondary users (SUs), where only partial observations are available at the SUs due to narrowband sensing and transmissions. The network operates in a time-slotted regime, where the traffic patterns of the PUs are modeled as finite-memory Markov chains, that are unknown to the SUs. Since observations are partial, then both channel sensing and access actions affect the throughput. Focusing on the case in which there is a single SU, our objective is to maximize the SU’s long-term throughput. To that aim, we develop a novel algorithm that learnsbothaccess and sensing policies via deep Q-learning, dubbed Double Deep Q-network for Sensing and Access (DDQSA). To the best of our knowledge, this is the first work that jointly optimizes both sensing and access policies for DSA via deep Q-learning. Next, we consider wireless networks with access policy which implements a fixed channel hopping dynamics, for which we analytically determine the optimal SU sensing and access policy and its associated throughput. Then, we demonstrate that indeed, the proposed DDQSA algorithm can achieve near-optimal performance for the considered network. Our results show that the proposed DDQSA algorithm learns a policy that implements both sensing and channel access, which significantly outperforms existing approaches, and can achieve the optimal performance in certain scenarios.

We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards process of each arm evolves according to an unknown Markovian rule, which is non-identical among different arms. At each time, a player chooses an arm out of N arms to play, and receives a random reward from a finite set of reward states. The arms are restless, that is, their local state evolves regardless of the player’s actions. The objective is an arm-selection policy that minimizes the regret, defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time t the arm that maximizes the expected immediate value. We develop the Learning under Exogenous Markov Process (LEMP) algorithm, that achieves a logarithmic regret order with time, and a finite-sample bound on the regret is established. Simulation results support the theoretical study and demonstrate strong performances of LEMP.

This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. In this paper we study dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users (PUs) and a secondary user (SU) which has only partial observations. The traffic patterns of the PUs are modeled as finite-memory Markov chains, and are unknown to the SU. It is noted that as observations are partial, then both channel sensing and channel access actions affect the throughput. Our objective in this work is to design a DSA algorithm such that the SU’s long-term throughput is maximized. To that aim, we show theoretically that the DSA problem can be formulated as a single-agent problem with a single policy for both sensing and access, and propose a novel algorithm that learns both the optimal access policy and the optimal sensing policy via deep Q-learning, which is referred to as Double Deep Q-network for Sensing and Access (DDQSA). To the best of our knowledge, this is the first instance of a deep Q-learning-based DSA algorithm, which learns both sensing and access policies. Our results show that the DDQSA algorithm learns a policy that implements both sensing and channel access, and achieves significantly better performance compared to existing approaches.

Anomaly detection among a large number of processes arises in many applications ranging from dynamic spectrum access to cybersecurity. In such problems one can often obtain noisy observations aggregated from a chosen subset of processes that conforms to a tree structure. The distribution of these observations, based on which the presence of anomalies is detected, may be only partially known. This gives rise to the need for a search strategy designed to account for both the sample complexity and the detection accuracy, as well as cope with statistical models that are known only up to some missing parameters. In this work we propose a sequential search strategy using two variations of the Generalized Log Likelihood Ratio statistic. Our proposed Hierarchical Dynamic Search (HDS) strategy is shown to be order-optimal with respect to the size of the search space and asymptotically optimal with respect to the detection accuracy. An explicit upper bound on the error probability of HDS is established for the finite sample regime. Extensive experiments are conducted, demonstrating the performance gains of HDS over existing methods.

The Corona virus disease 2019 (COVID-19) has significantly affected lives of people around the world. Today, isolation policy is mostly enforced by identifying infected individuals based on symptoms when these appear or by testing people and quarantining those who have been in close contact with infected people. In addition, many countries have imposed complete or partial lock-downs to control the spread of the disease. While lock-downs have succeeded to slow down the spread of the virus, they have devastating effects on the economy and social life. We argue that controlling the spread of the virus can be done by using active feedback to control testing for infection by actively testing individuals with a high probability of being infected. We develop an active testing strategy to achieve this goal, and demonstrate that it would have tremendous success in controlling the spread of the virus. Our results show up to a 50% reduction in quarantine rate and morbidity rate in typical settings as compared to existing methods.

The problem of searching for L anomalous processes among M processes is considered. At each time, the decision maker can observe a subset of K processes (i.e., multiple plays). The measurement drawn when observing a process follows one of two different distributions, depending whether the process is normal or abnormal. The goal is to design a policy that minimizes the Bayes risk which balances between the sample complexity, detection errors, and the switching cost associated with switching across processes. We develop a policy, dubbed consecutive controlled sensing (CCS), to achieve this goal. We prove theoretically that CCS is asymptotically optimal in terms of minimizing the Bayes risk as the sample complexity approaches infinity. Simulation results demonstrate strong performance of CCS in the finite regime as well.

We consider the adaptive routing problem in multihop wireless networks. The link states are assumed to be random variables drawn from unknown distributions, independent and identically distributed across links and time. This model has attracted a growing interest recently in cognitive radio networks and adaptive communication systems. In such networks, devices are cognitive in the sense of learning the link states and updating the transmission parameters to allow efficient resource utilization. This model contrasts sharply with the vast literature on routing algorithms that assumed complete knowledge about the link state means. The goal is to design an algorithm that learns online optimal paths for data transmissions to maximize the network throughput while attaining low path cost over flows in the network. We develop a novel Online Learning for Shortest path and Backpressure (OLSB) algorithm to achieve this goal. We show theoretically that OLSB achieves a logarithmic regret, defined as the loss of an algorithm as compared to a genie that has complete information about the link state means. Simulation results support the theoretical findings and demonstrate strong performance of the OLSB algorithm.

We consider the problem of anomaly detection among a large number of processes, where the probabilistic models of anomalies are unknown. At each time, aggregated noisy observations can be taken from a chosen subset of processes, where the chosen subset conforms to a tree structure. The observation distribution depends on the chosen subset and the absence/presence of anomalies. We develop a sequential search strategy using a hierarchical Kolmogorov-Smirnov (KS) statistics. Referred to as Tree-based Anomaly Search using KS statistics (TASKS), the proposed strategy is order-optimal with respect to the size of the search space and the detection accuracy.

The problem of detecting a few anomalous processes among a large number of data streams is considered. At each time, aggregated observations can be taken from a chosen subset of the processes, where the chosen subset conforms to a given tree structure. The random observations are drawn from a general distribution that may depend on the size of the chosen subset and the number of anomalous processes in the subset. We propose a sequential search strategy by devising an information-directed random walk on the tree-structured observation hierarchy. The proposed policy is shown to be asymptotically optimal with respect to the detection accuracy and order-optimal with respect to the size of the search space. Effectively localizing the data processing to small subsets of the search space, the proposed strategy is also efficient in terms of computation and memory requirement.

Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchanging their trained model with a central server, allowing to tune a global model without having the users share their possibly private data. A major challenge in FL is to reduce the bandwidth and energy consumption due to the repeated transmissions of large volumes of data by a large number of users over the wireless channel. Recently, over-the-air (OTA) FL has been suggested to achieve this goal. In this setting, all users transmit their data signal simultaneously over a Multiple Access Channel (MAC), and the computation is done over the wireless channel. In this paper, we develop a novel convergent OTA FL (COTAF) algorithm, which induces precoding and scaling upon transmissions to gradually mitigate the effect of the noisy channel, thus facilitating FL convergence. We analyze the convergence of COTAF to the loss minimizing model theoretically, showing its ability to achieve a convergence rate similar to that achievable over error-free channels. Our simulations demonstrate the improved convergence of COTAF for training using non-synthetic datasets.

An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. Furthermore, the proposed test offers considerable reduction in computation complexity.

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive, in general, due to the large-state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to the spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. The experimental results demonstrate the strong performance of the algorithm.

An active inference problem of detecting an anomalous process among M heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. Furthermore, the proposed test offers considerable reduction in implementation complexity.

We consider a binary hypothesis testing problem using Wireless Sensor Networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on an energy and spectrum efficient transmission scheme used to reduce the energy consumption and spectrum usage during the detection task. We propose a Density-Based Multiple Access (DBMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in DBMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. DBMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmissions saving and narrowband transmission over MAC. Second, it can be implemented by simple and dumb sensors (oblivious of observation statistics, and local data processing is not required) which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish both finite sample analysis and asymptotic analysis of the error probability with respect to the network size and provide conditions for obtaining exponential decay of the error. Numerical examples are provided to demonstrate the DBMA performance.

We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Markovian dynamic. The reward state of passive arms (which are not chosen to play at time t) evolves according to an arbitrary unknown random process. The objective is an arm-selection policy that minimizes the regret, defined as the reward loss with respect to a player that always plays the most rewarding arm. This class of RMAB problems has been studied recently in the context of communication networks and financial investment applications. We develop a strategy that selects arms to be played in a consecutive manner in which the selection sequencing rules are adaptively updated controlled by the current sample reward means, referred to as Adaptive Sequencing Rules (ASR) algorithm. By designing judiciously the adaptive sequencing rules of the chosen arms, we show that ASR algorithm achieves a logarithmic regret order with time and a finite-sample bound on the regret is established. Although existing methods have shown a logarithmic regret order with time in this RMAB setting, the theoretical analysis presents significant improvement in the regret scaling with respect to the system parameters under ASR. Extensive simulation results support the theoretical study and demonstrate strong performance of the algorithm as compared to existing methods.

We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels, and the users access the spectrum using a random access protocol. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain attempt probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is to find a multi-user strategy that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Experimental results have demonstrated that users are capable to learn good policies that achieve strong performance in this challenging partially observable setting only from their ACK signals, without online coordination, message exchanges between users, or carrier sensing.

The problem of detecting a few anomalous processes among a large number of M processes is considered. At each time, aggregated observations can be taken from a chosen subset of processes, where the chosen subset conforms to a given binary tree structure. The random observations are i.i.d. over time with a general distribution that may depend on the size of the chosen subset and the number of anomalous processes in the subset. The objective is a sequential search strategy that minimizes the sample complexity (i.e., the expected number of observations which represents detection delay) subject to a reliability constraint. A sequential test that results in a biased random walk on the tree is developed and is shown to be asymptotically optimal in terms of detection accuracy. Furthermore, it achieves the optimal logarithmic-order sample complexity in M provided that the Kullback-Liebler divergence between aggregated observations in the presence and the absence of anomalous processes are bounded away from zero at all levels of the tree structure as M approaches infinity. Sufficient conditions on the decaying rate of the aggregated observations to pure noise under which a sublinear scaling in M is preserved are also identified for the Bernoulli case.

The problem of least squares regression of a d-dimensional unknown parameter is considered. A stochastic gradient descent based algorithm with weighted iterate-averaging that uses a single pass over the data is studied and its convergence rate is analyzed. We first consider a bounded constraint set of the unknown parameter. Under some standard regularity assumptions, we provide an explicit O(1/k) upper bound on the convergence rate, depending on the variance (due to the additive noise in the measurements) and the size of the constraint set. We show that the variance term dominates the error and decreases with rate 1 /k, while the constraint set term decreases with rate log k/k2. We then compare the asymptotic ratio ρ between the convergence rate of the proposed scheme and the empirical risk minimizer (ERM) as the number of iterations approaches infinity. We show that ρ ≤ 4 under some mild conditions for all d ≥ 1. We further improve the upper bound by showing that ρ ≤ 4/3 for the case of d =1 and unbounded parameter set. Simulation results demonstrate strong performance of the algorithm as compared to existing methods, and coincide with ρ ≤ 4/3 even for large d in practice.

The problem of distributed rate maximization in multichannel ALOHA networks is considered. First, we study the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a best-response algorithm, where each user updates its strategy to increase its rate according to the channel state information and the current channel utilization. We prove the convergence of the algorithm to a Nash equilibrium in both homogeneous and heterogeneous networks using the theory of potential games. The performance of the best-response dynamic is analyzed and compared to a simple transmission scheme, where users transmit over the channel with the highest collision-free utility. Then, we consider the case where users are not restricted by transmission probability constraints. Distributed rate maximization under uncertainty is considered to achieve both efficiency and fairness among users. We propose a distributed scheme where users adjust their transmission probability to maximize their rates according to the current network state, while maintaining the desired load on the channels. We show that our approach plays an important role in achieving the Nash bargaining solution among users. Sequential and parallel algorithms are proposed to achieve the target solution in a distributed manner. The efficiencies of the algorithms are demonstrated through both theoretical and simulation results.

We consider distributed optimization over orthogonal collision channels in spatial multi-channel ALOHA networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. We develop simple distributed learning algorithms to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.

The problem of detecting a single anomalous process among a finite number M of processes is considered. At each time, a subset of the processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. This problem can be considered as a special case of active hypothesis testing first considered by Chernoff where a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically (as the error probability approaches zero) optimal. For the special case considered in this paper, we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime. We further extend the problem to the case where multiple anomalous processes are present. In particular, we examine the case where only an upper bound on the number of anomalous processes is known.

In this paper we consider the problem of distributed throughput maximization of cognitive radio networks with the multi-channel ALOHA medium access protocol. First, we characterize the Nash Equilibrium Points (NEPs) of the network when users solve an unconstrained rate maximization (i.e., the total transmission probability equals one). Then, we focus on constrained rate maximization, where user rates are subject to a total transmission probability constraint. We propose a simple best-response algorithm that solves the constrained rate maximization, where each user updates its strategy using its local channel state information (CSI) and by monitoring the channel utilization. We prove the convergence of the proposed algorithm using the theory of potential games. Furthermore, we show that the network approaches a unique equilibrium as the number of users increases. Then, we formulate the problem of choosing the access probability as a leader-followers Stackelberg game, where a single user is chosen to be the leader to manage the network. We show that a fully distributed setup can be applied to approximately optimize the network throughput for a large number of users. Finally, we extend the model to the case where primary and secondary users co-exist in the same frequency band.

In this paper, we consider the binary hypothesis testing problem using wireless sensor networks. We analyze the case where sensors transmit their local log-likelihood ratio (LLR) directly to a fusion center (FC) using an analog transmission scheme over multiple-access fading channels. Due to the nature of the wireless medium, the FC receives a superposition of sensor transmissions. The decision is made by the FC and is based on received data from the sensors. In contrast to the case of identical channels and i.i.d observations, the analog transmission of the LLR over multiple-access fading channels does not achieve the centralized error exponent. Large deviation tools are used in this paper to characterize the error exponent in the asymptotic regime (when the number of sensors approaches infinity) in the case of non-i.i.d observations and non-i.i.d fading channels. Chernoff bounding techniques are used to provide bounds on the error probability for a finite number of sensors when the observations and the fading channels are independent across sensors. Specific performance analysis is provided for detection over both i.i.d and spatially correlated Markovian fading channels. Simulation results then illustrate the detector's performance.

In this paper we investigate transmission scheduling by Medium Access Control (MAC) for energy-efficient detection using Wireless Sensor Networks (WSN). We consider the binary hypothesis testing problem. The decision is made by an access point and is based on received data from sensors that transmit through a fading channel. We study the significance of exploiting both Channel-State Information (CSI) and Likelihood-Ratio Information (LRI) to design an adequate MAC protocol that minimizes the total transmission energy required for optimal detection. We formulate the access problem as a history-dependent decision process. The optimal solution is mathematically intractable and suffers from exponential complexity as a function of model size. Hence, we propose an approximate solution using the Markov property to reduce complexity and make the problem mathematically tractable. We designed the LRI and CSI Based Access (LCBA) protocol based on this solution. The LCBA protocol trades off between LRI and CSI to reduce the total transmission energy. Simulation results show a significant performance gain of LCBA over existing approaches.

We consider transmission scheduling by medium access control (MAC) protocols for energy limited wireless sensor networks (WSN) in order to maximize the network lifetime. Time-varying opportunistic protocol (TOP) for maximizing the network lifetime is proposed. By executing TOP each sensor exploits local channel state information (CSI) and local residual energy information (REI). TOP implements opportunistic strategy in terms of favoring sensors with better channels when the network is young, while less opportunistic and more conservative strategy in terms of prioritizing sensors with higher residual energy when the network is old. TOP significantly simplifies the implementation of carrier sensing as compared to other distributed MAC protocols. Simulation results show that TOP achieves significant performance gains over other distributed MAC protocols.