Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private data. Existing wireless FL works primarily adopt two communication strategies: (1) over-the-air (OTA) computation, which exploits wireless signal superposition for simultaneous gradient aggregation, and (2) digital communication, which allocates orthogonal resources for gradient uploads. Prior work on OTA and digital FL either enforces zero bias (explicitly or via assumedhomogeneouspath loss) or permits uncontrolled bias, yielding high-variance updates underheterogeneouschannels and creating a performance bottleneck due to devices with poor channel conditions. We propose wireless FL updates that admit a structured, time-invariant model bias to achieve low-variance gradient aggregation, and analyze their convergence in a unified framework, in both strongly convex and non-convex settings. The resulting bounds reveal a biasvariance trade-off governed by the design parameters. To optimize this trade-off, we pose a non-convex joint design problem and develop a successive convex approximation framework to tune the parameters. Extensive experiments across heterogeneous wireless settings, covering both strongly convex and non-convex image classification tasks, compare the proposed OTA and digital designs against state-of-the-art baselines. The results demonstrate that optimizing the bias–variance trade-off through a structured bias yields faster FL convergence and improved generalization over existing schemes.

Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge server. By exploiting binary dithering and a longterm memory based error-compensation mechanism, NCAirFL achieves a convergence rate of order$\mathcal{O}(1 / \sqrt{T})$in terms of the average square norm of the gradient for general non-convex and smooth objectives, where$T$is the number of communication rounds. Experiments demonstrate the competitive performance of NCAirFL compared to vanilla FL with ideal communications and to coherent transmission-based benchmarks.

In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks, by optimizing the placement of unmanned aerial vehicle (UAV) relays, relay beamforming, and receive combining. The design is separated into two problems: beamforming-aware UAV placement optimization and transceiver design for minimum SINR maximization. A significant challenge in beamforming-aware UAV placement optimization is the lack of instantaneous channel state information (CSI) prior to deploying UAV relays, making it difficult to derive the beamforming SINR in non-orthogonal multi-user transmission. To address this issue, an approximation of the expected beamforming SINR is derived using the narrow beam property of a massive MIMO base station. Based on this, a UAV placement algorithm is proposed to provide UAV positions that improve the minimum expected beamforming SINR among users, using a difference-of-convex framework. Subsequently, after deploying the UAV relays to the optimized positions, and with estimated CSI available, a joint relay beamforming and receive combining (JRBC) algorithm is proposed to optimize the transceiver to improve the minimum beamforming SINR among users, using a block-coordinate descent approach. Numerical results show that the UAV placement algorithm combined with the JRBC algorithm provides a 4.6 dB SINR improvement over state-of-the-art schemes.

We propose a novel physical layer scheme for decentralized learning over wirelessly connected, serverless systems operating under frequency-selective channels. To achieve scalability with respect to the number of devices, we exploit the waveform superposition properties of wireless channels: devices map their local optimization signals to energy levels across OFDM subcarriers, and transmit simultaneously; each receiver then computes the energy received on each subcarrier, and leverages a non-coherent energy-superposition technique to estimate the weighted disagreement signal, used in conjunction with a decentralized gradient descent algorithm. To enable CSI-free operation over a broad class of frequency-selective channels, including static ones as a special case, we propose two mechanisms: independent phase shifts and coordinated subcarrier shifts at the transmitters. We show that these mechanisms ensure an unbiased estimate of the weighted disagreement signal, with weights given by the average channel gain across subcarriers. We also provide a bound on the variance of this estimate.

Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices, via iterative local updates (at devices) and global aggregations (at the server). In this paper, we develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions: (i) Network, allowing decentralized cooperation among the devices via device-to-device (D2D) communications. (ii) Heterogeneity, interpreted at three levels: (ii-a) Learning: PSL considers heterogeneous number of stochastic gradient descent iterations with different mini-batch sizes at the devices; (ii-b) Data: PSL presumes a dynamic environment with data arrival and departure, where the distributions of local datasets evolve over time, captured via a new metric for model/concept drift. (ii-c) Device: PSL considers devices with different computation and communication capabilities. (iii) Proximity, where devices have different distances to each other and the access point. PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning. We then propose network-aware dynamic model tracking to optimize the model learning vs. resource efficiency tradeoff, which we show is an NP-hard signomial programming problem. We finally solve this problem through proposing a general optimization solver. Our numerical results reveal new findings on the interdependencies between the idle times in-between the global aggregations, model/concept drift, and D2D cooperation configuration.

In this paper, we address the problem of estimating the delays and Doppler shifts introduced by a sparse wireless channel for orthogonal time frequency space (OTFS) modulation. We show that in the discrete time-frequency (TF) domain, the received signal resulting from an OTFS pilot signal is a superposition of two-dimensional (2D) complex exponentials, where the 2D frequencies of the complex exponentials are given by the delays and Doppler shifts of the scatterers. Thus, estimating the delays and Doppler shifts can be formulated as a 2D sinusoidal frequency estimation problem. We apply a 2D TF domain version of the well-known MUSIC algorithm to recover the delays and Doppler shifts. Since the reduced guard interval frame structure eliminates most of the interference between the pilot and data symbols, the data symbols can be filtered out from the received signal such that only the pilot signal component is used for the 2D MUSIC algorithm.

This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1°, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU-R M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28 GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential models for the spatial/angular autocorrelation coefficient under distance and alignment effects.

This paper presents NCOTA-DGD, a Decentralized Gradient Descent (DGD) algorithm that combines local gradient descent with a novel Non-Coherent Over-The-Air (NCOTA) consensus scheme to solve distributed machine-learning problems over wirelessly-connected systems. NCOTA-DGD leverages the waveform superposition properties of the wireless channels: it enables simultaneous transmissions under half-duplex constraints, by mapping local optimization signals to a mixture of preamble sequences, and consensus via non-coherent combining at the receivers. NCOTA-DGD operates without channel state information at transmitters and receivers, and leverages the average channel pathloss to mix signals, without explicit knowledge of the mixing weights (typically known in consensus-based optimization algorithms). It is shown both theoretically and numerically that, for smooth and strongly-convex problems with fixed consensus and learning stepsizes, the updates of NCOTA-DGD converge in Euclidean distance to the global optimum with rate$\mathrm{O}(K^{-1/4})$for a target of$K$iterations. NCOTA-DGD is evaluated numerically over a logistic regression problem, showing faster convergence vis-á-vis running time than implementations of the classical DGD algorithm over digital and analog orthogonal channels.

6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existing challenges of mmWave communication and adds new challenges associated with the wider bandwidths, more antennas, and harsher propagations. Notably, the frequency- and spatial-wideband (dual-wideband) effects are significant at sub-THz. This paper presents a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels. A set of frequency-dependent array response matrices are constructed, enabling channel recovery from multiple observations across subcarriers via multiple measurement vectors (MMV). Using the temporal correlation, MMV least squares (LS) is designed to estimate the channel based on the previous beam support, and MMV compressed sensing (CS) is applied to the residual signal. We refer to this as the MMV-LS-CS framework. Two-stage (TS) and MMV FISTA-based (M-FISTA) algorithms are proposed for the MMV-LS-CS framework. Leveraging the spreading loss structure, a channel refinement algorithm is proposed to estimate the path coefficients and time delays of the dominant paths. To reduce the computational complexity and enhance the beam resolution, a sequential search method using hierarchical codebooks is developed. Numerical results demonstrate the improved channel estimation accuracy of MMV-LS-CS over state-of-the-art techniques.

Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a learning and adaptation framework in which the dynamics of the communication beams are learned and then exploited to design adaptive beam-tracking and training with low overhead: on a long-timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training feedback to learn a probabilistic model of beam dynamics and enable predictive beam-tracking; on a short-timescale, an adaptive beam-training procedure is formulated as a partially observable (PO-) Markov decision process (MDP) and optimized viapoint-based value iteration(PBVI) by leveraging beam-training feedback and a probabilistic prediction of the strongest beam pair provided by the DR-VAE. In turn, beam-training feedback is used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. The proposed DR-VAE learning framework learns accurate beam dynamics: it reduces the Kullback-Leibler divergence between the ground truth and the learned model of beam dynamics by ~95% over the Baum-Welch algorithm and a naive learning approach that neglects feedback errors. Numerical results on a line-of-sight scenario with multipath and 3D beamforming reveal that the proposed dual timescale approach yields near-optimal spectral efficiency, and improves it by 130% over a policy that scans exhaustively over the dominant beam pairs, and by 20% over a state-of-the-art POMDP policy. Finally, a low-complexity policy is proposed by reducing the POMDP to an error-robust MDP, and is shown to perform well in regimes with infrequent feedback errors.

This paper studies distributed algorithms for (strongly convex) composite optimization problems over mesh networks, subject to quantized communications. Instead of focusing on a specific algorithmic design, a black-box model is proposed, casting linearly convergent distributed algorithms in the form of fixed-point iterates. The algorithmic model is equipped with a novel random or deterministic Biased Compression (BC) rule on the quantizer design, and a new Adaptive encoding Non-uniform Quantizer (ANQ) coupled with a communication-efficient encoding scheme, which implements the BC-rule using a finite number of bits (below machine precision). This fills a gap existing in most state-of-the-art quantization schemes, such as those based on the popular compression rule, which rely on communication of some scalar signals with negligible quantization error (in practice quantized at the machine precision). A unified communication complexity analysis is developed for the black-box model, determining the average number of bits required to reach a solution of the optimization problem within a target accuracy. It is shown that the proposed BC-rule preserves linear convergence of the unquantized algorithms, and a trade-off between convergence rate and communication cost under ANQ-based quantization is characterized. Numerical results validate our theoretical findings and show that distributed algorithms equipped with the proposed ANQ have more favorable communication cost than algorithms using state-of-the-art quantization rules.

Federated learning has generated significant interest, with nearly all works focused on a “star” topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through thenetworkdimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra-and inter-layer model learning that considers the network as amulti-layer cluster-based structure.MH-FLconsiders thetopology structuresamong the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes asemi-decentralized architecturefor federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to formlocal consensuson the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence forMH-FLwith respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm forMH-FLto tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages ofMH-FLin terms of resource utilization metrics.

In this work, a compressed sensing-aided wideband MIMO-OFDM channel training framework is proposed to reduce the training overhead in slowly-varying channels with frequency- and spatial-wideband (dual-wideband) effects. To combat the beam squint effect, a set of frequency-dependent array response matrices are constructed, enabling the recovery of the sparse beamspace channel from multiple observations across OFDM subcarriers, via multiple measurement vectors (MMV). A channel training algorithm (MMV-LS-CS) is proposed to estimate slowly-varying multipath channel parameters: MMV least squares (MMV-LS) is first used to estimate the channel on the previous beam index support, followed by MMV compressed sensing (MMV-CS) on the residual to estimate the time-varying multipath components. Finally, a channel refining algorithm is proposed to estimate the gains and time delays of the dominant channel paths jointly on pilot subcarriers. Numerical results show that MMV-LS-CS achieves more accurate and robust channel estimation than the state-of-the-art approach on slowly-varying dual-wideband MIMO-OFDM: given a moderate SNR of 20 dB, our algorithm attains$\text{NMSE}=0.15$, as opposed to the state-of-the-art which attains$\text{NMSE}=0.43$in the same configuration. Besides, MMV-LS-CS necessitates$\text{SNR} =14\ \text{dB}$to achieve the spectral efficiency of 6 bit/s/Hz/stream, while the state-of-the-art scheme needs$\text{SNR}=17\ \text{dB}$to attain the same spectral efficiency.

This paper proposes a dual timescale learning and adaptation framework to learn a probabilistic model of beam dynamics and concurrently exploit this model to design adaptive beam-training with low overhead: on a long timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training observations to learn a probabilistic model of beam dynamics; on a short timescale, an adaptive beam-training procedure is formulated as a partially observable Markov decision process and optimized using point-based value iteration by leveraging beam-training feedback and probabilistic predictions of the strongest beam pair provided by the DR-VAE. In turn, beam-training observations are used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. It is shown that the proposed DR-VAE learning framework learns accurate beam dynamics and, as learning progresses, the training overhead decreases and the spectral efficiency increases. Moreover, the proposed dual timescale approach achieves near-optimal spectral efficiency, with a gain of 85% over a policy that scans exhaustively over the dominant beam pairs, and of 18% over a state-of-the-art POMDP policy.

Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology be-tween the devices and a central server. In this paper, we propose two timescale hybrid federated learning (TT-Hf),which migrates to a more distributed topology via device-to-device (D2D) communications. In TT-HF, local model training occurs at devices via successive gradient iterations, and the synchronization process occurs at two timescales: (i) macro-scale, where global aggregations are carried out via device-server interactions, and (ii) micro-scale, where local aggregations are carried out via D2D cooperative consensus formation in different device clusters. Our theoretical analysis reveals how device, cluster, and network-level parameters affect the convergence of TT-HF, and leads to a set of conditions under which a convergence rate of O(1/t) is guaranteed. Experimental results demonstrate the improvements in convergence and utilization that can be obtained by TT-HF over state-of-the-art federated learning baselines.

In this paper, a novel spectrum sensing and access strategy based on approximate Partially Observable Markov Decision Processes (POMDPs) is proposed, wherein a cognitive radio learns the time-frequency correlation model defining the occupancy behavior of incumbents, via the Baum-Welch algorithm, and concurrently devises an optimal strategy to perform spectrum sensing and access that exploits this learned correlation model. To ameliorate the complexity of the POMDP optimization, the PERSEUS algorithm, a randomized point-based value iteration method, is designed, with fragmentation and Hamming distance state filters. Evaluating the cognitive radio throughput against incumbent interference, we demonstrate that, with sensing restrictions, our framework achieves a 6% performance gain over that attained by a maximum a-posteriori (MAP) state estimator with prior model knowledge, and outperforms correlation-coefficient based clustering algorithms by an average of 60%; additionally, it surpasses a Neyman-Pearson Detector that assumes independence among channels with no sensing restrictions, by an average of 25%. Furthermore, unlike state-of-the-art algorithms, the proposed design facilitates the regulation of the trade-off between cognitive radio throughput and incumbent interference via a penalty parameter in the underlying MDP.

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we proposetwo timescale hybrid federated learning(TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. InTT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior ofTT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period ofTT-HFover time to target a sublinear convergence rate of$\mathcal {O}(1/t)$while minimizing network resource utilization. Our subsequent experiments demonstrate thatTT-HFsignificantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.

Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an important consideration of federated learning at the network edge: communication delays between the edge nodes and the aggregator. A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step. Through theoretical analysis, an upper bound is derived on the global model loss achieved by FedDelAvg, which reveals a strong dependency of learning performance on the values of the weighting and learning rate. Experimental results on a popular ML task indicate significant improvements in terms of convergence speed when optimizing the weighting scheme to account for delays.

This paper studies the adaptive trajectory design of a rotary-wing UAV serving as a relay between ground nodes dispersed in a circular cell and a central base station. Assuming the ground nodes generate uplink data transmissions randomly according to a Poisson process, we seek to minimize the expected average communication delay to service the data transmission requests, subject to an average power constraint on the mobility of the UAV. The problem is cast as a semi-Markov decision process, and it is shown that the policy exhibits a two-scale structure, which can be efficiently optimized: in the outer decision, upon starting a communication phase, and given its current radius, the UAV selects a target end radius position so as to optimally balance a trade-off between average long-term communication delay and power consumption; in the inner decision, the UAV selects its trajectory between the start radius and the selected end radius, so as to greedily minimize the delay and energy consumption to serve the current request. Numerical evaluations show that, during waiting phases, the UAV circles at some optimal radius at the most energy efficient speed, until a new request is received. Lastly, the expected average communication delay and power consumption of the optimal policy is compared to that of static and mobile heuristic schemes, demonstrating a reduction in latency by over 50% and 20%, respectively.

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on-the-field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users' positions, given received power measurements on a small subset of positions. Based on these predictions and on positional side information, a small subset of beams is recommended to reduce the training overhead of beam-alignment. Numerical results evaluated with the Quadriga channel simulator demonstrate that the proposed algorithm achieves correct alignment with high probability using small training overhead: given power measurement on only 20% of the possible positions when using a discrete coverage area, our algorithm attains a probability of correct alignment of 80%, with only 2% of trained beams, as opposed to a state-of-the-art scheme which achieves 50% correct alignment in the same configuration. To the best of our knowledge, this is the first work to consider the beam recommendation problem based on measurements collected on a small subset of positions.

Mobility may degrade the performance of next-generation vehicular networks operating at the millimeter-wave spectrum: frequent loss of alignment and blockages require repeated beam training and handover, thus incurring huge overhead. In this paper, an adaptive and joint design of beam training, data transmission and handover is proposed, that exploits the mobility process of mobile users and the dynamics of blockages to optimally trade-off throughput and power consumption. At each time slot, the serving base station decides to perform either beam training, data communication, or handover when blockage is detected. The problem is cast as a partially observable Markov decision process, and solved via an approximate dynamic programming algorithm based on PERSEUS [2]. Numerical results show that the PERSEUS-based policy performs near-optimally, and achieves a 55% gain in spectral efficiency compared to a baseline scheme with periodic beam training. Inspired by its structure, an adaptive heuristic policy is proposed with low computational complexity and small performance degradation.

This paper studies the trajectory optimization problem in a scenario where a single rotary-wing UAV acts as a relay of data payloads for downlink transmission requests generated randomly by two ground nodes (GNs) in a wireless network. The goal is to optimize the UAV trajectory in order to minimize the expected average communication delay to serve these random requests. It is shown that the problem can be cast as a semi-Markov decision process (SMDP), and the resulting minimization problem is solved via multi- chain policy iteration. The optimality of a two-scale optimization approach is proved: the optimal trajectory in the communication phase greedily minimizes the communication delay of the current request while moving between the current start position and a target end position (inner optimization); the end positions are selected to minimize the expected average long-term delay in the SMDP (outer optimization). Numerical simulations show that the expected average delay is minimized when the UAV moves towards the geometric center of the GNs during phases in which it is not actively servicing transmission requests, and demonstrate significant improvements over sensible heuristics. Finally, it is revealed that the optimal end positions of communication phases become increasingly independent of the data payload, for large data payload values.

Millimeter-wave (mm-wave) systems rely on narrow-beams to cope with the severe signal attenuation in the mm-wave frequency band. However, susceptibility to beam mis- alignment due to mobility or blockage requires the use of beam-alignment schemes, with huge cost in terms of overhead and use of system resources. In this paper, a beam-alignment scheme is proposed based on Bayesian multi-armed bandits, with the goal to maximize the alignment probability and the data-communication throughput. A Bayesian approach is proposed, by considering the state as a posterior distribution over angles of arrival (AoA) and of departure (AoD), given the history of feedback signaling and of beam pairs scanned by the base- station (BS) and the user-end (UE). A simplified sufficient statistic for optimal control is identified, in the form of preference of BS-UE beam pairs. By bounding a value function, the second-best preference policy is formulated, which strikes an optimal balance between exploration and exploitation by selecting the beam pair with the current second-best preference. Through Monte-Carlo simulation with analog beamforming, the superior performance of the second-best preference policy is demonstrated in comparison to existing schemes based on first-best preference, linear Thompson sampling, and upper confidence bounds, with up to 7%, 10% and 30% improvements in alignment probability, respectively.

Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors, and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0 dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network.

Millimeter-wave will be a key technology in next-generation wireless networks thanks to abundant bandwidth availability. However, the use of large antenna arrays with beamforming demands precise beam alignment between the transmitter and the receiver and may entail huge overhead in mobile environments. This paper investigates the design of an optimal interactive beam alignment and data communication protocol, with the goal of minimizing power consumption under a minimum rate constraint. The base station selects beam alignment or data communication and the beam parameters, based on the feedback from the user end. Based on the sectored antenna model and uniform prior on the angles of departure and arrival (AoD/AoA), the optimality of a fixed-length beam-alignment phase followed by a data-communication phase is demonstrated. Moreover, a decoupled fractional beam-alignment method is shown to be optimal, which decouples the alignment of AoD and AoA over time, and iteratively scans a fraction of their region of uncertainty. A heuristic policy is proposed for non-uniform prior on AoD/AoA, with provable performance guarantees, and it is shown that the uniform prior is the worst-case scenario. The performance degradation due to detection errors is studied analytically and via simulation. The numerical results with analog beams depict up to 4dB, 7.5dB, and 14dB gains over a state-of-the-art bisection method and conventional and interactive exhaustive search policies, respectively, and demonstrate that the sectored model provides valuable insights for beam-alignment design.

Future vehicular communication networks call for new solutions to support their capacity demands, by leveraging the potential of the millimeter-wave (mm-wave) spectrum. Mobility, in particular, poses severe challenges in their design, and as such shall be accounted for. A key question in mm-wave vehicular networks is how to optimize the trade-off between directive Data Transmission (DT) and directional Beam Training (BT), which enables it. In this paper, learning tools are investigated to optimize this trade-off. In the proposed scenario, a Base Station (BS) uses BT to establish a mm-wave directive link towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off, a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where the system state corresponds to the position of the MU within the road link. The goal is to maximize the number of bits delivered by the BS to the MU over the communication session, under a power constraint. The resulting optimal policies reveal that adaptive BT/DT procedures significantly outperform common-sense heuristic schemes, and that specific mobility features, such as user position estimates, can be effectively used to enhance the overall system performance and optimize the available system resources.

Millimeter-wave (mm-wave) communications incur a high beam alignment cost in mobile scenarios such as vehicular networks. Therefore, an efficient beam alignment mechanism is required to mitigate the resulting overhead. In this paper, a one-dimensional mobility model is proposed where a mobile user (MU), such as a vehicle, moves along a straight road with time-varying and random speed, and communicates with base stations (BSs) located on the roadside over the mm-wave band. To compensate for location uncertainty, the BS widens its transmission beam and, when a critical beamwidth is achieved, it performs beam-sweeping to refine the MU position estimate, followed by data communication over a narrow beam. The average rate and average transmission power are computed in closed form and the optimal beamwidth for communication, number of sweeping beams, and transmission power allocation are derived so as to maximize the average rate under an average power constraint. Structural properties of the optimal design are proved, and a bisection algorithm to determine the optimal sweeping - communication parameters is designed. It is shown numerically that an adaptation of the IEEE 802.11ad standard to the proposed model exhibits up to 90\% degradation in spectral efficiency compared to the proposed scheme.

This paper presents millimeter wave propagation measurements at 28 GHz for a typical suburban environment using a 400-megachip-per-second custom- designed broadband sliding correlator channel sounder and highly directional 22-dBi (15° half-power beamwidth) horn antennas. With a 23-dBm transmitter installed at a height of 27m to emulate a microcell deployment, the receiver obtained more than 5000 power delay profiles over distances from 80m to 1000m at 50 individual sites and on two pedestrian paths. The resulting basic transmission losses were compared with predictions of the over-rooftop model in recommendation ITU-R P.1411-9. Our analysis reveals that the traditional channel modeling approach may be insufficient to deal with the varying site-specific propagations of millimeter waves in suburban environments. For line-of-sight measurements, the path loss exponents obtained for the close-in (CI) free space reference distance model and the alpha-beta-gamma (ABG) model are 2.00 and 2.81, respectively, which are close to the recommended site-general value of 2.29. The root mean square errors (RMSEs) for these two reference models are 9.93dB and 9.70dB, respectively, which are slightly lower than that for the ITU site-general model (10.34dB). For non-line-of-sight measurements, both reference models, with the resulting path loss exponents of 2.50 for the CI model and 1.12 for the ABG model, outperformed the site-specific ITU model by around 14dB RMSE.

This paper investigates the design of access policies in spectrum sharing networks by exploiting the retransmission protocol of legacy primary users (PUs) to improve the spectral efficiency via opportunistic retransmissions at secondary users (SUs) and chain decoding. The optimal policy maximizing the SU throughput under an interference constraint to the PU and its performance are found in closed form. It is shown that the optimal policy randomizes among three modes: Idle, the SU remains idle over the retransmission window of the PU, to avoid causing interference; Interference Cancellation, the SU transmits only after decoding the PU packet, to improve its own throughput via interference cancellation; and Always Transmit, the SU transmits over the retransmission window of the PU to maximize the future potential of interference cancellation via chain decoding. This structure is exploited to design a stochastic optimization algorithm to facilitate learning and adaptation when the model parameters are unknown or vary over time, based on ARQ feedback from the PU and CSI measurements at the SU receiver. It is shown numerically that, for a 10% interference constraint, the optimal access policy yields 15% improvement over a state-of-the-art scheme without SU retransmissions, and up to 2× gain over a scheme using a non-adaptive access policy instead of the optimal one.

In this paper, a multi-scale approach to spectrum sensing in cognitive cellular networks is proposed. In order to overcome the huge cost incurred in the acquisition of full network state information, a hierarchical scheme is proposed, based on which local state estimates are aggregated up the hierarchy to obtain aggregate state information at multiple scales, which are then sent back to each cell for local decision making. Thus, each cell obtains fine-grained estimates of the channel occupancies of nearby cells, but coarse-grained estimates of those of distant cells. The performance of the aggregation scheme is studied in terms of the trade-off between the throughput achievable by secondary users and the interference generated by the activity of these secondary users to primary users. In order to account for the irregular structure of interference patterns arising from path loss, shadowing, and blockages, which are especially relevant in millimeter wave networks, a greedy algorithm is proposed to find a multi-scale aggregation tree to optimize the performance. It is shown numerically that this tailored hierarchy outperforms a regular tree construction by 60%.

This paper investigates the design of secondary access policies which exploit the temporal redundancy of the retransmission protocol employed by primary users (PU) to improve the spectral efficiency of wireless networks. Secondary users (SU) perform selective retransmissions in order to optimize the potential of interference cancellation at the receiver. The corrupted signals are selectively buffered at the SU receiver, and then decoded via the successive application of chain decoding [1]. The optimal SU access policy which maximizes the SU throughput under a constraint on the maximum interference caused to the PU is derived, and its performance is found in closed form. It is shown that such policy optimally randomizes among three modes of operation of the SU: 1) The SU remains idle over the entire retransmission interval of the PU, to avoid interfering with the PU; 2) The SU transmits only after decoding the PU packet to leverage interference cancellation; 3) The SU always transmits over the entire retransmission interval of the PU, so as to leverage chain decoding. The optimal randomization is determined by the constraint on the maximum interference caused to the PU. It is shown numerically that chain decoding attains a throughput gain of 15% with respect to a state-of-the art scheme where the SU does not perform selective retransmissions.

This paper develops a control framework for a network of energy harvesting nodes connected to a Base Station (BS) over a multiple access channel. Due to fluctuations in energy availability and, possibly, energy outages, the number of nodes attempting channel access is random and varies over time. Thus, each node must carefully adapt its access probability to the network state to optimize network performance. In order to reduce the complexity of network control, a lightweight and flexible design framework is proposed where energy storage dynamics are replaced by dynamic average power constraints at each node, induced by the time correlated energy supply. The BS adapts the access probability of the “active” nodes (those currently under a favorable energy harvesting state) so as to maximize throughput. The resulting policy takes the form of access probability as a function of the local energy harvesting state and number of active nodes. The structure of the throughput-optimal policy is analytically derived for the genie-aided case of non-causal knowledge of the number of active nodes. Inspired by it, a Bayesian estimation approach is presented for the more practical scenario where the BS estimates the number of active nodes. The proposed scheme is shown to outperform by 20% a scheme in which the nodes operate based only on local state information, and to be robust against the impact of energy storage dynamics at a fraction of the complexity.

Herein, we analyze the sample complexity of general weighted ℓ1minimization in terms of support recovery from noisy underdetermined measurements. This analysis generalizes prior work for standard ℓ1minimization by considering the weighting effect. We state explicit relationship between the weights and the sample complexity such that i.i.d random Gaussian measurement matrices used with weighted ℓ1minimization recovers the support of the underlying signal with high probability as the problem dimension increases. This result provides a measure that is predictive of relative performance of different algorithms. Motivated by the analysis, a new iterative weighted strategy is proposed. In the Reweighted Partial Support (RePS) algorithm, a sequence of weighted ℓ1minimization problems are solved where partial support recovery is used to prune the optimization; furthermore, the weights used for the next iteration are updated by the current estimate. RePS is compared to other weighted algorithms through the proposed measure and numerical results, which demonstrate its superior performance for a spectrum occupancy estimation problem motivated by cognitive radio.

Microbial communities play a significant role in bioremediation, plant growth, human and animal digestion, global elemental cycles including the carbon-cycle, and water treatment. They are also posed to be the engines of renewable energy via microbial fuel cells, which can reverse the process of electrosynthesis. Microbial communication regulates many virulence mechanisms used by bacteria. Thus, it is of fundamental importance to understand interactions in microbial communities and to develop predictive tools that help control them, in order to aid the design of systems exploiting bacterial capabilities. This position paper explores how abstractions from communications, networking and information theory can play a role in understanding and modeling bacterial interactions. In particular, two forms of interactions in bacterial systems will be examined: electron transfer and quorum sensing. While the diffusion of chemical signals has been heavily studied, electron transfer occurring in living cells and its role in cell-cell interaction is less understood. Recent experimental observations open up new frontiers in the design of microbial systems based on electron transfer, which may coexist with the more well-known interaction strategies based on molecular diffusion. In quorum sensing, the concentration of certain signature chemical compounds emitted by the bacteria is used to estimate the bacterial population size, so as to activate collective behaviors. In this position paper, queuing models for electron transfer are summarized and adapted to provide new models for quorum sensing. These models are stochastic, and thus capture the inherent randomness exhibited by cell colonies in nature. It is shown that queuing models allow the characterization of the state of a single cell as a function of interactions with other cells and the environment, thus enabling the construction of an information theoretic framework, while being amenable to complexity reduction using methods based on statistical physics and wireless network design.

This paper introduces a novel technique that enables access by a cognitive secondary user (SU) to a spectrum occupied by an incumbent primary user (PU) that employs Type-I hybrid automatic retransmission request (ARQ). The technique allows the SU to perform selective retransmissions of SU data packets, whose transmission previously failed. The temporal redundancy introduced by the PU ARQ protocol and by the selective retransmission process of the SU can be exploited by the SU receiver to perform interference cancellation (IC) over multiple transmission slots, thus creating a “clean” channel for the decoding of the concurrent SU or PU packets. The chain decoding (CD) technique is initiated by a successful decoding operation of an SU or a PU packet and proceeds by an iterative application of IC as previously buffered packets become decodable and their interference can be removed, thus making it possible to recover the concurrent data packets, and so on, until no more packets are decodable. Based on this scheme, an optimal policy is designed that maximizes the SU throughput under a constraint on the average long-term PU performance. The optimality of the CD protocol is proved, which determines which packet the SU should send at any given time, based on four basic rules. Moreover, a decoupling principle is proved, which establishes the optimality of decoupling the secondary access strategy from the CD protocol. Specifically, first, the SU access policy, optimized via dynamic programming, specifies whether the SU should access the channel or remain idle, based on a compact state representation of the protocol, and second, the CD protocol embeds four basic rules that are used to select the packet transmitted by the SU. It is shown numerically that CD outperforms by up to 35% other schemes considered in the literature, which do not employ retransmissions at the SU pair and thus do not exploit the full potentiality of IC.

This paper considers a wireless network composed of a pair of sensors powered by energy harvesting devices (EHDs), which transmit data to a receiver over a shared wireless channel. At any given time, based on the energy levels of the two rechargeable batteries of the sensors, a central controller (CC) decides on the amount of energy to be drawn from the two batteries and used for transmission. The problem considered is the maximization of the long-term average reward associated with data transmission, by optimizing the transmission strategy of the two nodes, in the case of a collision channel model and both i.i.d. and correlated energy arrivals. In addition, contrary to the traditional assumption that the amount of energy available to the sensors can be easily estimated, we derive the optimal policy in the cases where the state of charge (SOC) may not be perfectly known by the central controller, analyzing the performance degradation caused by this imperfect knowledge of the SOC. For this second scenario, supposing that the CC is only aware that each SOC is “LOW” or “HIGH,” we show that the impact of imperfect knowledge decreases with the two battery capacities and is negligible in most cases of practical interest.

In this paper, a multiscale approach to spectrum sensing in cognitive cellular networks is analyzed. Observing that wireless interference decays with distance, and that estimating the entire spectrum occupancy across the network entails substantial energy cost and communication overhead, a protocol for distributed spectrum estimation is defined by which secondary users maintain fine-grained estimates of the spectrum occupancy of nearby cells, but coarse-grained estimates of that of distant cells. This is accomplished by arranging the cellular network into a hierarchy of increasingly coarser macro-cells and having secondary users fuse local spectrum estimates up the hierarchy. The spectrum occupancy is modeled as a Markov process, and the system is optimized by defining a probabilistic framework for spectrum sensing and information exchange that balances improvements in spectrum estimation against energy costs. The performance of the multiscale scheme is evaluated numerically, showing that it offers substantial improvements in energy efficiency over local estimation. On the other hand, it is shown that schemes that attempt to estimate the state of the whole network perform poorly, due to the excessive cost of performing information exchange with far away cells, and to the fact that, knowing the spectrum occupancy of distant cells, which experience low interference levels, results in a small increase in reward.

Motivated by recent discoveries of multi-cellular microbial communities that transfer electrons across centimeter-length scales, this paper studies the information capacity of bacterial cables via electron transfer, which may coexist with the more well-known communication strategies based on molecular diffusion. The bacterial cable is modeled as an electron queue, which transports electrons from the encoder to the decoder located at the two ends of the cable. The encoder controls the desired input electron intensity, whereas the decoder attempts to decode the transmitted message based on the measured output electron process. Clogging of the cable, induced by local ATP saturation and resulting in a loss of electron transport efficiency along the cable, is modeled. The case where both the encoder and the decoder have full causal channel state information (CSI) with binary inputs is studied. A discrete-time version of the system is considered, enabling the computation of an achievable rate for the continuous-time system, based on known results on the capacity of finite-state Markov channels. The regime of asymptotically small time-slot duration is studied, and it is shown that the capacity optimization problem can be recast as a Markov decision process, which enables the use of standard optimization algorithms, e.g., policy iteration, to compute the capacity and the optimal expected desired input electron intensity, which generates the binary signal.

In this paper, a cross-layer framework to perform spectrum sensing and scheduling in agile wireless networks under resource constraints is presented. A network of secondary users (SUs) opportunistically accesses portions of the spectrum left unused by a network of licensed primary users (PUs). A central controller (CC) schedules the traffic of the SUs over the spectrum bands, based on distributed compressed spectrum sensing performed by the SUs. Both sensing and scheduling are controlled based on the current spectrum occupancy belief, with the goal to maximize the SU throughput, under constraints on the PU throughput degradation and the sensing-transmission cost incurred by the SUs. The high optimization complexity is reduced by proposing a partially myopic scheduling strategy, where the total traffic of the SUs is determined optimally via dynamic programming, whereas the allocation of the resulting total traffic across frequency bands is determined via a myopic maximization of the instantaneous trade-off between PU and SU throughputs, which can be solved efficiently using convex optimization tools. Structural results of the partially myopic scheduling strategy are proved. Simulation results demonstrate how the proposed framework allows to balance optimally the cost of acquisition of state information via distributed spectrum sensing and the cost of data transmission incurred by the SUs, while achieving the best trade-off between PU and SU throughput under the resource constraints available.

Recent discoveries of bacterial cables that transfer electrons across centimeter-length scales motivate the study of their information capacity. The bacterial cable is modeled as an electron queue that transfers electrons from the encoder at the electron donor source to the decoder at the electron acceptor sink. The model allows to capture the coupling between the electron signal and the energetic state of the cells via clogging due to local ATP saturation along the cable. Based on the analysis of a discrete-time scheme with asymptotically small time-slot duration, and assuming full causal channel state information (CSI), the optimality of binary input distributions is proved, i.e., the encoder transmits at either maximum or minimum intensity, as dictated by the physical constraints of the cable. It is proved that the optimal binary signal can be determined via dynamic programming, and that it has smaller intensity than that given by the myopic policy, which greedily maximizes the instantaneous information rate but neglects its effect on the steady-state distribution of the cable. This work represents a first contribution towards the design of electron signaling schemes in more complex microbial systems, e.g., biofilms, where the tension between maximizing the transfer of information and guaranteeing the well-being of the overall bacterial community arises, and motivates further research on the design of more practical schemes, where CSI is only partially available.

Wireless sensors can integrate rechargeable batteries and energy-harvesting (EH) devices to enable long-term, autonomous operation, thus requiring intelligent energy management to limit the adverse impact of energy outages. This work considers a network of EH wireless sensors, which report packets with a random utility value to a fusion center (FC) over a shared wireless channel. Decentralized access schemes are designed, where each node performs a local decision to transmit/discard a packet, based on an estimate of the packet's utility, its own energy level, and the scenario state of the EH process, with the objective to maximize the average long-term aggregate utility of the packets received at the FC. Due to the non-convex structure of the problem, an approximate optimization is developed by resorting to a mathematical artifice based on a game theoretic formulation of the multiaccess scheme, where the nodes do not behave strategically, but rather attempt to maximize a common network utility with respect to their own policy. The symmetric Nash equilibrium (SNE) is characterized, where all nodes employ the same policy; its uniqueness is proved, and it is shown to be a local maximum of the original problem. An algorithm to compute the SNE is presented, and a heuristic scheme is proposed, which is optimal for large battery capacity. It is shown numerically that the SNE typically achieves near-optimal performance, within 3% of the optimal policy, at a fraction of the complexity, and two operational regimes of EH-networks are identified and analyzed: an energy-limited scenario, where energy is scarce and the channel is under-utilized, and a network-limited scenario, where energy is abundant and the shared wireless channel represents the bottleneck of the system.

In this paper, a framework for dynamic high-dimensional hypothesis testing in wireless sensor networks is presented. The sensor nodes (SNs) collect and transmit to a fusion center (FC), in a distributed fashion, compressed measurements of a time-correlated hypothesis vector. The FC, based on the measurements collected, tracks the hypothesis vector, and feeds back minimal information about the uncertainty in the current estimate, which enables adaptation of the SNs' data collection and transmission strategy. The policy of the SNs is optimized with the overall objective of minimizing the detection error probability, under sensing and transmission cost constraints incurred by each SN. A Bernoulli approximation on the detection error is employed, which enables a significant reduction in the optimization complexity and the design of scalable estimators based on sparse approximation recovery algorithms. Simulation results demonstrate that, for a target 5% detection error, the adaptive scheme attains 90% and 50% cost savings with respect to a memoryless scheme which does not exploit the time-correlation and a non-adaptive one, respectively.

In this paper, a cross-layer framework for joint control and distributed sensing in agile wireless networks is presented, where an agent schedules actions to control a partially observable Markov decision process, whose state is inferred by collecting measurements from nearby assistant wireless nodes with cognitive and sensing capabilities (ANs). The framework makes it possible to model practical constraints of wireless networks, such as the cost incurred by the ANs to sense and transmit to the agent and the shared wireless channel, as well as to jointly optimize the acquisition of state information at the agent via distributed sensing, and the scheduling policy, under sensing-transmission cost constraints for the ANs. The optimality of a two-stage decomposition is proved, which enables decoupling of the optimization of action scheduling and distributed sensing. This scheme is applied to spectrum sensing, where the activity of licensed (PU, primary) users is measured by distributed wireless assisting receivers, based on which an agile (SU, secondary) user adapts its transmissions over time. Simulation results demonstrate that the proposed adaptive joint sensing-scheduling policy improves the SU throughput up to 50% over a scheme employing non-adaptive sensing, for a given constraint on the throughput degradation to the PU pair and cost incurred by the ANs, and up to a three-fold increase over a scheme where sensing is performed only locally by the SU.

Biological systems are known to communicate by diffusing chemical signals in the surrounding medium. However, most of the recent literature has neglected theelectron transfermechanism occurring among living cells, and its role in cell-cell communication. Each cell relies on a continuous flow of electrons from its electron donor to its electron acceptor through the electron transport chain to produce energy in the form of the molecule adenosine triphosphate, and to sustain the cell's vital operations and functions. While the importance of biological electron transfer is well-known for individual cells, the past decade has also brought about remarkable discoveries of multi-cellular microbial communities that transfer electrons between cells and across centimeter length scales, e.g., biofilms and multi-cellular bacterial cables. These experimental observations open up new frontiers in the design of electron-based communications networks in microbial communities, which may coexist with the more well-known communication strategies based on molecular diffusion, while benefiting from a much shorter communication delay. This paper develops a stochastic model that links the electron transfer mechanism to the energetic state of the cell. The model is also extensible to larger communities, by allowing for electron exchange between neighboring cells. Moreover, the parameters of the stochastic model are fit to experimental data available in the literature, and are shown to provide a good fit.

Wireless sensors can be integrated with energy harvesting (EH) devices to enable long-term, autonomous operation, necessitating efficient energy management. Existing research assumes knowledge of the state-of-charge (SOC) of the rechargeable battery; however, accurate SOC estimation in real-world devices is typically costly or impractical. This paper investigates the impact of imperfect SOC knowledge and the design of policies to cope with such uncertainty. The optimization complexity is reduced by decoupling the different time scales of the system: first, the short-term average performance is optimized with respect to fast-varying exogenous state variables, under an average energy consumption constraint, but neglecting battery dynamics; then, the policy dictating the average energy consumption as a function of state variables evolving over longer time scales is optimized, based on the detailed battery dynamics. A local search algorithm is presented to determine a locally optimal policy. The performance degradation compared to the scenario with perfect SOC knowledge is shown to decrease with increasing storage capacity and decreasing uncertainty in the EH source, and is within 5% for most cases of practical interest. Moreover, near-optimal performance is achieved by only a loose SOC knowledge, which distinguishes between high/low SOC levels. Finally, the impact of time correlation in the EH source is investigated. EH state knowledge is shown to be more critical than SOC knowledge, hence precise knowledge of the former can obviate the need for accurate information about the latter.

Harvesting-Based Wireless Sensor Devices are increasingly being deployed in today's sensor networks, due to their demonstrated advantages in terms of prolonged lifetime and autonomous operation. However, irreversible degradation mechanisms jeopardize battery lifetime, calling for intelligent management policies, which minimize the impact of these phenomena while guaranteeing a minimum Quality of Service (QoS). This paper explores a mathematical characterization of harvesting-based battery-powered sensor devices, focusing on the impact of the battery discharge policy on the irreversible degradation of the storage capacity. A general framework based on Markov chains which captures the battery degradation process is proposed. Based on such model, it is shown that a degradationaware policy significantly improves the lifetime of the sensor compared to "greedy" operation policies, while guaranteeing the minimum required QoS.

This paper introduces a novel technique for access by a cognitive Secondary User (SU) using best-effort transmission to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the primary ARQ protocol to perform Interference Cancellation (IC) at the SU receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward IC, where SUrx, after decoding the PU message, cancels its interference in the (possible) following PU retransmissions of the same message, to improve the SU throughput; Backward IC, where SUrx performs IC on previous SU transmissions, whose decoding failed due to severe PU interference. Secondary access policies are designed that determine the secondary access probability in each state of the network so as to maximize the average long-term SU throughput by opportunistically leveraging IC, while causing bounded average long-term PU throughput degradation and SU power expenditure. It is proved that the optimal policy prescribes that the SU prioritizes its access in the states where SUrx knows the PU message, thus enabling IC. An algorithm is provided to optimally allocate additional secondary access opportunities in the states where the PU message is unknown. Numerical results are shown to assess the throughput gain provided by the proposed techniques.

Energy Harvesting Wireless Sensor Devices are increasingly being considered for deployment in sensor networks, due to their demonstrated advantages of prolonged lifetime and autonomous operation. However, irreversible degradation mechanisms jeopardize battery lifetime, calling for intelligent management policies, which minimize the impact of these phenomena while guaranteeing a minimum Quality of Service (QoS). This paper explores a mathematical characterization of these devices, focusing on the interplay between the battery discharge policy and the irreversible degradation of the storage capacity. We propose a stochastic Markov chain framework, suitable for policy optimization, which captures the degradation status of the battery. We present a general result of Markov chains, which exploits the timescale separation between the communication time-slot of the device and the battery degradation process, and enables an efficient optimization. We show that this model fits well the behavior of real batteries for what concerns their storage capacity degradation over time. We demonstrate that a degradation-aware policy significantly improves the lifetime of the sensor compared to "greedy" policies, while guaranteeing the minimum required QoS. Finally, a simple heuristic policy, which never discharges the battery below a given threshold, is shown to achieve near-optimal performance in terms of battery lifetime.

This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver. Modeling the ambient energy supply by a two-state Markov chain ("GOOD" and "BAD"), assuming a finite battery capacity constraint, and associating data transmission with a given energy cost, we propose low-complexity transmission policies, that achieve near-optimal performance in terms of the average long-term importance of the reported data. In particular, we derive the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced. Our analysis demonstrates that the performance of the BP largely depends on the power-to-depletion, defined as the power that a fully charged battery can supply on average over a BAD period. Numerical results show that the optimal BP achieves near-optimal performance and that a BP which avoids energy overflow further reduces the gap with respect to the globally optimal policy. A heuristic BP, based on the analysis of a system with a deterministic and periodic energy supply, is also proposed, and the parallels between the deterministic system and its stochastic counterpart are discussed.

As Energy Harvesting Devices (EHD) become more widely deployed in sensor network platforms, the need arises for "smart" operation policies which can ensure long-term, autonomous and reliable operation. Existing research has relied on the implicit assumption of perfect knowledge of the energy available in the EHD. However, estimating the energy level of the batteries or super-capacitors employed in real-world EHDs, commonly known as State-Of-Charge (SOC), is a non-trivial task. In this paper, we design operation policies that maximize the long-term reward under imperfect knowledge of the SOC. Through an array of simulation results, we quantify the performance degradation due to imperfect SOC knowledge, and show that it increases with decreasing storage capacity and increasing variance in the energy arrival process. In the particular case of a two-state controller, i.e., a controller which knows only if the SOC is HIGH or LOW, we prove that, for a linear reward function, there is no performance loss, while, for a logarithmic reward function, simulations show that the loss is typically less than 5%.

Nowadays, many devices in wireless sensor networks are provided with energy harvesting capability to allow for their continuous operation over long periods of time. In principle, the energy level within each sensor should be managed optimally to ensure the best performance. Network engineers, however, often consider optimality under the idealized assumption of perfect knowledge about the State-of-Charge (SOC) of the device. This information is not always realistic or accurate. In our previous work [1], we showed that optimal policies for sensing, transmission, and battery usage should rather consider uncertainty on the SOC of the device. In this paper, we extend that investigation, therein performed in the idealized scenario of i.i.d. energy arrivals, by considering a correlated energy generation process. We show that the knowledge of the SOC and that of the energy generation process are useful in a complementary manner, that is they can be traded for each other. Moreover, the knowledge on the state of the energy generation process can obviate the need for acquiring accurate SOC information. This investigation paves the road for a new line of research in wireless sensor networks, allowing a tighter interaction between the designers of energy harvesting and battery storage mechanisms on the one hand, and the engineers of network operation and control policies on the other.