We study function computation over a Gaussian multiple-access channel (MAC), where multiple transmitters aim at computing a function of their values at a common receiver. To this end, we propose a novel coded-modulation framework for over-the-air computation (OAC) based on hierarchical constellation design, which supports reliable computation of multiple function outputs using a single channel use. Moreover, we characterize the achievable computation rate and show that the proposed hierarchical constellations can compute R output functions with decoding error probability epsilon while the gap to the optimal computation rate scales as O(\log_2(1/ε)/K) for independent source symbols, where K denotes the number of transmitters. Consequently, this gap vanishes as the network size grows, and the optimal rate is asymptotically attained. Furthermore, we introduce a shielding mechanism based on variable-length block coding that mitigates noise-induced error propagation across constellation levels while preserving the superposition structure of the MAC. We show that the shielding technique improves reliability, yielding a gap that scales optimally as O(\log_2\ln{(1/ε)}), regardless of the source distribution. Together, these results identify the regimes in which uncoded or lightly coded OAC is information-theoretically optimal, providing a unified framework for low-latency, channel-agnostic function computation.

For digital over-the-air computation, the ChannelComp framework has recently been proposed to design digital modulations to compute any arbitrary function over a multiple access channel. To reduce modulation design complexity while increasing computation reliability, this paper integrates a bit-partitioning procedure into ChannelComp. The key process is to partition the input bit sequence into several groups, map each group to a single modulation symbol and transmit the encoded symbol sequence across multiple time slots. With the objective to maximize a worst-case constellation distance, we develop two bit-partitioning methods. In uniform bit-partitioning, bits are evenly distributed across groups and modulation is designed via a max-min optimization, which is handled by a CCCP that solves a sequence of second-order cone programming subproblems. In importance-adaptive bit-partitioning (IABP), the bit allocation is adapted to the significance of individual bit positions, and the modulation and partitioning are jointly optimized. To keep the overall complexity manageable, simulated annealing is employed in the outer loop to update the partitioning, while a CCCP-based solver is used in the inner loop for modulation design. Numerical results show that both methods provide robust computation in noisy channels, and IABP achieves up to a 5 dB reduction in computation error compared to Sequential Modulation for AirComp, especially for product computation.

This paper presents a digital over-the-air (OTA) computation architecture that reliably aggregates nomographic functions, e.g., sum, product, maximum, and sum-of-squares—over a fading multiple-access channel. Every sensor quantises its reading through a multiple-level amplitude-shift modulation (ASM) hierarchy whose rectangular QAM grids are jointly optimised by a level-wise, maximum-a-posteriori (MAP) search. The optimisation weighs quantization distortion against MAP–detection error, producing the closed-form expression that accommodates both symmetric and asymmetric (weighted) sum. Asymmetric sum is achieved by means of a greatest-common-divisor lattice alignment. In addition, an equal-split quantization is derived for noise-dominated regimes. Numerical results show that: the proposed digital–OTA design surpasses the analog–OTA baseline on three of the four nomographic tasks by up to 20 dB; under heterogeneous node weights the scheme maintains normalised mean square error (NMSE)$\le 10^{-1}$throughout, whereas analog OTA degrades sharply. These results confirm that ASM-based hierarchical quantization, combined with MAP-optimal grid selection, provides a versatile and spectrally efficient foundation for next-generation edge learning and federated analytics.

This paper proposes a coordinated beamforming scheme for a multi-cell integrated sensing and communication (ISAC) system. A target-centric graph is constructed, with the coordinate system centered at the detection target. Specifically, base stations (BSs) and users are represented as nodes, with their coordinates serving as node features, while channel realizations between nodes are modeled as edge features. To evaluate sensing performance, the Neyman-Pearson detector is employed to compute the detection probability for a fixed false alarm probability. The optimization problem is formulated to maximize the detection probability of the target at the origin while ensuring QoS communication requirements and satisfying the transmit power budget. This sensing-centric problem is addressed using graph neural networks (GNNs), which generate parameterized policies for coordinated beamforming. The GNNs are trained via primal-dual approach, leveraging a small duality gap for efficient convergence. Additionally, specific layers are utilized to generate user association policies for communication links, enabling efficient processing of the graph-structured data after sparsity enhancement. Simulation results validate the feasibility and effectiveness of the proposed GNN-based approach in achieving high detection probability.

Recently, the ChannelComp framework has proposed digital over-the-air computation by designing digital modulations that enable the computation of arbitrary functions. Unlike traditional analog over-the-air computation, which is restricted to nomographic functions, ChannelComp enables a broader range of computational tasks while maintaining compatibility with digital communication systems. This framework is intended for applications that favor local information processing over the mere acquisition of data. However, ChannelComp is currently designed for scalar function computation, while numerous data-centric applications necessitate vector-based computations, and it is susceptible to channel fading. In this work, we introduce a generalization of the ChannelComp framework, called VecComp, by integrating ChannelComp with multiple-antenna technology. This generalization not only enables vector function computation but also ensures scalability in the computational complexity, which increases only linearly with the vector dimension. As such, VecComp remains computationally efficient and robust against channel impairments, making it suitable for high-dimensional, data-centric applications. We establish a non-asymptotic upper bound on the mean squared error of VecComp, affirming its computation efficiency under fading channel conditions. Numerical experiments show the effectiveness of VecComp in improving the computation of vector functions and fading compensation over noisy and fading multiple-access channels.

We consider the problem of non-coherent over-the-air computation (AirComp), where$n$devices carry highdimensional data vectors$\mathrm{x}_{i} \in \mathbb{R}^{d}$of sparsity$\left\vert\mathrm{x}_{i}\right\vert_{0} \leq k$and the sum of these data vectors has to be computed at a receiver. Previous results on non-coherent AirComp require more than$d$channel uses to compute functions of$\mathrm{x}_{i}$, where the extra redundancy is used to combat non-coherent signal aggregation. However, if the data vectors are sparse, sparsity can be exploited to offer significantly cheaper communication. In this paper, we propose to use random transforms to transmit lower-dimensional projections$s_{i} \in \mathbb{R}^{T}$of the data vectors. These projected vectors are communicated to the receiver using a majority vote (MV)AirComp scheme, which estimates the bit-vector corresponding to the signs of the aggregated projections, i.e.,$\mathbf{y}=\text{sign}\left(\sum_{i} \mathbf{s}_{i}\right)$. By leveraging 1-bit compressed sensing (1bCS) at the receiver, the real-valued and high-dimensional aggregate$\sum_{i} \mathrm{x}_{i}$can be recovered from$y$. We prove analytically that the proposed MVCS scheme estimates the aggregate data vector$\sum_{i} \mathrm{x}_{i}$with$\ell_{2}$-norm error$\epsilon$in$T=\mathcal{O}\left(k n \log (d) / \epsilon^{2}\right)$channel uses. We consider distributed histogram estimation, a canonical building block for federated analytics, as an aplication for MVCS where the data vectors$\mathrm{x}_{i}$are inherently 1 -sparse. Our numerical evaluations demonstrate that our scheme achieves the same order of communication cost as state-of-the-art methods while avoiding the complexity and overhead of additional cryptographic tools.

To meet the requirements of the artificial internet of things (AIoT), decentralization is essential to provide wide coverage, trustworthiness, and low-latency communication. For this purpose, decentralized federated learning (DFL) has rapidly evolved and gained popularity in recent years by reducing reliance on a central server and promoting a more robust, scalable, and privacy-preserving system. Nevertheless, the exchange of model updates and gradients in peer-to-peer (P2P) wireless communication systems introduces new vulnerabilities that threaten both model performance and data security, while frequent P2P communication among clients can lead to high communication costs. To address these issues, in this work, we develop a communicationefficient and security-enhanced DFL algorithm, which integrates the fast incremental alternating direction method of multipliers (FI-ADMM) algorithm with parameter-selective additively homomorphic encryption. Additionally, by introducing a first-order approximation for primal updates and rearranging the update order in FI-ADMM, the proposed method is superior to the other benchmarks in terms of computational and time complexity, which is validated by theoretical analysis and simulations.

This paper investigates the use of the newly opened, and relatively unexplored, 6 GHz band for office occupancy detection using Wi-Fi sensing. To deliver accurate and efficient occupancy detection, we develop a novel channel model-informed feature engineering method combined with a random forest optimization strategy. Specifically, physically interpretable channel state information (CSI) amplitude-based features, such as the RicianK-factor and channel coherence time, are employed to capture channel variations induced by human presence and movement. A dual sliding window approach is introduced to effectively extract temporally relevant channel parameters, significantly improving computational efficiency and classification accuracy. Experimental validation conducted in a realistic office environment demonstrates that the proposed method achieves an average occupancy classification accuracy of 98.28%, outperforming existing methods while substantially reducing computational complexity. These findings suggest that integrating this Wi-Fi sensing approach into next-generation networks (e.g., IEEE 802.11bf) can enhance real-time responsiveness and reliability in smart building applications such as security and energy management.

Communication and computation are traditionally treated as separate entities, allowing for individual optimizations. However, many applications focus on local information’s functionality rather than the information itself. For such cases, harnessing interference for computation in a multiple access channel through digital over-the-air computation can notably increase the computation, as established by the ChannelComp method. However, the coding scheme originally proposed in ChannelComp may suffer from high computational complexity because it is general and is not optimized for specific modulation categories. Therefore, this study considers a specific category of digital modulations for over-the-air computations, quadrature amplitude modulation (QAM) and pulse-amplitude modulation (PAM), for which we introduce a novel coding scheme called SumComp. Furthermore, we derive a mean squared error (MSE) analysis for SumComp coding in the computation of the arithmetic mean function and establish an upper bound on the mean absolute error (MAE) for a set of nomographic functions. Simulation results are presented to affirm the superior performance of SumComp coding compared to traditional analog over-the-air computation and the original coding in ChannelComp approaches in terms of both MSE and MAE over a noisy multiple access channel. Specifically, SumComp coding shows at least 10 dB improvements for computing arithmetic and geometric mean on the normalized MSE for low noise scenarios.

In this paper, we consider the ChannelComp framework, where multiple transmitters aim to compute a function of their values at a common receiver while using digital modulations over a multiple access channel. ChannelComp provides a general framework for computation by designing digital constellations for over-the-air computation. Currently, ChannelComp uses a symbol-level encoding. However, encoding repeated transmissions of the same symbol and performing the function computation using the corresponding received sequence may significantly improve the computation performance and reduce the encoding complexity. In this paper, we propose a new scheme where each transmitter repeats the transmission of the same symbol over multiple time slots while encoding such repetitions and designing constellation diagrams to minimize computational errors. We formally model such a scheme by an optimization problem, whose solution jointly identifies the constellation diagram and the repetition code. We call the proposed scheme Repetition for Multiple Access Computing (ReMAC). To manage the computational complexity of the optimization, we divide it into two tractable subproblems. We verify the performance of ReMAC by numerical experiments. The simulation results reveal that ReMAC can reduce the computation error in noisy and fading channels by approximately up to 4.5 dB compared to standard ChannelComp, particularly for the max function.

The Internet of Things interconnects billions of devices and forms a vast network where users sporadically transmit short messages through multi-path wireless channels. These channels are characterized by the superposition of a small number of scaled and delayed copies of Dirac spikes. At the receiver, the observed signal is a sum of these convolved signals, and the task is to find the amplitudes, continuous-indexed delays, and transmitted messages from a single signal. This task is inherently ill-posed without additional assumptions on the channel or messages. In this work, we assume the channel exhibits sparsity in the delay domain and that independent and identically distributed random linear encoding is applied to the messages at the devices. Leveraging these assumptions, we propose a semidefinite programming optimization capable of simultaneously recovering both messages and the delay parameters of the channels from only a single received signal. Our theoretical analysis establishes that the required number of samples at the receiver scales proportionally to the sum-product of sparsity and message length of all users, aligning with the degrees of freedom in the lifting-type optimization frameworks. Numerical experiments confirm the efficacy of the proposed method in accurately estimating closely-spaced delay parameters and recovering messages.

This paper investigates over-the-air computation (AirComp) over multiple-access time-varying channels, where devices with high mobility transmit their sensing data to a fusion center (FC) for averaging. To combat the Doppler shift induced by time-varying channels, each device adopts orthogonal time frequency space (OTFS) modulation. Our objective is minimizing the mean squared error (MSE) for the target function estimation. Due to the multipath time-varying channels, the OTFS-based AirComp not only suffers from noise but also interference. Specifically, we propose three schemes, namely S1, S2, and S3, for the target function estimation. S1 directly estimates the target function under the impacts of noise and interference. S2 mitigates the interference by introducing a zero padding-assisted OTFS. In S3, we propose an iterative algorithm to estimate the function in a matrix form. In the numerical results, we evaluate the performance of S1, S2, and S3 from the perspectives of MSE and computational complexity, and compare them with benchmarks. Specifically, compared to benchmarks, S3 outperforms them with a significantly lower MSE but incurs a higher computational complexity. In contrast, S2 demonstrates a reduction in both MSE and computational complexity. Lastly, S1 shows superior error performance at small SNR and reduced computational complexity.

We consider the problem of secure histogram es-timation, where$n$users hold private items xifrom a size-d domain and a server aims to estimate the histogram of the user items. Previous results utilizing orthogonal communication schemes have shown that this problem can be solved securely with a total communication cost of O(n2log(d)) bits by hiding each item xiwith a mask. In this paper, we offer a different approach to achieving secure aggregation. Instead of masking the data, our scheme protects individuals by aggregating their messages via a multiple-access channel. A naive communication scheme over the multiple-access channel requires$d$channel uses, which is generally worse than the O(n21og(d)) bits communication cost of the prior art in the most relevant regime$d$>>$n$. Instead, we propose a new scheme that we call Over-the-Air Group Testing (AirG T) which uses group testing codes to solve the histogram estimation problem in O(n log(d)) channel uses. AirGT reconstructs the histogram exactly with a vanishing probability of error Perror= O(d-T) that drops exponentially in the number of channel uses$T$.

In this paper, we consider the ChannelComp frame-work, which facilitates the computation of desired functions by multiple transmitters over a common receiver using digital mod-ulations across a multiple access channel. While ChannelComp currently offers a broad framework for computation by designing digital constellations for over-the-air computation and employing symbol-level encoding, encoding the repeated transmissions of the same symbol and using the corresponding received sequence may significantly improve the computation performance and reduce the encoding complexity. In this paper, we propose an enhancement involving the encoding of the repetitive transmission of the same symbol at each transmitter over multiple time slots and the design of constellation diagrams, with the aim of minimizing computational errors. We frame this enhancement as an optimization problem, which jointly identifies the constellation diagram and the channel code for repetition, which we call ReChCompCode. To manage the computational complexity of the optimization, we divide it into two tractable subproblems. Through numerical experiments, we evaluate the performance of ReChCompCode. The simulation results reveal that ReCh-CompCode can reduce the computation error by approximately up to 30 dB compared to standard ChannelComp, particularly for product functions.

Unmanned aerial vehicle (UAV)-assisted communication is becoming a streamlined technology in providing improved coverage to the internet-of-things (IoT) based devices. Rapid deployment, portability, and flexibility are some of the fundamental characteristics of UAVs, which make them ideal for effectively managing emergency-based IoT applications. This paper studies a UAV-assisted wireless IoT network relying on non-orthogonal multiple access (NOMA) to facilitate uplink connectivity for devices spread over a disaster region. The UAV setup is capable of relaying the information to the cellular base station (BS) using decode and forward relay protocol. By jointly utilizing the concepts of unsupervised machine learning (ML) and solving the resulting non-convex problem, we can maximize the total energy efficiency (EE) of IoT devices spread over a disaster region. Our proposed approach uses a combination of k-medoids and Silhouette analysis to perform resource allocation, whereas, power optimization is performed using iterative methods. In comparison to the exhaustive search method, our proposed scheme solves the EE maximization problem with much lower complexity and at the same time improves the overall energy consumption of the IoT devices. Moreover, in comparison to a modified version of greedy algorithm, our proposed approach improves the total EE of the system by $19 \%$ for a fixed 50 k target number of bits.

Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.

This paper investigates efficient distributed training of a Federated Learning (FL) model over a wireless network of wireless devices. The communication iterations of the distributed training algorithm may be substantially deteriorated or even blocked by the effects of the devices’ background traffic, packet losses, congestion, or latency. We abstract the communication-computation impacts as an ‘iteration cost’ and propose a cost-aware causal FL algorithm (FedCau) to tackle this problem. We propose an iteration-termination method that trade-offs the training performance and networking costs. We apply our approach when workers use the slotted-ALOHA, carrier-sense multiple access with collision avoidance (CSMA/CA), and orthogonal frequency-division multiple access (OFDMA) protocols. We show that, given a total cost budget, the training performance degrades as either the background communication traffic or the dimension of the training problem increases. Our results demonstrate the importance of proactively designing optimal cost-efficient stopping criteria to avoid unnecessary communication-computation costs to achieve a marginal FL training improvement. We validate our method by training and testing FL over the MNIST and CIFAR-10 dataset. Finally, we apply our approach to existing communication efficient FL methods from the literature, achieving further efficiency. We conclude that cost-efficient stopping criteria are essential for the success of practical FL over wireless networks.

In this work, we investigate federated edge learning over a fading multiple access channel. To alleviate the communication burden between the edge devices and the access point, we introduce a pioneering digital over-the-air computation strategy employing q-ary quadrature amplitude modulation, culminating in a low latency communication scheme. Indeed, we propose a new federated edge learning framework in which edge devices use digital modulation for over-the-air uplink transmission to the edge server while they have no access to the channel state information. Furthermore, we incorporate multiple antennas at the edge server to overcome the fading inherent in wireless communication. We analyze the number of antennas required to mitigate the fading impact effectively. We prove a non-asymptotic upper bound for the mean squared error for the proposed federated learning with digital over-the-air uplink transmissions under both noisy and fading conditions. Leveraging the derived upper bound, we characterize the convergence rate of the learning process of a non-convex loss function in terms of the mean square error of gradients due to the fading channel. Furthermore, we substantiate the theoretical assurances through numerical experiments concerning mean square error and the convergence efficacy of the digital federated edge learning framework. Notably, the results demonstrate that augmenting the number of antennas at the edge server and adopting higher-order modulations improve the model accuracy up to 60%.

We consider the problem of gridless blind deconvolution and demixing (GB2D) in scenarios where multiple users communicate messages through multiple unknown channels, and a single base station (BS) collects their contributions. This scenario arises in various communication fields, including wireless communications, the Internet of Things, over-the-air computation, and integrated sensing and communications. In this setup, each user's message is convolved with a multi-path channel formed by several scaled and delayed copies of Dirac spikes. The BS receives a linear combination of the convolved signals, and the goal is to recover the unknown amplitudes, continuous-indexed delays, and transmitted waveforms from a compressed vector of measurements at the BS. However, without prior knowledge of the transmitted messages and channels, GB2D is highly challenging and intractable in general. To address this issue, we assume that each user's message follows a distinct modulation scheme living in a known low-dimensional subspace. By exploiting these subspace assumptions and the sparsity of the multipath channels for different users, we transform the nonlinear GB2D problem into a matrix tuple recovery problem from a few linear measurements. To achieve this, we propose a semidefinite programming optimization that exploits the specific low-dimensional structure of the matrix tuple to recover the messages and continuous delays of different communication paths from a single received signal at the BS. Finally, our numerical experiments show that our proposed method effectively recovers all transmitted messages and the continuous delay parameters of the channels with sufficient samples.

Over-the-air computation (AirComp) is a known technique in which wireless devices transmit values by analog amplitude modulation so that a function of these values is computed over the communication channel at a common receiver. The physical reason is the superposition properties of the electromagnetic waves, which naturally return sums of analog values. Consequently, the applications of AirComp are almost entirely restricted to analog communication systems. However, the use of digital communications for over-the-air computations would have several benefits, such as error correction, synchronization, acquisition of channel state information, and easier adoption by current digital communication systems. Nevertheless, a common belief is that digital modulations are generally unfeasible for computation tasks because the overlapping of digitally modulated signals returns signals that seem to be meaningless for these tasks. This paper breaks through such a belief and proposes a fundamentally new computing method, named ChannelComp, for performing over-the-air computations by any digital modulation. In particular, we propose digital modulation formats that allow us to compute a wider class of functions than AirComp can compute, and we propose a feasibility optimization problem that ascertains the optimal digital modulation for computing functions over-the-air. The simulation results verify the superior performance of ChannelComp in comparison to AirComp, particularly for the product functions, with around 10 dB improvement of the computation error.

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

Current sound-based practices and systems developed in both academia and industry point to convergent research trends that bring together the field of Sound and Music Computing with that of the Internet of Things. This paper proposes a vision for the emerging field of the Internet of Sounds (IoS), which stems from such disciplines. The IoS relates to the network of Sound Things, i.e., devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In the IoS paradigm, which merges under a unique umbrella the emerging fields of the Internet of Musical Things and the Internet of Audio Things, heterogeneous devices dedicated to musical and non-musical tasks can interact and cooperate with one another and with other things connected to the Internet to facilitate sound-based services and applications that are globally available to the users. We survey the state of the art in this space, discuss the technological and non-technological challenges ahead of us and propose a comprehensive research agenda for the field.

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as it does not require retraining and entails no loss in accuracy. Unfortunately, resource-constrained devices such as those in the Internet of Things may need to offload the related computation to more powerful servers, particularly, at the network edge. However, edge servers have limited resources compared to those in the cloud; therefore, inference offloading generally requires dividing the original DNN into different pieces that are then assigned to multiple edge servers. Related approaches in the state-of-the-art either make strong assumptions on the system model or fail to provide strict performance guarantees. This article specifically addresses these limitations by applying distributed assignment to DNN inference at the edge. In particular, it devises a detailed model of DNN-based inference, suitable for realistic scenarios involving edge computing. Optimal inference offloading with load balancing is also defined as a multiple assignment problem that maximizes proportional fairness. Moreover, a distributed algorithm for DNN inference offloading is introduced to solve such a problem in polynomial time with strong optimality guarantees. Finally, extensive simulations employing different data sets and DNN architectures establish that the proposed solution significantly improves upon the state-of-the-art in terms of inference time (1.14 to 2.62 times faster), load balance (with Jain’s fairness index of 0.9), and convergence (one order of magnitude less iterations).

We develop a gradient-like algorithm to minimize a sum of peer objective functions based on coordination through a peer interconnection network. The coordination admits two stages: the first is to constitute a gradient, possibly with errors, for updating locally replicated decision variables at each peer and the second is used for error-free averaging for synchronizing local replicas. Unlike many related algorithms, the errors permitted in our algorithm can cover a wide range of inexactnesses, as long as they are bounded. Moreover, we do not impose any gradient boundedness conditions for the objective functions. Furthermore, the second stage is not conducted in a periodic manner, like many related algorithms. Instead, a locally verifiable criterion is devised to dynamically trigger the peer-to-peer coordination at the second stage, so that expensive communication overhead for error-free averaging can significantly be reduced. Finally, the convergence of the algorithm is established under mild conditions.

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.

Over-the-air computation (AirComp) has recently emerged as an efficient analog method for data acquisition from wireless sensor devices. In essence, AirComp exploits the signal superposition property of a multiple access channel to estimate functions of the transmitted data points. Unless devices are excluded from participation, state-of-the-art AirComp methods do not achieve unbiased function computation, thereby introducing systematic errors in the acquired function. In this paper, we propose a new AirComp scheme that employs retransmissions to achieve probabilistically unbiased function computation. We solve a power control problem that minimizes the bias subject to a peak transmission power constraint. We show that the optimal power control follows a greedy structure that maximizes the devices' contribution to the received function at every retransmission. Numerical results show that the proposed scheme can achieve unbiased function computation with a few retransmissions and drastically reduce the mean squared error in the function estimation compared to the current state-of-the-art.

Backscatter communication (BC) and radio-frequency energy harvesting (RF-EH) are two promising technologies for extending the battery lifetime of wireless devices. Although there have been some qualitative comparisons between these two technologies, quantitative comparisons are still lacking, especially for massive IoT networks. In this paper, we address this gap in the research literature, and perform a quantitative comparison between BC and RF-EH in massive IoT networks with multiple primary users and multiple low-power devices acting as secondary users. An essential feature of our model is that it includes the interferences caused by the secondary users to the primary users, and we show that these interferences significantly impact the system performance of massive IoT networks. For the RF-EH model, the power requirements of digital-to-analog and signal amplification are taken into account. We pose and solve a power minimization problem for BC, and we show analytically when BC is better than RF-EH. The results of the numerical simulations illustrate the significant benefits of using BC in terms of saving power and supporting massive IoT, compared to using RF-EH. The results also show that the backscatter coefficients of the BC devices must be individually tunable, in order to guarantee good performance of BC.

Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.

In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated Learning can leverage data clustering algorithms and build a machine learning model for each cluster. However, traditional data clustering algorithms, when applied to the handover prediction, exhibit three main limitations: the risk of data privacy breach, the fixed shape of clusters, and the non-adaptive number of clusters. To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division. We show that the generative adversarial network-based clustering preserves privacy. The cluster calibration deals with dynamic environments by modifying clusters. Moreover, the divisive clustering explores the different number of clusters by repeatedly selecting and dividing a cluster into multiple clusters. A baseline algorithm and our algorithm are tested on a time series forecasting task. We show that our algorithm improves the performance of forecasting models, including cellular network handover, by 43%.

In the aeronautics industry, wireless avionics intracommunications (WAICs) have a tremendous potential to improve efficiency and flexibility while reducing weight, fuel consumption, and maintenance costs over traditional wired avionics systems. This survey starts with an overview of the major benefits and opportunities in the deployment of wireless technologies for critical applications in an aircraft. The current state of the art is presented in terms of system classifications based on data rate demands and transceiver installation locations. We then discuss major technical challenges in the design and realization of the envisioned aircraft applications. Although WAIC has aspects and requirements similar to mission-critical applications of industrial automation, it also has specific issues, such as wireless channels, complex structures, operations, and safety of the aircraft that make this area of research self-standing and challenging. Existing wireless techniques are discussed to investigate the applicability of the current solutions for the critical operations of an aircraft. Specifically, IEEE 802.15.4-based and Bluetooth-based solutions are discussed for low data rate applications, whereas IEEE 802.11-based and UWB-based solutions are considered for high data rate applications. We conclude the survey by highlighting major research directions in this emerging area.

Full-duplex communications have the potential to almost double the spectral efficiency. To realize such a potentiality, the signal separation at base station’s antennas plays an essential role. This article addresses the fundamentals of such separation by proposing a new smart antenna architecture that allows every antenna to be either shared or separated between uplink and downlink transmissions. The benefits of such architecture are investigated by an assignment problem to optimally assign antennas, beamforming and power to maximize the weighted sum spectral efficiency. We propose a near-to-optimal solution using block coordinate descent that divides the problem into assignment problems, which are NP-hard, a beamforming and power allocation problems. The optimal solutions for the beamforming and power allocation are established while near-to-optimal solutions to the assignment problems are derived by semidefinite relaxation. Numerical results indicate that the proposed solution is close to the optimum, and it maintains a similar performance for high and low residual self-interference powers. With respect to the usually assumed antenna separation technique and half-duplex transmission, the sum spectral efficiency gains increase with the number of antennas. We conclude that our proposed smart antenna assignment for signal separation is essential to realize the benefits of multiple antenna full-duplex communications.

Wireless communication is evolving to support critical control in automation systems. The fifth-generation (5G) mobile network air interface New Radio adopts a scalable numerology and mini-slot transmission for short packets that make it potentially suitable for critical control systems. The reliable minimum cycle time is an important indicator for industrial communication techniques but has not yet been investigated within 5G. To address such a question, this article considers 5G-based industrial networks and uses the delay optimization based on data-driven channel characterization (CCDO) approach to propose a method to evaluate the reliable minimum cycle time of 5G. Numerical results in three representative industrial environments indicate that following the CCDO approach, 5G-based industrial networks can achieve, in real-world scenario, millisecond-level minimum cycle time to support several hundred nodes with reliability higher than 99.9999%.

As the Internet of Musical Things (IoMusT) emerges, audio-specific operating systems (OSs) are required on embedded hardware to ease development and portability of IoMusT applications. Despite the increasing importance of IoMusT applications, in this article, we show that there is no OS able to fulfill the diverse requirements of IoMusT systems. To address such a gap, we propose the Elk Audio OS as a novel and open source OS in this space. It is a Linux-based OS optimized for ultra-low-latency and high-performance audio and sensor processing on embedded hardware, as well as for handling wireless connectivity to local and remote networks. Elk Audio OS uses the Xenomai real-time kernel extension, which makes it suitable for the most demanding of low-latency audio tasks. We provide the first comprehensive overview of Elk Audio OS, describing its architecture and the key components of interest to potential developers and users. We explain operational aspects like the configuration of the architecture and the control mechanisms of the internal sound engine, as well as the tools that enable an easier and faster development of connected musical devices. Finally, we discuss the implications of Elk Audio OS, including the development of an open source community around it.

Spectral precoding is a promising technique to suppress out-of-band emissions and comply with leakage constraints over adjacent frequency channels and with mask requirements on the unwanted emissions. However, spectral precoding may distort the original data vector, which is formally expressed as the error vector magnitude (EVM) between the precoded and original data vectors. Notably, EVM has a deleterious impact on the performance of multiple-input multiple-output orthogonal frequency division multiplexing-based systems. In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements. We first formulate and solve the EVM-unconstrained mask-compliant spectral precoding problem, which serves as a springboard to the design of two EVM-constrained spectral precoding schemes. The first scheme takes into account a wideband EVM-constraint which limits the average in-band distortion. The second scheme takes into account frequency-selective EVM-constraints, and consequently, limits the signal distortion at the subcarrier level. Numerical examples illustrate that both proposed schemes outperform previously developed schemes in terms of important performance indicators such as block error rate and system-wide throughput while complying with spectral mask and EVM constraints.

Although spectral precoding is a propitious technique to suppress out-of-band emissions, it has a detrimental impact on the system-wide throughput performance, notably, in high data-rate multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM), because of (spatially-coloured) transmit error vector magnitude (TxEVM) emanating from spectral precoding. The first contribution of this paper is to propose two mask-compliant spectral precoding schemes, which mitigate the resulting TxEVM seen at the receiver by capitalizing on the immanent degrees-of-freedom in (massive) MIMO systems and consequently improve the system-wide throughput. Our second contribution is an introduction to a new and simple three-operator consensus alternating direction method of multipliers (ADMM) algorithm, referred to as TOP-ADMM, which decomposes a large-scale problem into easy-to-solve subproblems. We employ the proposed TOP-ADMM-based algorithm to solve the spectral precoding problems, which offer computational efficiency. Our third contribution presents substantial numerical results by using an NR release 15 compliant simulator. In case of perfect channel knowledge at the transmitter, the proposed methods render similar block error rate and throughput performance as without spectral precoding yet meeting out-of-band emission (OOBE) requirements at the transmitter. Further, no loss on the OOBE performance with a graceful degradation on the throughput is observed under channel uncertainty.

This paper addresses the problem of distributed training of a machine learning model over the nodes of a wireless communication network. Existing distributed training methods are not explicitly designed for these networks, which usually have physical limitations on bandwidth, delay, or computation, thus hindering or even blocking the training tasks. To address such a problem, we consider a general class of algorithms where the training is performed by iterative distributed computations across the nodes. We assume that the nodes have some background traffic and communicate using the slotted-ALOHA protocol. We propose an iteration-termination criterion to investigate the trade-off between achievable training performance and the overall cost of running the algorithms. We show that, given a total running budget, the training performance becomes worse as either the background communication traffic or the dimension of the training problem increases. We conclude that a co-design of distributed optimization algorithms and communication protocols is essential for the success of machine learning over wireless networks and edge computing.

Backscatter communication is a promising solution for enabling information transmission between ultra-low-power devices, but its potential is not fully understood. One major problem is dealing with the interference between the backscatter devices, which is usually not taken into account, or simply treated as noise in the cases where there are a limited number of backscatter devices in the network. In order to better understand this problem in the context of massive IoT (Internet of Things), we consider a network with a base station having one antenna, serving one primary user, and multiple IoT devices, called secondary users. We formulate an optimization problem with the goal of minimizing the needed transmit power for the base station, while the ratio of backscattered signal, called backscatter coefficient, is optimized for each of the IoT devices. Such an optimization problem is non-convex and thus finding an optimal solution in real-time is challenging. In this paper, we prove necessary and sufficient conditions for the existence of an optimal solution, and show that it is unique. Furthermore, we develop an efficient solution algorithm, only requiring solving a linear system of equations with as many unknowns as the number of secondary users. The simulation results show a lower energy outage probability by up to 40-80 percentage points in dense networks with up to 150 secondary users. To our knowledge, this is the first work that studies backscatter communication in the context of massive IoT, also taking into account the interference between devices.

The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis, and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this article, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss the challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.

Wireless communication is gaining popularity in the industry for its simple deployment, mobility, and low cost. Ultralow latency and high reliability requirements of mission-critical industrial applications are highly demanding for wireless communication, and the indoor industrial environment is hostile to wireless communication due to the richness of reflection and obstacles. Assessing the effect of the industrial environment on the reliability and latency of wireless communication is a crucial task, yet it is challenging to accurately model the wireless channel in various industrial sites. In this article, based on the comprehensive channel measurement results from the National Institute of Standards and Technology at 2.245 and 5.4 GHz, we quantify the reliability degradation of wireless communication in multipath fading channels. A delay optimization based on the channel characterization is then proposed to minimize packet transmission times of a cyclic prefix orthogonal frequency division multiplexing system under a reliability constraint at the physical layer. When the transmission bandwidth is abundant and the payload is short, the minimum transmission time is found to be restricted by the optimal cyclic prefix duration, which is correlated with the communication distance. Results further reveal that using relays may, in some cases, reduce end-to-end latency in industrial sites, as achievable minimum transmission time significantly decreases at short communication ranges.

Large online music databases under Creative Commons licenses are rarely recorded by well-known artists, therefore conventional metadata-based search is insufficient in their adaptation to instrument players’ needs. The emerging class of smart musical instruments (SMIs) can address this challenge. Thanks to direct internet connectivity and embedded processing, SMIs can send requests to repositories and reproduce the response for improvisation, composition, or learning purposes. We present a smart guitar prototype that allows retrieving songs from large online music databases using criteria different from conventional music search, which were derived from interviewing 30 guitar players. We investigate three interaction methods coupled with four search criteria (tempo, chords, key and tuning) exploiting intelligent capabilities in the instrument: (i) keywords-based retrieval using an embedded touchscreen; (ii) cloud-computing where recorded content is transmitted to a server that extracts relevant audio features; (iii) edge-computing where the guitar detects audio features and sends the request directly. Overall, the evaluation of these methods with beginner, intermediate, and expert players showed a strong appreciation for the direct connectivity of the instrument with an online database and the approach to the search based on the actual musical content rather than conventional textual criteria, such as song title or artist name.

Wirelessly-powered sensor networks (WPSNs) are becoming increasingly important in different monitoring applications. We consider a WPSN where a multiple-antenna base station, which is dedicated for energy transmission, sends pilot signals to estimate the channel state information and consequently shapes the energy beams toward the sensor nodes. Given a fixed energy budget at the base station, in this paper, we investigate the novel problem of optimally allocating the power for the channel estimation and for the energy transmission. We formulate this non-convex optimization problem for general channel estimation and beamforming schemes that satisfy some qualification conditions. We provide a new solution approach and a performance analysis in terms of optimality and complexity. We also present a closed-form solution for the case where the channels are estimated based on a least square channel estimation and a maximum ratio transmit beamforming scheme. The analysis and simulations indicate a significant gain in terms of the network sensing rate, compared to the fixed power allocation, and the importance of improving the channel estimation efficiency.

Millimeter-wave using large-antenna arrays is a key technological component for the future cellular systems, where it is expected that hybrid beamforming along with quantized phase shifters will be used due to their implementation and cost efficiency. In this paper, we investigate the efficacy of full-duplex mmWave communication with hybrid beamforming using low-resolution phase shifters. We assume that the self-interference can be sufficiently cancelled by a combination of propagation domain and digital self-interference techniques, without any analog self-interference cancellation. We formulate the problem of joint self-interference suppression and downlink beamforming as a mixed-integer nonconvex joint optimization problem. We propose LowRes, a near-to-optimal solution using penalty dual decomposition. Numerical results indicate that LowRes using low-resolution phase shifters perform within 3% of the optimal solution that uses infinite phase shifter resolution. Moreover, even a single quantization bit outperforms half-duplex transmissions, respectively by 29% and 10% for both low and high residual self-interference scenarios, and for a wide range of practical antenna to radio-chain ratios. Thus, we conclude that 1-bit phase shifters suffice for full-duplex millimeter-wave communications, without requiring any additional new analog hardware.

An innovative feature of the 5th Generation mobile network (5G) is to consider industrial applications as use cases for which its new radio access, 5G New Radio, aims to provide ultra low latency and ultra high reliability performance. These requirements are fulfilled by minimizing standard performance indicators such as end-to-end latency and packet error rate. However, industrial control applications typically require periodic exchange of small data, where the ability of networks to support short and deterministic cycle times is the main key performance indicator. This paper proposes a methodology to evaluate the achievable cycle time of an industrial network deployed over the 5G New Radio specifications. Numerical results shows that 5G can achieve millisecond level cycle time with network size of several hundred, which is promising for many factory automation applications.

To make the system available at low-cost, millimeter-wave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large hybrid analog-digital arrays is that the digital baseband cannot directly access the signal to/from each antenna. To address this limitation, recent research has focused on retransmissions, iterative precoding, and subspace decomposition methods. Unlike these approaches that exploited the channel's low-rank, in this work we exploit the sparsity of the received signal at both the transmit/receive antennas. While the signal itself is de facto dense, it is well-known that most signals are sparse under an appropriate choice of basis. By delving into the structured compressive sensing (CS) framework and adapting them to variants of the mmWave hybrid architectures, we provide methodologies to recover the analog signal at each antenna from the (low-dimensional) digital signal. Moreover, we characterizes the minimal numbers of measurement and RF chains to provide this recovery, with high probability. We discuss their applications to common variants of the hybrid architecture. By leveraging the inherent sparsity of the received signal, our analysis reveals that a hybrid MIMO system can be "turned into" a fully digital one: the number of needed RF chains increases logarithmically with the number of antennas.

Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More specifically, we propose a framework that jointly learns the input-output mapping as well as the most representative samples of the dataset (sufficient dataset). Our analytical results show that the cardinality of the sufficient dataset increases sub-linearly with respect to the original dataset size. Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error.

In this paper, we consider a mobile edge computing (MEC)network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC as a whole. For the offloading users, non-orthogonal multiple access (NOMA)is adopted for information transmission. We consider rate-adaptive computational tasks and aim at maximizing the sum computation rate of all users by jointly optimizing the individual computing mode selection (local computing or offloading), the time allocations for energy transfer and for information transmission, together with the local computing speed or the transmission power level. The major difficulty of the rate maximization problem lies in the combinatorial nature of the multiuser computing mode selection and its involved coupling with the time allocation. We also study the case where the offloading users adopt time division multiple access (TDMA)as a benchmark, and derive the optimal time sharing among the users. We show that the maximum achievable rate is the same for the TDMA and the NOMA system, and in the case of NOMA it is independent from the decoding order, which can be exploited to improve system fairness. To maximize the sum computation rate, for the mode selection we propose a greedy solution based on the wireless channel gains, combined with the optimal allocation of energy transfer time. Numerical results show that the proposed solution maximizes the computation rate in homogeneous networks, and binary offloading leads to significant gains. Moreover, NOMA increases the fairness of rate distribution among the users significantly, when compared with TDMA.

In a typical wirelessly powered sensor network (WPSN), wireless chargers provide energy to sensor nodes by using wireless energy transfer (WET). The chargers can greatly improve the lifetime of a WPSN using energy beamforming by a proper charging scheduling of energy beams. However, the supplied energy still may not meet the demand of the energy of the sensor nodes. This issue can be alleviated by deploying redundant sensor nodes, which not only increase the total harvested energy but also decrease the energy consumption per node provided that an efficient scheduling of the sleep/awake of the nodes is performed. Such a problem of joint optimal sensor deployment, WET scheduling, and node activation is posed and investigated in this paper. The problem is an integer optimization that is challenging due to the binary decision variables and non-linear constraints. Based on the analysis of the necessary condition such that the WPSN be immortal, we decouple the original problem into a node deployment problem and a charging and activation scheduling problem. Then, we propose an algorithm and prove that it achieves the optimal solution under a mild condition. The simulation results show that the proposed algorithm reduces the needed nodes to deploy by approximately 16%, compared with a random-based approach. The simulation also shows that if the battery buffers are large enough, the optimality condition will be easy to meet.

Due to the increasing density of wireless devices, the ever-growing demands for extremely high data rates, and the spectrum scarcity at the sub-6 GHz bands, making use of the spectrum-rich millimeter-wave (mmWave) frequencies is among the most important technology trends for future wireless networks. The major commercial potential of mmWave networks has led to mmWave being considered a key element for 5G-and-beyond mobile cellular networks, as well as for emerging Gbps-speed Wi-Fi networks based on the IEEE 802.11ad and draft IEEE 802.11ay standards. Despite this intense interest in mmWave communications from both the research community and industry, much fundamental research is still needed, especially at the higher layers of the networking stack.

Ultra-high reliable and low-latency communication (URLLC) is envisaged to support emerging applications with strict latency and reliability requirements. Critical industrial control is among the most important URLLC applications where the stringent requirements make the deployment of wireless networks critical, especially as far as latency is concerned. Since the amount of data exchanged in critical industrial communications is generally small, an effective way to reduce the latency is to minimize the packet's synchronization overhead, starting from the physical layer (PHY). This paper proposes to use a short one-symbol PHY preamble for critical wireless industrial communications, reducing significantly the transmission latency with respect to other wireless standards. Dedicated packet detection and synchronization algorithms are discussed, analyzed, and tuned to ensure that the required reliability level is achieved with such extremely short preamble. Theoretical analysis, simulations, and experiments show that detection error rates smaller than 10-6can be achieved with the proposed preamble while minimizing the latencies.

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission-critical communication services requiring latency in the order of milliseconds or submilliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross-layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of submilliseconds latency. In this paper, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent tradeoffs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research.

The underutilized millimeter-wave (mm-wave) band is a promising candidate to enable extremely high data rate communications in future wireless networks. However, the special characteristics of the mm-wave systems such as high vulnerability to obstacles (due to high penetration loss) and to mobility (due to directional communications) demand a careful design of the association between the clients and access points (APs). This challenge can be addressed by distributed association techniques that gracefully adapt to wireless channel variations and client mobilities. We formulated the association problem as a mixed-integer optimization aiming to maximize the network throughput with proportional fairness guarantees. This optimization problem is solved first by a distributed dual decomposition algorithm, and then by a novel distributed auction algorithm where the clients act asynchronously to achieve near-to-optimal association between the clients and APs. The latter algorithm has a faster convergence with a negligible drop in the resulting network throughput. A distinguishing novel feature of the proposed algorithms is that the resulting optimal association does not have to be re-computed every time the network changes (e.g., due to mobility). Instead, the algorithms continuously adapt to the network variations and are thus very efficient. We discuss the implementation of the proposed algorithms on top of existing communication standards. The numerical analysis verifies the ability of the proposed algorithms to optimize the association and to maintain optimality in the time-variant environments of the mm-wave networks.

In this paper, we present some contributions from our recent investigation. We address the open issue of interference coordination for sub-28 GHz millimeter-wave communication, by proposing fast- converging coordination algorithms, for dense multi-user multi-cell networks. We propose to optimize a lower bound on the network sum-rate, after investigating its tightness. The bound in question results in distributed optimization, requiring local information at each base station and user. We derive the optimal solution to the transmit and receive filter updates, that we dub non-homogeneous waterfilling, and show its convergence to a stationary point of the bound. We also underline a built-in mechanism to turn-off data streams with low SINR, and allocate power to high-SNR streams. This `stream control' is a at the root of the fast-converging nature of the algorithm. Our numerical result conclude that low- overhead coordination offers large gains, for dense sub-28 GHz systems. These findings bear direct relevance to the ongoing discussions around 5G New Radio.

We address a generalization of change point detection with the purpose of detecting the change locations and the levels of clusters of a piecewise constant signal. Our approach is to model it as a nonparametric penalized least square model selection on a family of models indexed over the collection of partitions of the design points and propose a computationally efficient algorithm to approximately solve it. Statistically, minimizing such a penalized criterion yields an approximation to the maximum a-posteriori probability (MAP) estimator. The criterion is then analyzed and an oracle inequality is derived using a Gaussian concentration inequality. The oracle inequality is used to derive on one hand conditions for consistency and on the other hand an adaptive upper bound on the expected square risk of the estimator, which statistically motivates our approximation. Finally, we apply our algorithm to simulated data to experimentally validate the statistical guarantees and illustrate its behavior.

Industrial applications pose the most stringent requirements to the underlying communication networks. Wireless industrial communication is gaining popularity due to its cost-effectiveness, flexibility and capability of functioning in harsh environments. However, to replace the wired counterpart in the most critical applications performing real-time control and monitoring, the according requirements in terms of latency, reliability and determinism must be met, the latency among which is found to be the bottleneck. To improve the latency performance of wireless communication, modification or new techniques should be developed. Moreover, medium access control (MAC) design should be based on valid timing parameters, as to achieve ultra-low latency, the timing parameters are pushed to the limits. In this paper, we consider to provide fundamental timing constraints as valid inputs for MAC design for wireless high performance network. We start from determining the fundamental constraints as well as the affecting factor in the timing perspective, and then we review and analyze some state-of-art wireless implementations in terms of the timing indexes. Based on the investigation and analysis, realistic timing constraints of different algorithms, hardware, mechanisms are presented, and three main directions for MAC design in wireless high performance network are outlined.

The lifetime of a wireless sensor network (WSN) determines how long the network can be used to monitor the area of interest. Hence, it is one of the most important performance metrics for WSN. The approaches used to prolong the lifetime can be briefly divided into two categories: reducing the energy consumption, such as designing an efficient routing, and providing extra energy, such as using wireless energy transfer (WET) to charge the nodes. Contrary to the previous line of work where only one of those two aspects is considered, we investigate these two together. In particular, we consider a scenario where dedicated wireless chargers transfer energy wirelessly to sensors. The overall goal is to maximize the minimum sampling rate of the nodes while keeping the energy consumption of each node smaller than the energy it receives. This is done by properly designing the routing of the sensors and the WET strategy of the chargers. Although such a joint routing and energy beamforming problem is non-convex, we show that it can be transformed into a semi-definite optimization problem (SDP). We then prove that the strong duality of the SDP problem holds, and hence, the optimal solution of the SDP problem is attained. Accordingly, the optimal solution for the original problem is achieved by a simple transformation. We also propose a lowcomplexity approach based on pre-determined beamforming directions. Moreover, based on the alternating direction method of multipliers, the distributed implementations of the proposed approaches are studied. The simulation results illustrate the significant performance improvement achieved by the proposed methods. In particular, the proposed energy beamforming scheme significantly outperforms the schemes where one does not use energy beamforming, or one does not use optimized routing. A thorough investigation of the effect of system parameters, including the number of antennas, the number of nodes, and the number of chargers, on the system performance is provided. The promising convergence behavior of the proposed distributed approaches is illustrated.

In wireless communication networks, interference models are routinely used for tasks, such as performance analysis, optimization, and protocol design. These tasks are heavily affected by the accuracy and tractability of the interference models. Yet, quantifying the accuracy of these models remains a major challenge. In this paper, we propose a new index for assessing the accuracy of any interference model under any network scenario. Specifically, it is based on a new index that quantifies the ability of any interference model in correctly predicting harmful interference events, that is, link outages. We consider specific wireless scenario of both conventional sub-6 GHz and millimeter-wave networks and demonstrate how our index yields insights into the possibility of simplifying the set of dominant interferers, replacing a Nakagami or Rayleigh random fading by an equivalent deterministic channel, and ignoring antenna sidelobes. Our analysis reveals that in highly directional antenna settings with obstructions, even simple interference models (such as the classical protocol model) are accurate, while with omnidirectional antennas, more sophisticated and complex interference models (such as the classical physical model) are necessary. Our new approach makes it possible to adopt the simplest interference model of adequate accuracy for every wireless network.

Although the benefits of precoding and combining of data streams are widely recognized, the potential of precoding the pilot signals at the user equipment (UE) side and combining them at the base station (BS) side has not received adequate attention. This paper considers a multiuser multiple input multiple output (MU-MIMO) cellular system in which the BS acquires channel state information (CSI) by means of uplink pilot signals and proposes pilot precoding and combining to improve the CSI quality. We first evaluate the channel estimation performance of a baseline scenario in which CSI is acquired with no pilot precoding. Next, we characterize the channel estimation error when the pilot signals are precoded by spatial filters that asymptotically maximize the channel estimation quality. Finally, we study the case when, in addition to pilot precoding at the UE side, the BS utilizes the second order statistics of the channels to further improve the channel estimation performance. The analytical and numerical results show that, specially in scenarios with large number of antennas at the BS and UEs, pilot precoding and combining has a great potential to improve the channel estimation quality in MU-MIMO systems.

Wireless sensor networks (WSNs) consist of energy limited sensor nodes, which limits the network lifetime. Such a lifetime can be prolonged by employing the emerging technology of wireless energy transfer (WET). In WET systems, the sensor nodes can harvest wireless energy from wireless charger, which can use energy beamforming to improve the efficiency. In this paper, a scenario where dedicated wireless chargers with multiple antennas use energy beamforming to charge sensor nodes is considered. The energy beamforming is coupled with the energy consumption of sensor nodes in terms of data routing, which is one novelty of the paper. The energy beamforming and the data routing are jointly optimized by a non-convex optimization problem. This problem is transformed into a semidefinite optimization problem, for which strong duality is proved, and thus the optimal solution exists. It is shown that the optimal solution of the semi-definite programming problem allows to derive the optimal solution of the original problem. The analytical and numerical results show that optimal energy beamforming gives two times better monitoring performance than that of WET without using energy beamforming.

To increase the spectral efficiency of wireless networks without requiring full-duplex capability of user devices, a potential solution is the recently proposed three-node full-duplex mode. To realize this potential, networks employing three-node full-duplex transmissions must deal with self-interference and user-to-user interference, which can be managed by frequency channel and power allocation techniques. Whereas previous works investigated either spectral efficient or fair mechanisms, a scheme that balances these two metrics among users is investigated in this paper. This balancing scheme is based on a new solution method of the multi-objective optimization problem to maximize the weighted sum of the per-user spectral efficiency and the minimum spectral efficiency among users. The mixed integer nonlinear nature of this problem is dealt by Lagrangian duality. Based on the proposed solution approach, a low-complexity centralized algorithm is developed, which relies on large scale fading measurements that can be advantageously implemented at the base station. Numerical results indicate that the proposed algorithm increases the spectral efficiency and fairness among users without the need of weighting the spectral efficiency. An important conclusion is that managing user-to-user interference by resource assignment and power control is crucial for ensuring spectral efficient and fair operation of full-duplex networks.

The lifetime of a wireless sensor network (WSN) is limited by the lifetime of the individual sensor nodes. A promising technique to extend the lifetime of the nodes is wireless energy transfer. The WSN lifetime can also be extended by exploiting the redundancy in the nodes' deployment, which allows the implementation of duty-cycling mechanisms. In this paper, the joint problem of optimal sensor node deployment and WET scheduling is investigated. Such a problem is formulated as an integer optimization whose solution is challenging due to the binary decision variables and non-linear constraints. To solve the problem, an approach based on two steps is proposed. First, the necessary condition for which the WSN is immortal is established. Based on this result, an algorithm to solve the node deployment problem is developed. Then, the optimal WET scheduling is given by a scheduling algorithm. The WSN is shown to be immortal from a networking point of view, given the optimal deployment and WET scheduling. Theoretical results show that the proposed algorithm achieves the optimal node deployment in terms of the number of deployed nodes. In the simulation, it is shown that the proposed algorithm reduces significantly the number of nodes to deploy compared to a random-based approach. The results also suggest that, under such deployment, the optimal scheduling and WET can make WSNs immortal.

Although the benefits of precoding and combining data signals are widely recognized, the potential of these techniques for pilot transmission is not fully understood. This is particularly relevant for multiuser multiple-input multiple-output (MU-MIMO) cellular systems using millimeter-wave (mmWave) communications, where multiple antennas have to be used both at the transmitter and the receiver to overcome the severe path loss. In this paper, we characterize the gains of pilot precoding and combining in terms of channel estimation quality and achievable data rate. Specifically, we consider three uplink pilot transmission scenarios in an mmWave MU-MIMO cellular system: 1) non-precoded and uncombined; 2) precoded but uncombined; and 3) precoded and combined. We show that a simple precoder that utilizes only the second-order statistics of the channel reduces the variance of the channel estimation error by a factor that is proportional to the number of user equipment (UE) antennas. We also show that using a linear combiner design based on the second-order statistics of the channel significantly reduces multiuser interference and provides the possibility of reusing some pilots. Specifically, in the large antenna regime, pilot precoding and combining help to accommodate a large number of UEs in one cell, significantly improve channel estimation quality, boost the signal-to-noise ratio of the UEs located close to the cell edges, alleviate pilot contamination, and address the imbalanced coverage of pilot and data signals.

In cellular networks, the three-node full-duplex transmission mode has the potential to increase spectral efficiency without requiring full-duplex capability of users. Consequently, three-node full-duplex in cellular networks must deal with self-interference and user-to-user interference, which can be managed by power control and user-frequency assignment techniques. This paper investigates the problem of maximizing the sum spectral efficiency by jointly determining the transmit powers in a distributed fashion, and assigning users to frequency channels. The problem is formulated as a mixed-integer nonlinear problem, which is shown to be non-deterministic polynomial-time hard. We investigate a close-to-optimal solution approach by dividing the joint problem into a power control problem and an assignment problem. The power control problem is solved by Fast-Lipschitz optimization, while a greedy solution with guaranteed performance is developed for the assignment problem. Numerical results indicate that compared with the half-duplex mode, both spectral and energy efficiencies of the system are increased by the proposed algorithm. Moreover, results show that the power control and assignment solutions have important, but opposite roles in scenarios with low or high self-interference cancellation. When the self-interference cancellation is high, user-frequency assignment is more important than power control, while power control is essential at low self-interference cancellation.

In Wireless Sensor Networks (WSNs), to supply energy to the sensor nodes, wireless energy transfer (WET) is a promising technique. One of the most efficient procedures to transfer energy to the sensor nodes consists in using a sharp wireless energy beam from the base station to each node at a time. A natural fundamental question is what is the lifetime ensured by WET and how to maximize the network lifetime by scheduling the transmissions of the energy beams. In this paper, such a question is addressed by posing a new lifetime maximization problem for WET enabled WSNs. The binary nature of the energy transmission process introduces a binary constraint in the optimization problem, which makes challenging the investigation of the fundamental properties of WET and the computation of the optimal solution. The sufficient condition for which the WET makes WSNs immortal is established as function of the WET parameters. When such a condition is not met, a solution algorithm to the maximum lifetime problem is proposed. The numerical results show that the lifetime achieved by the proposed algorithm increases by about 50% compared to the case without WET, for a WSN with a small to medium size number of nodes. This suggests that it is desirable to schedule WET to prolong lifetime of WSNs having small or medium network sizes.

We develop a new framework for measuring and comparing the accuracy of any wireless interference models used in the analysis and design of wireless networks. Our approach is based on a new index that assesses the ability of the interference model to correctly predict harmful interference events, i.e., link outages. We use this new index to quantify the accuracy of various interference models used in the literature, under various scenarios such as Rayleigh fading wireless channels, directional antennas, and blockage (impenetrable obstacles) in the network. Our analysis reveals that in highly directional antenna settings with obstructions, even simple interference models (e.g., the classical protocol model) are accurate, while with omnidirectional antennas, more sophisticated and complex interference models (e.g., the classical physical model) are necessary. Our new approach makes it possible to adopt the appropriate interference model of adequate accuracy and simplicity in different settings.

Ongoing IEC 61850 standardization activities aim at improved grid reliability through advanced monitoring and remote control services in medium- and low-voltage. However, extending energy automation beyond the substation boundaries introduces the need for timely and reliable information exchange over wide areas. LTE appears as a promising solution since it supports extensive coverage, low latency, high throughput and Quality-of-Service (QoS) differentiation. In this paper, the feasibility of implementing IEC 61850 services over public LTE infrastructure is investigated. Since standard LTE cannot meet the stringent latency requirements of such services, a new LTE QoS class is introduced along with a new LTE scheduler that prioritizes automation traffic with respect to background human-centric traffic. Two representative grid automation services are considered, a centralized (MMS) and a distributed one (GOOSE), and the achievable latency/throughput performance is evaluated on a radio system simulator platform. Simulations of realistic overload scenarios demonstrate that properly designed LTE schedulers can successfully meet the performance requirements of IEC 61850 services with negligible impact on background traffic.

Millimeter wave (mmWave) spectrum is one of the frontiers in the evolution towards the next generation of the wireless communication systems, which can provide great performance benefits at the variform access and backbone networks. However, at the access level the typical rapidly fading behavior of the mmWave channel imposes the careful design of client association to access points (APs), as well as relaying to other clients, which can act as bridge toward the APs. This challenge is hereby addressed by a distributed approach that optimally solves the joint client association and relaying problem. The problem is posed as a novel multi-dimensional assignment problem, for which an original solution method is established by a series of transformations that lead to a tractable minimum cost flow problem. The method allows to design distributed auction algorithms where the clients and relays act asynchronously to achieve optimal client-relay-AP association. It is shown that the algorithms converge to a solution that maximizes the total network throughput within a desired bound.

Contamination in drinkable water distribution networks can be potentially monitored by new and agile mobile sensor networks. These sensor networks are composed of static sensor nodes, which are pre-installed, of mobile sensor nodes, which are released into the water network for a more punctual monitoring, and of sink nodes, which are used to collect data from mobile sensor nodes. Thus, the activation of the sink nodes as well as the release locations of the mobile nodes must be carefully decided to ensure timely and accurate event detections. Unfortunately, no approach can be found in the literature to optimally determine the release locations of the mobile sensor nodes and the activation of the sink nodes. In this paper, a novel optimization approach to solve such a problem is posed. The problem is particularly challenging due to the potential large size of the networks, the undetermined movement of the mobile sensor nodes, the integer decision variables associated to the release locations of these mobile sensor nodes, and the binary decision variables associated to the activation of the sink nodes. To account for the mobile node mobility across the water distribution network, a stochastic mobility model is considered. It is shown that the objective function of the optimization problem exhibits submodular properties, which allow establishing a mobile nodes release algorithm. The benefits and efficiency of the proposed algorithm are illustrated by analysis and numerical evaluations. It is concluded that the proposed optimization based approach allows efficient monitoring of water distribution networks by mobile sensor nodes.

This paper investigates the extent to which spectrum sharing in millimeter-wave (mmWave) networks with multiple cellular operators is a viable alternative to traditional dedicated spectrum allocation. Specifically, we develop a general mathematical framework to characterize the performance gain that can be obtained when spectrum sharing is used, as a function of the underlying beamforming, operator coordination, bandwidth, and infrastructure sharing scenarios. The framework is based on joint beamforming and cell association optimization, with the objective of maximizing the long-term throughput of the users. Our asymptotic and non-asymptotic performance analyses reveal five key points: 1) spectrum sharing with light on-demand intra- and inter-operator coordination is feasible, especially at higher mmWave frequencies (for example, 73 GHz); 2) directional communications at the user equipment substantially alleviate the potential disadvantages of spectrum sharing (such as higher multiuser interference); 3) large numbers of antenna elements can reduce the need for coordination and simplify the implementation of spectrum sharing; 4) while inter-operator coordination can be neglected in the large-antenna regime, intra-operator coordination can still bring gains by balancing the network load; and 5) critical control signals among base stations, operators, and user equipment should be protected from the adverse effects of spectrum sharing, for example by means of exclusive resource allocation. The results of this paper, and their extensions obtained by relaxing some ideal assumptions, can provide important insights for future standardization and spectrum policy.

In wireless network resource allocation, the radio power control problems are often solved by fixed point algorithms. Although these algorithms give feasible problem solutions, such solutions often lack notion of problem optimality. This paper reconsiders well-known fixed-point algorithms, such as those with standard and type-II standard interference functions, and investigates the conditions under which they give optimal solutions. The optimality is established by the recently proposed fast-Lipschitz optimization framework. To apply such a framework, the analysis is performed by a logarithmic transformation of variables that gives tractable fast-Lipschitz problems. It is shown how the logarithmic problem constraints are contractive by the standard or type-II standard assumptions on the power control problem, and how sets of cost functions fulfill the fast-Lipschitz qualifying conditions. The analysis on nonmonotonic interference function allows establishing a new qualifying condition for fast-Lipschitz optimization. The results are illustrated by considering power control problems with standard interference function, problems with type-II standard interference functions, and a case of subhomogeneous power control problems. Given the generality of fast-Lipschitz optimization compared to traditional methods for resource allocation, it is concluded that such an optimization may help to determine the optimality of many resource allocation problems in wireless networks.

We study the repair problem in distributed storage systems where storage nodes are connected through packet erasure channels and some nodes are dedicated to repair [termed as dedicated-for-repair (DR) storage nodes]. We first investigate the minimum required repair-bandwidth in an asymptotic setup, in which the stored file is assumed to have an infinite size. The result shows that the asymptotic repair-bandwidth over packet erasure channels with a fixed erasure probability has a closed-form relation to the repair-bandwidth in lossless networks. Next, we show the benefits of DR storage nodes in reducing the repair bandwidth, and then we derive the necessary minimal storage space of DR storage nodes. Finally, we study the repair in a nonasymptotic setup, where the stored file size is finite. We study the minimum practical-repair-bandwidth, i.e., the repair-bandwidth for achieving a given probability of successful repair. A combinatorial optimization problem is formulated to provide the optimal practical-repair-bandwidth for a given packet erasure probability. We show the gain of our proposed approaches in reducing the repair-bandwidth.

Millimeter-wave (mmWave) communication systems use a large number of antenna elements that can potentially overcome severe channel attenuation by narrow beamforming. Narrow-beam operation in mmWave networks also reduces multiuser interference, introducing the concept of noise-limited wireless networks as opposed to interference-limited ones. The noise-limited or interference-limited regime heavily reflects on the medium access control (MAC) layer throughput and on proper resource allocation and interference management strategies. Yet, these regimes are ignored in current approaches to mmWave MAC layer design, with the potential disastrous consequences on the communication performance. In this paper, we investigate these regimes in terms of collision probability and throughput. We derive tractable closed-form expressions for the collision probability and MAC layer throughput of mmWave ad hoc networks, operating under slotted ALOHA. The new analysis reveals that mmWave networks may exhibit a non-negligible transitional behavior from a noise-limited regime to an interference-limited one, depending on the density of the transmitters, density and size of obstacles, transmission probability, operating beamwidth, and transmission power. Such transitional behavior necessitates a new framework of adaptive hybrid resource allocation procedure, containing both contention-based and contention-free phases with on-demand realization of the contention-free phase. Moreover, the conventional collision avoidance procedure in the contention-based phase should be revisited, due to the transitional behavior of interference, to maximize throughput/delay performance of mmWave networks. We conclude that, unless proper hybrid schemes are investigated, the severity of the transitional behavior may significantly reduce throughput/delay performance of mmWave networks.

A promising new transmission mode in cellular networks is the three-node full-duplex (FD) mode, which involves a base station with FD capability and two half-duplex user transmissions on the same frequency channel for uplink and downlink. The three-node FD mode can increase spectral efficiency, especially in the low transmit power regime, without requiring FD capability at user devices. However, when a large set of users are scheduled in this mode, self-interference at the base station and user-to-user interference can substantially hinder the potential gains of FD communications. This paper investigates the problem of grouping users to pairs and assigning frequency channels to each pair in a spectral efficient and fair manner. Specifically, the joint problem of user uplink/downlink frequency channel pairing and power allocation is formulated as a mixed integer nonlinear problem that is solved by a novel joint fairness assignment maximization algorithm. Realistic system-level simulations indicate that the spectral efficiency of the users having the lowest spectral efficiency is increased by the proposed algorithm, while a high ratio of connected users in different loads and self-interference levels is maintained.

In hierarchical cognitive radio networks, unlicensed secondary users can increase their achievable rates by assisting licensed primary user transmissions via cooperation. In this paper, a novel approach to maximizing the transmission rates in the secondary network by optimizing the relay selection, the secondary transmit powers, and the cooperative relaying power splitting parameters is proposed. The resulting optimization problem is mixed integer and nonconvex, which makes it NP hard to find the optimal solutions. Therefore, centralized and distributed solution methods to find near-to-optimal solutions of this challenging problem are proposed. The methods are based on iteratively solving the secondary relay selection by a greedy approach, and the optimal power allocation problem by a fixed-point approach together with alternating direction method of multipliers. It is established that both centralized and distributed solution methods always converge. The numerical results illustrate how the performance of the proposed solution methods depend on the primary performance margins, and show that they give a near-to-optimal solution in few iterations.

Millimeter wave (mmWave) systems are emerging as an essential technology for enabling extremely high data rate wireless communications. The main limiting factors of mmWave systems are blockage (high penetration loss) and deafness (misalignment between the beams of the transmitter and receiver). To alleviate these problems, it is imperative to incorporate efficient association and relaying between terminals and access points. Unfortunately, the existing association techniques are designed for the traditional interference-limited networks, and thus are highly suboptimal for mmWave communications due to narrow-beam operations and the resulting non-negligible interference-free behavior. This paper introduces a distributed approach that solves the joint association and relaying problem in mmWave networks considering the load balancing at access points. The problem is posed as a novel stochastic optimization problem, which is solved by distributed auction algorithms where the clients and relays act asynchronously to achieve optimal client-relay-access point association. It is shown that the algorithms provably converge to a solution that maximizes the aggregate logarithmic utility within a desired bound. Numerical results allow quantification of the performance enhancements introduced by the relays, and the substantial improvements of the network throughput and fairness among the clients by the proposed association method as compared to standard approaches. It is concluded that mmWave communications with proper association and relaying mechanisms can support extremely high data rates, connection reliability, and fairness among the clients.

The recent development of low cost wireless sensors enables water monitoring through dense wireless sensor networks (WSN). Sensor nodes are battery powered devices, and hence their limited energy resources have to be optimally managed. The latest advancements in compressive sensing (CS) provide ample promise to increase WSNs lifetime by limiting the amount of measurements that have to be collected. Additional energy savings can be achieved through CS-based scheduling schemes that activate only a limited number of sensors to sense and transmit their measurements, whereas the rest are turned off. The ultimate objective is to maximize network lifetime without sacrificing network connectivity and monitoring performance. This problem can be approximated by an energy balancing approach that consists of multiple simpler subproblems, each of which corresponds to a specific time period. Then, the sensors that should be activated within a given period can be optimally derived through dynamic programming. The complexity of the proposed CS-based scheduling scheme is characterized and numerical evaluation reveals that it achieves comparable monitoring performance by activating only a fraction of the sensors.

Millimeter wave (mmWave) wireless networks rely on narrow beams to support multi-gigabit data rates. Nevertheless, the alignment of transmitter and receiver beams is a time-consuming operation, which introduces an alignment-throughput tradeoff. A wider beamwidth reduces the alignment overhead, but leads also to reduced directivity gains. Moreover, existing mmWave standards schedule a single transmission in each time slot, although directional communications facilitate multiple concurrent transmissions. In this paper, a joint consideration of the problems of beamwidth selection and scheduling is proposed to maximize effective network throughput. The resulting optimization problem requires exact knowledge of network topology, which may not be available in practice. Therefore, two standard-compliant approximation algorithms are developed, which rely on underestimation and overestimation of interference. The first one aims to maximize the reuse of available spectrum, whereas the second one is a more conservative approach that schedules together only links that cause no interference. Extensive performance analysis provides useful insights on the directionality level and the number of concurrent transmissions that should be pursued. Interestingly, extremely narrow beams are in general not optimal.

Caching at the network edge is considered a promising solution for addressing the ever-increasing traffic demand of mobile devices. The problem of proactive content replication in hierarchical cache networks, which consist of both network edge and core network caches, is considered in this paper. This problem arises because network service providers wish to efficiently distribute content so that user-perceived performance is maximized. Nevertheless, current high-complexity replication algorithms are impractical due to the vast number of involved content items. Clustering algorithms inspired from machine learning can be leveraged to simplify content replication and reduce its complexity. Specifically, similar items could be clustered together, e.g., according to their popularity in space and time. Replication on a cluster-level is a problem of substantially smaller dimensionality, but it may result in suboptimal decisions compared to item-level replication. The factors that cause performance loss are identified and a clustering scheme that addresses the specific challenges of content replication is devised. Extensive numerical evaluations, based on realistic traffic data, demonstrate that for reasonable cluster sizes the impact on actual performance is negligible.

Strict quality of service requirements of industrial applications, challenged by harsh environments and huge interference especially in multi-vendor sites, demand incorporation of cognition in industrial wireless sensor networks (IWSNs). In this paper, a distributed protocol of light complexity for congestion regulation in cognitive IWSNs is proposed to improve the channel utilization while ensuring predetermined performance for specific devices, called primary devices. By sensing the congestion level of a channel with local measurements, a novel congestion control protocol is proposed by which every device decides whether it should continue operating on the channel, or vacate it in case of saturation. Such a protocol dynamically changes the congestion level based on variations of non-stationary wireless environment as well as traffic demands of the devices. The proposed protocol is implemented on STM32W108 chips that offer IEEE 802.15.4 standard communications. Experimental results confirm substantial performance enhancement compared to the original standard, while imposing almost no signaling/computational overhead. In particular, channel utilization is increased by 56% with fairness and delay guarantees. The presented results provide useful insights on low-complexity adaptive congestion control mechanism in IWSNs.

Empirical studies show that cruising for car parking accounts for a non-negligible amount of the daily traffic, especially in central areas of large cities. Therefore, mechanisms for minimizing traffic from cruising directly affect the dynamics of traffic congestions. One way to minimizing cruising traffic is efficient car-parking-slot assignment. Usually, the related design problems are combinatorial and the worst-case complexity of optimal methods grows exponentially with the problem sizes. As a result, almost all existing methods for parking slot assignment are simple and greedy approaches, where each car or the user is assigned a free parking slot, which is closer to its destination. Moreover, no emphasis is placed to optimize any form of fairness among the users as the a social benefit. In this paper, the fairness as a metric for modeling the aggregate social benefit of the users is considered. An algorithm based on Lagrange duality is developed for car-parking-slot assignment. Numerical results illustrate the performance of the proposed algorithm compared to the optimal assignment and a greedy method.

Accurate estimation of the position of network nodes is essential, e.g., in localization, geographic routing, and vehicular networks. Unfortunately, typical positioning techniques based on ranging or on velocity and angular measurements are inherently limited. To overcome the limitations of specific positioning techniques, the fusion of multiple and heterogeneous sensor information is an appealing strategy. In this paper, we investigate the fundamental performance of linear fusion of multiple measurements of the position of mobile nodes, and propose a new distributed recursive position estimator. The Cramér-Rao lower bounds for the parametric and a-posteriori cases are investigated. The proposed estimator combines information coming from ranging, speed, and angular measurements, which is jointly fused by a Pareto optimization problem where the mean and the variance of the localization error are simultaneously minimized. A distinguished feature of the method is that it assumes a very simple dynamical model of the mobility and therefore it is applicable to a large number of scenarios providing good performance. The main challenge is the characterization of the statistical information needed to model the Fisher information matrix and the Pareto optimization problem. The proposed analysis is validated by Monte Carlo simulations, and the performance is compared to several Kalman-based filters, commonly employed for localization and sensor fusion. Simulation results show that the proposed estimator outperforms the traditional approaches that are based on the extended Kalman filter when no assumption on the model of motion is used. In such a scenario, better performance is achieved by the proposed method, but at the price of an increased computational complexity.

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.

Developing an efficient spectrum access policy enables cognitive radios to dramatically increase spectrum utilization while ensuring the predetermined quality of service levels for primary users (PUs). In this paper, the modeling, performance analysis, and optimization of a distributed secondary network with a random sensing order policy are studied. Specifically, secondary users (SUs) create a random order of available channels upon PUs' return, and then, they find optimal transmission and handoff opportunities in a distributed manner. By a Markov chain analysis, the average throughputs of the SUs and the average interference level among the SUs and the PUs are investigated. A maximization of the secondary network performance in terms of the throughput while keeping under control the average interference is proposed. It is shown that, despite traditional views, a nonzero false alarm in the channel sensing can increase channel utilization, particularly in a dense secondary network where the contention is too high. Then, two simple and practical adaptive algorithms are established to optimize the network. The second algorithm follows the variations of the wireless channels in nonstationary conditions and outperforms even static brute force optimization while demanding few computations. The convergence of the distributed algorithms is theoretically investigated based on the analytical performance indicators established by the Markov chain analysis. Finally, numerical results validate the analytical derivations and demonstrate the efficiency of the proposed schemes. It is concluded that fully distributed sensing order algorithms can lead to substantial performance improvements in cognitive radio networks without the need for centralized management or message passing among the users.

The millimeter-wave (mmWave) frequency band is seen as a key enabler of multigigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which leads to high directivity gains, fully directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. This paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.

Millimeter-wave communications in the 60-GHz band are considered one of the key technologies for enabling multigigabit wireless access. However, the special characteristics of such a band pose major obstacles to the optimal utilization of the wireless resources, where the problem of efficient client association to access points (APs) is of vital importance. In this paper, the client association in 60-GHz wireless access networks is investigated. The AP utilization and the quality of the rapidly vanishing communication links are the control parameters. Because of the tricky nonconvex and combinatorial nature of the client association optimization problem, a novel solution method is developed to guarantee balanced and fair resource allocation. A new distributed, lightweight, and easy-to-implement association algorithm, based on Lagrangian duality theory and subgradient methods, is proposed. It is shown that the algorithm is asymptotically optimal, that is, the relative duality gap diminishes to zero as the number of clients increases .

A framework for the distributed optimization of reactive cognitive networks with multiple secondary users is presented. The secondary users iteratively locate the policy maximizing their aggregate performance under bounds on the maximum performance loss caused to the primary users. The policy accounts for the impact of interference on the dynamics of the primary users' network due to transmission and channel access protocols. To minimize coordination overhead, it is assumed that the secondary users only coordinate the policy, whereas actions in each slot are independently selected by the individual secondary user based on the agreed policy. The distributed optimization technique proposed herein is based on alternating convex optimization. Numerical results are presented assessing the performance of the obtained transmission policy with respect to the optimal centralized and fully-coordinated policy.

Developing an efficient spectrum access policy enables cognitive radios to dramatically increase spectrum utilization while assuring predetermined quality of service levels for the primary users. In this paper, modeling, performance analysis, and optimization of a distributed secondary network with random sensing order policy are studied. Specifically, the secondary users create a random order of the available channels and then find a transmission opportunity in a distributed manner. By a Markov chain analysis, the average throughputs of the secondary users and average interference level between the secondary and primary users are evaluated. Then, a maximization of the performance of the secondary network in terms of throughput while keeping under control the average interference is proposed. A simple and practical adaptive algorithm is established to optimize the network. Finally, numerical results are provided to validate the analytical derivations and demonstrate the performance of the proposed schemes. It is shown that distributed algorithms can achieve substantial performance improvements in cognitive radio networks without the need of centralized operations or management.

Passive tags based on backscattered signals yield low energy consumption and enable a rich set of applications. In this paper, the performance of the dynamic framed ALOHA of the ISO/IEC 18000-6C standard is investigated. A Markov chain model of the protocol is developed by including the effects of the channel under different propagation scenarios. Depending on the air interface parameters and the fading components, it is shown that multiple access interference determines complex and unexpected effects on the system performance. An explicit analysis of the successful interrogation probability is obtained with respect to the number of tags, number of interrogation rounds and fading severity. It is shown that channel statistics may induce significant degradation, although a moderate spread of the fading can be beneficial especially for those contexts that include time-varying conditions.

Efficient handover algorithms are essential for highly performing mobile wireless communications. These algorithms depend on numerous parameters, whose settings must be appropriately optimized to offer a seamless connectivity. Nevertheless, such an optimization is difficult in a time varying context, unless adaptive strategies are used. In this paper, a new approach for the handover optimization is proposed. First, a new modeling of the handover process by a hybrid system that takes as input the handover parameters is established. Then, this hybrid system is used to pose some dynamical optimization approaches where the probability of outage and the probability of handover are considered. Since it is shown that these probabilities are difficult to compute, simple approximations of adequate accuracy are developed. Based on these approximations, a new approach to the solution of the handover optimizations is proposed. A distributed optimization algorithm is then developed to maximize handover performance. From an extensive set of results obtained by numerical computations and simulations, it is shown that the proposed algorithm improves performance of the handover considerably when compared to more traditional approaches.

Although the performance of the medium access control (MAC) of the IEEE 802.15.4 has been investigated under the assumption of ideal wireless channel, the understanding of the cross-layer dynamics between MAC and physical layer is an open problem when the wireless channel exhibits path loss, multi-path fading, and shadowing. The analysis of MAC and wireless channel interaction is essential for consistent performance prediction, correct design and optimization of the protocols. In this paper, a novel approach to analytical modeling of these interactions is proposed. The analysis considers simultaneously a composite channel fading, interference generated by multiple terminals, the effects induced by hidden terminals, and the MAC reduced carrier sensing capabilities. Depending on the MAC parameters and physical layer thresholds, it is shown that the MAC performance indicators over fading channels can be far from those derived under ideal channel assumptions. As novel results, we show to what extent the presence of fading may be beneficial for the overall network performance by reducing the multiple access interference, and how this information can be used for joint selection of MAC and physical layer parameters.

In smart homes, it is essential to reliably detect events including water leakages. A control action, such as shutting the water pipes, relies on reliable event detection. In this demo, a wireless sensor network for detection and localization of events in smart homes is presented. The demo is based on novel distributed detection-estimation and localization algorithms. A graphical user interface to visualize in real-time the network status is developed. Upon a detected event, the user is alerted through a Twitter notification. In the experiments the false alarm probability is improved by 30% and the average relative localization error is 1.7%.

Wireless sensor networks for industrial automation applications must offer timely, reliable, and energy efficient communications at both low and high data rate. While traditional communication technologies between 2.4 GHz and 5 GHz are sometimes incapable to efficiently achieve the aforementioned goals, new communication strategies are emerging, such as millimeterWave communications. In this overview paper, the general requirements that factory and process automation impose on the network design are reviewed. Moreover, this paper presents and qualitatively evaluates the 60 GHz millimeterWave communication technology for automation. It is argued that the upcoming 60 GHz millimeterWave technology brings an enormous potential and can influence the design of the future communication infrastructures in factory and process automation.

Medium access control (MAC) and routing protocols are fundamental blocks in the design of low power and lossy networks (LLNs). As new networking standards are being proposed and different existing research solutions patched, evaluating the performance of the network becomes challenging. Specific solutions that can be individually efficient, when stacked together may have unexpected effects on the overall network behavior. In this paper, we provide an analysis of the fundamental MAC and routing protocols for LLNs: IEEE 802.15.4 MAC and IETF RPL. Moreover, a characterization of their cross-layer interactions is presented by a mathematical description, which is essential to truly understand the protocols mutual effects and their dynamics. Novel metrics that guide the interaction between MAC and routing are compared to existing metrics. Furthermore, a protocol selection mechanism is implemented to select the appropriate routing metric and MAC parameters given specific performance constraints. Analytical and experimental results show that the behavior of the MAC protocol can hurt the performance of the routing protocol and vice versa, unless these two are carefully optimized together.

There have been emerging lots of applications for distributed storage systems e.g., those in wireless sensor networks or cloud storage. Since storage nodes in wireless sensor networks have limited battery, it is valuable to find a repair scheme with optimal transmission costs (e.g., energy). The optimal-cost repair has been recently investigated in a centralized way. However a centralized control mechanism may not be available or is very expensive. For the scenarios, it is interesting to study optimal-cost repair in a decentralized setup. We formulate the optimal-cost repair as convex optimization problems for the network with convex transmission costs. Then we use primal and dual decomposition approaches to decouple the problem into subproblems to be solved locally. Thus, each surviving node, collaborating with other nodes, can minimize its transmission cost such that the global cost is minimized. We further study the optimality and convergence of the algorithms. Finally, we discuss the code construction and determine the field size for finding feasible network codes in our approaches.

A major constraint in deployments of resource-limited networks is the energy consumption related to the battery lifetime of the network nodes. To this end, power efficient digital modulation techniques such as On-Off keying (OOK) are highly attractive. In this paper, a novel complete probabilistic description of the Direct Sequence - Coded Division Multiple Access (DS-CDMA) system with random signatures employing OOK modulation is presented. The system scenario considers simultaneously transmitting nodes in Rayleigh fading conditions. Numerical simulations are provided to support the derived results.

The IEEE 802.15.4 communication protocol is a de-facto standard for wireless applications in industrial and home automation. Although the performance of the medium access control (MAC) of the IEEE 802.15.4 has been thoroughly investigated under the assumption of ideal wireless channel, there is still a lack of understanding of the cross-layer interactions between MAC and physical layer in the presence of realistic wireless channel models that include path loss, multi-path fading and shadowing. In this paper, an analytical model of these dynamics is proposed. The analysis considers simultaneously a composite Rayleigh-lognormal channel fading, interference generated by multiple terminals, the effects induced by hidden terminals, and the MAC reduced carrier sensing capabilities. It is shown that the reliability of the contention-based MAC over fading channels is often far from that derived under ideal channel assumptions. Moreover, it is established to what extent fading may be beneficial for the overall network performance.

In this paper, routing metrics for low power and lossy networks are designed and evaluated. The cross-layer interactions between routing and medium access control (MAC) are explored, by considering the specifications of IETF RPL over the IEEE 802.15.4 MAC. In particular, the experimental study of a reliability metric that extends the expected transmission count (ETX) to include the effects of the level of contention and the parameters at MAC layer is presented. Moreover, a novel metric that guarantees load balancing and increased network lifetime by fulfilling reliability constraints is introduced. The aforementioned metrics are compared to a routing approach based on backpressure mechanism.

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical modelbased techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

Most applications of wireless sensor networks require reliable and timely data communication with maximum possible network lifetime under low traffic regime. These requirements are very critical especially for the stability of wireless sensor and actuator networks. Designing a protocol that satisfies these requirements in a network consisting of sensor nodes with traffic pattern and location varying over time and space is a challenging task. We propose an adaptive optimal duty-cycle algorithm running on top of the IEEE 802.15.4 medium access control to minimize power consumption while meeting the reliability and delay requirements. Such a problem is complicated because simple and accurate models of the effects of the duty cycle on reliability, delay, and power consumption are not available. Moreover, the scarce computational resources of the devices and the lack of prior information about the topology make it impossible to compute the optimal parameters of the protocols. Based on an experimental implementation, we propose simple experimental models to expose the dependency of reliability, delay, and power consumption on the duty cycle at the node and validate it through extensive experiments. The coefficients of the experimental-based models can be easily computed on existing IEEE 802.15.4 hardware platforms by introducing a learning phase without any explicit information about data traffic, network topology, and medium access control parameters. The experimental-based model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while meeting the reliability and delay requirements in the packet transmission. The algorithm is easily implementable on top of the IEEE 802.15.4 medium access control without any modifications of the protocol. An experimental implementation of the distributed adaptive algorithm on a test bed with off-the-shelf wireless sensor devices is presented. The experimental performance of the algorithms is compared to the existing solutions from the literature. The experimental results show that the experimental-based model is accurate and that the proposed adaptive algorithm attains the optimal value of the duty cycle, maximizing the lifetime of the network while meeting the reliability and delay constraints under both stationary and transient conditions. Specifically, even if the number of devices and their traffic configuration change sharply, the proposed adaptive algorithm allows the network to operate close to its optimal value. Furthermore, for Poisson arrivals, the duty-cycle protocol is modeled as a finite capacity queuing system in a star network. This simple analytical model provides insights into the performance metrics, including the reliability, average delay, and average power consumption of the duty-cycle protocol.

To offer flexible quality of service to several classes of applications, the medium access control (MAC) protocol of IEEE 802.15.4 wireless sensor networks (WSNs) combines the advantages of a random access with contention with a time division multiple access (TDMA) without contention. Understanding reliability, delay, and throughput is essential to characterizing the fundamental limitations of the MAC and optimizing its parameters. Nevertheless, there is not yet a clear investigation of the achievable performance of hybrid MAC. In this article, an analytical framework for modeling the behavior of the hybrid MAC protocol of the IEEE 802.15.4 standard is proposed. The main challenge for an accurate analysis is the coexistence of the stochastic behavior of the random access and the deterministic behavior of the TDMA scheme. The analysis is done in three steps. First, the contention access scheme of the IEEE 802.15.4 exponential back-off process is modeled through an extended Markov chain that takes into account channel, retry limits, acknowledgements, unsaturated traffic, and superframe period. Second, the behavior of the TDMA access scheme is modeled by another Markov chain. Finally, the two chains are coupled to obtain a complete model of the hybrid MAC. By using this model, the network performance in terms of reliability, average packet delay, average queuing delay, and throughput is evaluated through both theoretical analysis and experiments. The protocol has been implemented and evaluated on a testbed with off-the-shelf wireless sensor devices to demonstrate the utility of the analysis in a practical setup. It is established that the probability density function of the number of received packets per superframe follows a Poisson distribution. It is determined under which conditions the guaranteed time slot allocation mechanism of IEEE 802.15.4 is stable. It is shown that the mutual effect between throughput of the random access and the TDMA scheme for a fixed superframe length is critical to maximizing the overall throughput of the hybrid MAC. In high traffic load, the throughput of the random access mechanism dominates over TDMA due to the constrained use of TDMA in the standard. Furthermore, it is shown that the effect of imperfect channels and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters. Finally, it is argued that the traffic generation model established in this article may be used to design an activation timer mechanism in a modified version of the CSMA/CA algorithm that guarantees a stable network performance.

Distributed processing through ad hoc and sensor networks is having a major impact on scale and applications of computing. The creation of new cyber-physical services based on wireless sensor devices relies heavily on how well communication protocols can be adapted and optimized to meet quality constraints under limited energy resources. The IEEE 802.15.4 medium access control protocol for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by a parallel and distributed tuning of the medium access control parameters. Such a tuning is difficult, because simple and accurate models of the influence of these parameters on the probability of successful packet transmission, packet delay, and energy consumption are not available. Moreover, it is not clear how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run on resource-constrained devices. In this paper, a Markov chain is proposed to model these relations by simple expressions without giving up the accuracy. In contrast to previous work, the presence of limited number of retransmissions, acknowledgments, unsaturated traffic, packet size, and packet copying delay due to hardware limitations is accounted for. The model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while guaranteeing a given successful packet reception probability and delay constraints in the packet transmission. The algorithm does not require any modification of the IEEE 802.15.4 medium access control and can be easily implemented on network devices. The algorithm has been experimentally implemented and evaluated on a testbed with off-the-shelf wireless sensor devices. Experimental results show that the analysis is accurate, that the proposed algorithm satisfies reliability and delay constraints, and that the approach reduces the energy consumption of the network under both stationary and transient conditions. Specifically, even if the number of devices and traffic configuration change sharply, the proposed parallel and distributed algorithm allows the system to operate close to its optimal state by estimating the busy channel and channel access probabilities. Furthermore, results indicate that the protocol reacts promptly to errors in the estimation of the number of devices and in the traffic load that can appear due to device mobility. It is also shown that the effect of imperfect channel and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters.

An energy-efficient, reliable and timely data transmission is essential for Wireless Sensor Networks (WSNs) employed in scenarios where plant information must be available for control applications. To reach a maximum efficiency, cross-layer interaction is a major design paradigm to exploit the complex interaction among the layers of the protocol stack. This is challenging because latency, reliability, and energy are at odds, and resource-constrained nodes support only simple algorithms. In this paper, the novel protocol Breath is proposed for control applications. Breath is designed for WSNs where nodes attached to plants must transmit information via multihop routing to a sink. Breath ensures a desired packet delivery and delay probabilities while minimizing the energy consumption of the network. The protocol is based on randomized routing, medium access control, and duty-cycling jointly optimized for energy efficiency. The design approach relies on a constrained optimization problem, whereby the objective function is the energy consumption and the constraints are the packet reliability and delay. The challenging part is the modeling of the interactions among the layers by simple expressions of adequate accuracy, which are then used for the optimization by in-network processing. The optimal working point of the protocol is achieved by a simple algorithm, which adapts to traffic variations and channel conditions with negligible overhead. The protocol has been implemented and experimentally evaluated on a testbed with off-the-shelf wireless sensor nodes, and it has been compared with a standard IEEE 802.15.4 solution. Analytical and experimental results show that Breath is tunable and meets reliability and delay requirements. Breath exhibits a good distribution of the working load, thus ensuring a long lifetime of the network. Therefore, Breath is a good candidate for efficient, reliable, and timely data gathering for control applications.

The problem of maximizing a utility function while limiting the outage probability below an appropriate threshold is investigated. A coded-division multi access wireless network under mixed Nakagami-lognormal fading is considered. Solving such a utility maximization problem is difficult because the problem is non-convex and non-geometric with mixed integer and real decision variables and no explicit functions of the constraints are available. In this paper, three methods for the solution of the utility maximization problem are proposed. By the first method, a simple explicit outage approximation is used and the constraint that rates are integers is relaxed yielding a standard convex programming optimization that can be solved quickly but at the price of a reduced accuracy. The second method uses a more accurate outage approximation, which allows one solving the utility maximization problem by the Lagrange duality for non-convex problems and contraction mapping theory. The third method is a combination of the first and the second one. Numerical results show that the first method performs well for average values of the outage requirements, whereas the second one is always more accurate, but is also more computationally expensive. Finally, the third method gives same accuracy as the second one, but has a lower computational complexity only for a small number of transmitters.

IEEE 802.15.4 multi-hop wireless networks are an important communication infrastructure for many applications, including industrial control, home automation, and smart grids. Existing analysis of the IEEE 802.15.4 medium access control (MAC) protocol are often based on assumptions of homogeneous traffic and ideal carrier sensing, which are far from the reality when predicting performance for multi-hop networks. In this paper, a generalized analysis of the unslotted IEEE 802.15.4 MAC is presented. The model considers heterogeneous traffic and hidden terminals due to limited carrier sensing capabilities, and allows us to investigate jointly IEEE 802.15.4 MAC and routing algorithms. The analysis is validated via Monte Carlo simulations, which show that routing over multi-hop networks is significantly influenced by the IEEE 802.15.4 MAC performance. Routing decisions based on packet loss probability may lead to an unbalanced distribution of the traffic load across paths, thus motivating the need of a joint optimization of routing and MAC.

Control applications over wireless sensor networks (WSNs) require timely, reliable, and energy efficient communications. Cross-layer interaction is an essential design paradigm to exploit the complex interaction among the layers of the protocol stack and reach a maximum efficiency. Such a design approach is challenging because reliability and latency of delivered packets and energy are at odds, and resource constrained nodes support only simple algorithms. In this paper, the TREnD protocol is introduced for control applications over WSNs in industrial environments. It is a cross-layer protocol that embraces efficiently routing algorithm, MAC, data aggregation, duty cycling, and radio power control. The protocol parameters are adapted by an optimization problem, whose objective function is the network energy consumption, and the constraints are the reliability and latency of the packets. TREnD uses a simple algorithm that allows the network to meet the reliability and latency required by the control application while minimizing for energy consumption. TREnD is implemented on a test-bed and compared to some existing protocols. Experimental results show good performance in terms of reliability, latency, low duty cycle, and load balancing for both static and time-varying scenarios.

The IEEE 802.15.4 standard for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by tuning the medium access control parameters macMinBE, macMax-CSMABackoffs, and macMaxFrameRetries. Such a tuning is difficult, because simple and accurate models of the influence of these parameters on the probability of successful packet transmission, packet delay and energy consumption are not available. Moreover, it is not clear how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run on resource-constrained nodes. In this paper, an effective analytical model is used to derive an adaptive algorithm at the medium access control layer for minimizing the power consumption while guaranteeing reliability and delay constraints in the packet transmission. The algorithm does not require any modifications of the IEEE 802.15.4 standard and can be easily implemented on existing network nodes. Numerical results show that the analysis is accurate, that the proposed algorithm satisfies reliability and delay constraints, and ensures a longer lifetime of the network under both stationary and transient network conditions.

We present a novel approach for Medium Access Control (MAC) protocol design based on protocol engine. Current way of designing MAC protocols for a specific application is based on two steps: First the application specifications (such as network topology and packet generation rate), the requirements for energy consumption, delay and reliability, and the resource constraints from the underlying physical layer (such as energy consumption and data rate) are specified, and then the protocol that satisfies all these constraints is designed. Main drawback of this procedure is that we have to restart the design process for each possible application, which may be a waste of time and efforts. The goal of a MAC protocol engine is to provide a library of protocols together with their analysis such that for each new application the optimal protocol is chosen automatically among its library with optimal parameters. We illustrate the MAC engine idea by including an original analysis of IEEE 802.15.4 unslotted random access and Time Division Multiple Access (TDMA) protocols, and implementing these protocols in the software framework called SPINE, which runs on top of TinyOS and is designed for health care applications. Then we validate the analysis and demonstrate how the protocol engine chooses the optimal protocol under different application scenarios via an experimental implementation.

Ensuring a seamless connection when users are moving across radio cells is essential to guarantee a high communication quality. In this paper, performance of TCP during the handover in a Long Term Evolution (LTE) network is investigated. Specifically, mobile users with high bit rates TCP services are considered, and the impacts of the intra LTE handover over their perceived throughput are studied. Due to the mobility of the users across radio cells, the high bandwidth required, and possible network congestions, it is shown that the handover may cause sudden degradation of the quality of the communication if the process is not correctly controlled. To alleviate these problems, three solutions are proposed: fast path switch, handover prediction, and active queue management. The first two solutions avoids excessive delay in the packet delivery during the handover, whereas the second solution acts at the transport network with an active queue management. Simulation results, obtained by an extension of the ns-2 simulator, show that the proposed solutions present advantages, and that the handover prediction used with the active queue management increases TCP performance significantly.

We propose a comprehensive theoretical framework to evaluate reliability and energy consumption of distributed source coding (DSC) in wireless sensor networks (WSNs) applications. Energy efficiency and the amount of measurements that can be successfully decoded in tree-based WSNs employing DSC in the presence of different coding topologies and packet aggregation schemes (PA) are accurately characterized. The system model includes a realistic network architecture with multi-hop communication, automatic repeat request protocol (ARQ), packet losses due to channel impairments and collisions, and correlation properties of the sensed phenomena. Four DSC topologies and three alternatives of PA are considered. The analysis is carried out by evaluating the expressions of reliability of DSC in terms of probability of measurements that cannot be decoded (loss factor), and the efficiency in terms of average energy consumption of the network. Numerical results show that the best choice of DSC topology and packet aggregation depends highly on the network parameters and source characteristics. Therefore, the analysis developed in this paper can be used as an effective mean to optimize network operations.

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.

A generalized analysis of the IEEE 802.15.4 medium access control (MAC) protocol in terms of reliability, delay and energy consumption is presented. The IEEE 802.15.4 exponential backoff process is modeled through a Markov chain taking into account retry limits, acknowledgements, and unsaturated traffic. Simple and effective approximations of the reliability, delay and energy consumption under low traffic regime are proposed. It is demonstrated that the delay distribution of IEEE 802.15.4 depends mainly on MAC parameters and collision probability. In addition, the impact of MAC parameters on the performance metrics is analyzed. The analysis is more general and gives more accurate results than existing methods in the literature. Monte Carlo simulations confirm that the proposed approximations offer a satisfactory accuracy.

Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis.

Time-critical applications for wireless sensor networks (WSNs) are an important class of services supported by the standard IEEE 802.15.4. Control, actuation, and monitoring are all examples of applications where information must be delivered within some deadline. Understanding the delay in the packet delivery is fundamental to assess performance limitation for the standard. In this paper we analyze the guaranteed time slot (GTS) allocation mechanism used in IEEE 802.15.4 networks for time- critical applications. Specifically, we propose a Markov chain to model the stability, delay, and throughput of GTS allocation. We analyze the impact of the protocol parameters on these performance indexes. Monte Carlo simulations show that the theoretical analysis is quite accurate. Thus, our analysis can be used to design efficient GTS allocation for IEEE 802.15.4.

On-off keying (OOK) is an attractive modulation technique to reduce energy consumption of power-constrained wireless networks. The OOK detection threshold, however, must be carefully selected to minimize the bit error probability (BER). This is a challenging task to accomplish on resource-limited nodes or on networks with high-mobility. In this paper, an efficient algorithm to compute the optimal threshold is proposed. The system scenario considers nodes simultaneously transmitting over same frequencies in Rayleigh-log-normal or Rice-log-normal fading conditions. It is shown that by using the Stirling expansion for the BER, and a log-normal approximation, a quick contraction- mapping can be built to achieve the threshold numerically. The mapping is simple to implement and converges quickly. Numerical simulations verify the validity of the theoretical analysis, and show that the new algorithm performs quite well in scenarios of practical interest.

To achieve satisfactory overall performance, optimal rate allocation in a networked control system with highly limited communication resources is instrumental. In this paper, a rate allocation technique for state feedback control in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall cost is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved. It is shown that a non-uniform quantization is in general the best strategy for state feedback control over noisy channels. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.

A radio power control strategy to achieve maximum throughput for the up-link of CDMA wireless systems with variable spreading factor is investigated. The system model includes slow and fast fading, rake receiver, and multi-access interference caused by users with heterogeneous data sources. The quality of the communication is expressed in terms of outage probability, while the throughput is defined as the sum of the users' transmit rates. The outage probability is accounted for by resorting to a lognormal approximation. A mixed integer-real optimization problem P1, where the objective function is the throughput under outage probability constraints, is investigated. Problem P1is solved in two steps: firstly, we propose a modified problem P2to provide feasible solutions, and then the optimal solution is obtained with an efficient branch-and-bound search. Numerical results are presented and discussed to assess the validity of our approach.

A theoretical framework is proposed for accurate comparison of minimum energy coding in Coded Division Multiple Access (CDMA) Wireless Sensor Networks (WSNs). Energy consumption and reliability are analyzed for two coding schemes: Minimum Energy coding (ME), and Modified Minimum Energy coding (MME). A detailed model of consumed energy is described as function of the coding, radio transmit power, the characteristics of the transceivers, and the dynamics of the wireless channel. Since CDMA is strongly limited by multi-access interference, the system model includes all the relevant characteristics of wireless propagation. A distributed and asynchronous algorithm, which minimizes the total energy consumption by controlling the radio power, is developed. Numerical results are presented to validate the theoretical analysis and show under which conditions MME outperforms ME with respect to energy consumption and bit error rate. It is concluded that MME is more energy efficient than ME only for short codewords.

The problem of maximizing the sum of the transmit rates while limiting the outage probability below an appropriate threshold is investigated for networks where the nodes have limited processing capabilities. We focus on CDMA wireless network whose rates are characterized under mixed Rayleigh- lognormal fading. The outage probability is given implicitly by a complex function so that solving the optimization problem requires substantial computing. In this paper, we propose a novel explicit approximation of this function that allows solving the problem in an affordable manner. We propose two solutions of the maximization problem with the simplified outage probability constraint: one solves the problem using mixed integer-real programming. The other relaxes the constraints that rates be integers yielding a standard convex programming optimization that can be solved much faster. Numerical results show that our approaches perform well for average values of the outage requirements.

We present a novel approach for minimizing the energy consumption of medium access control (MAC) protocols developed for duty-cycled wireless sensor networks (WSN) for the unslotted IEEE 802.15.4 standard while guaranteeing delay and reliability constraints. The main challenge in this optimization is the random access associated with the existing IEEE 802.15.4 hardware and MAC specification that prevents controlling the exact transmission time of the packets. Data traffic, network topology, MAC, and the key parameters of duty cycles (sleep and wake time) determine the amount of random access, which in turn determines delay, reliability and energy consumption. We formulate and solve an optimization problem where the objective function is the total energy consumption in transmit, receive, listen and sleep states, subject to constraints of delay and reliability of the packet delivery and the decision variables are the sleep and wake time of the receivers. The optimal solution can be easily implemented on existing IEEE 802.15.4 hardware platforms, by storing light look-up tables in the receiver nodes. Numerical results show that the protocol outperforms significantly existing solutions.

The novel cross-layer protocol Breath for wireless sensor networks is designed, implemented, and experimentally evaluated. The Breath protocol is based on randomized routing, MAC and duty-cycling, which allow it to minimize the energy consumption of the network while ensuring a desired packet delivery end-to-end reliability and delay. The system model includes a set of source nodes that transmit packets via multi-hop communication to the destination. A constrained optimization problem, for which the objective function is the network energy consumption and the constraints are the packet latency and reliability, is posed and solved. It is shown that the communication layers can be jointly optimized for energy efficiency. The optimal working point of the network is achieved with a simple algorithm, which adapts to traffic variations with negligible overhead. The protocol was implemented on a test-bed with off-the-shelf wireless sensor nodes. It is compared with a standard IEEE 802.15.4 solution. Experimental results show that Breath meets the latency and reliability requirements, and that it exhibits a good distribution of the working load, thus ensuring a long lifetime of the network.

A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors' measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This bound decreases with the number of neighboring nodes. The estimation properties of the algorithm are illustrated via computer simulations, which are intended to compare our estimator performance with distributed schemes that were proposed previously in the literature. The results of the paper allow to trading-off communication constraints, computing efforts and estimation quality for a class of distributed filtering problems.

A comprehensive performance evaluation of a cross- layer solution to increase users' downlink data rates over HSDPA is provided. The solution consists of a proxy entity between a server and the radio network controller, and cross-layer signalling from the base station to the proxy. The performance of the solution is evaluated though a detailed ns-2 simulator environment, which includes all HSDPA features, as well as some existing TCP enhancing protocols widely adopted for internet traffic over wireless links. Numerical results show that the proxy significantly increases the users' throughput, while also improving the utilization of the radio resources.

We characterize the maximum throughput achievable for the up-link of a power-controlled WCDMA wireless system with variable spreading factor. Our system model includes multi access interference caused by users with heterogeneous data sources, and quality of service expressed in terms of outage probability. Inner loop and outer loop power control mechanisms are also explicitly taken into account. We express the system throughput as the sum of the users transmission rates, and propose a mixed integer optimization program where the objective function is the sum of the rates under outage probability constraints. Then, we solve the optimization problem proposing an efficient optimal approach based on two steps: firstly, a modified problem provides feasible solutions, and then the optimal solution is obtained with branch and bound criteria. Numerical results confirm the validity of our approach, and show how the throughput depends on the power control fluctuation, activity of the sources, and quality of service.

A new distributed estimation algorithm for tracking using a wireless sensor network is presented. We investigate how to track a time varying signal, noisily sensed by the nodes of the network. The algorithm is distributed, meaning that it does not require a central coordination among the nodes. Moreover, the proposed approach is scalable with respect to the network size, which means that its complexity does not grow with respect to the total number of nodes. The algorithm designed turns out to be composed by a cascade structure. Local constraints are determined to guarantee the global asymptotic stability of the estimation error. The algorithm can be applied e.g., for the position estimation, temporal synchronization, as well as tracking of signals. Performance is illustrated by simulations, where our filter is shown to behave better than other distributed schemes proposed in the literature.

An adaptive handover algorithm for wireless communication systems is addressed in this paper. Moving from the Generalized Extended Least Square handover algorithm proposed in N. Benvenuto et al. (2002), we model the handover mechanism as a hybrid system, and we include it in a dynamic optimization problem which is solved through the use of a trellis diagram. The objective function takes into account the parameters that influence the handover mechanism. The optimal solution of the program allows to evaluate performance of the handover in terms of the probability of handover and probability of outage.

A new control structure is proposed to improve user experience of wireless Internet. Information on radio bandwidth and queue length available in the radio network, close to the base station, is used in a proxy that resides between the Internet and the cellular system. The control algorithm in the proxy sets the window size according to event-triggered information on radio bandwidth changes and time-triggered information on the queue length at the wireless link. The mechanism is compared to TCP Reno in two simulation scenarios. The first scenario models a dedicated channel with stepwise changes in the bandwidth, while the second scenario models the High-speed Downlink Shared Channel recently introduced by 3GPP. The proposed mechanism significantly reduces the amount of buffer space needed in the radio network, and it also gives modest improvements to user response time and link utilization. Reduced buffering is particularly beneficial for third-party end-to-end real-time services such as voice, video, and online gaming.

Presents abstracts of forthcoming articles.

In this paper, a lognormal approximation is proposed for the sum of lognormal processes weighted by binary processes. The analytical approach moves from the method early proposed by Wilkinson for approximating first-order statistics of a sum of lognormal components, and extends to incorporate second-order statistics and the presence of both time-correlated random binary weights and cross-correlated lognormal components in moments' matching. Since the sum of weighted lognormal processes models the signal-to-interference-plus-noise ratio (SINR) of wireless systems, the method can be applied to evaluate in an effective and accurate way the outage occurrence rate and outage duration for different wireless systems of practical interest. In a frequency-reuse-based cellular system, the method is applied for various propagation scenarios, characterized by different shadowing correlation decay distances and correlations among shadowing components. A further case of relevant interest is related to power-controlled wideband wireless systems, where the random weights are binary random variables denoting the activity status of each interfering source. Finally, simulation results are used to confirm the validity of the analysis.

We present a system level design methodology for clustered wireless sensor networks based on a semi-random communication protocol called SERAN, a mathematical model that allows to optimize the protocol parameters, and a network initialization and maintenance procedure. SERAN is a two-layer (routing and MAC) protocol. At both layers, SERAN combines a randomized and a deterministic approach. While the randomized component provides robustness over unreliable channels, the deterministic component avoids an explosion of packet collisions and allows our protocol to scale with network size. The combined result is a high reliability and major energy savings when dense clusters are used. Our solution is based on a mathematical model that characterizes performance accurately without resorting to extensive simulations. Thanks to this model, the user needs only to specify the application requirements in terms of end-to-end packet delay and packet loss probability, select the intended hardware platform, and the protocol parameters are set automatically to satisfy latency requirements and optimize for energy consumption.

Abstract — We present a novel protocol architecture for ubiquitous networks. Our solution is based on a randomized routing, MAC and duty cycling protocols that allow for performance and reliability leveraging node density. We show how the three layers can be jointly optimized for energy efficiency and we present a completely distributed algorithm that allows for the network to reach the optimal working point and adapt to traffic variations with negligible overhead. Finally, we present a set of simulation results that support our mathematical model. I.

Abstract — Spatio-Temporal processing is a control technique to increase the quality of the received signals in wireless networks. Outage events have a strong influence not only on the performance of the physical layer, but also on routing, MAC, and application layer. In this paper, we propose an outage based performance analysis of collaborative STP for WSNs. After an accurate characterization of the wireless channel, we derive the outage statistics as function of the STP coefficients, and investigate the effects of STP on the probability, average duration and rate of the outage events. Furthermore, we show that a proper control policy of the STP coefficients can be derived according to the requirements from the applications and WSNs communication layers.

In this paper, we propose a theoretical setup for evaluation of energy efficiency of wireless sensor networks (WSNs) with distributed source coding (DSC) algorithms and packet aggregation (PA). We consider four topologies for DSC and three alternatives for PA, and the system model includes a realistic network architecture with multi-hop communication, automatic repeat request protocol (ARQ), and packet losses. The analysis is carried out in two steps. Firstly we derive the packet loss probability, and then evaluate the average number of packets transmitted throughout the network. This second performance index can then be mapped onto an energy efficiency indicator. The proposed model is specifically adopted for performance comparison of the different coding strategies and aggregation schemes in terms of energy efficiency. Numerical results show that packet overheads have a relevant influence on performance, while the ARQ protocol introduces negligible effects on the energy consumption. Furthermore, DSC topologies with master- slave approach and fragmentation of packets exhibit better performance.

An overview of some recent advances in distributed information processing for control over wireless sensor networks is presented in this paper. Firstly, a taxonomy of fundamental control and communication schemes for these systems is introduced. Next, specific research issues are proposed and discussed with three prominent examples on distributed source coding with packet aggregation, distributed cooperative diversity and distributed cooperative localization. In regard to these examples, it is argued about some open research problems and suggestions for further investigations on joint control and communication design for distributed processing and control over wireless sensor networks.

We derive a power control policy for a group of sensor nodes that are monitoring a real-time application sensitive to disconnections (outages) of the communication. Specifically, we suggest that the sensor nodes perform cooperative diversity while running a sleep discipline. After the description of a detailed model of the wireless links, we propose a power minimization algorithm with a constraint expressed in terms of outage probability. Suboptimal solutions are also discussed. Numerical examples are provided for various number of nodes, wireless scenarios and nodes activities. It is argued that nodes with reduced activity show better performance

SERAN is a two-layer (routing and MAC) protocol for wireless sensor networks in manufacturing plants. At both layers, SERAN combines a randomized and a deterministic approach. While the randomized component provides robustness over unreliable channels, the deterministic component avoids an explosion of packet collisions and allows our protocol to scale with network size. Our solution is based on a mathematical model that characterizes performance accurately without extensive simulations. SERAN is robust against node failures and clock drifts, supports data aggregation algorithms and is easily implementable in any of the existing hardware platforms. Although SERAN was designed for manufacturing plants applications, it can be used in any type of clustered topology. We consider a representative case study and we present simulation results to show SERAN efficiency

This paper proposes a log-normal approximation for the sum of on-off log-normal processes. An analytical approach is developed, that moves from the method early proposed by Wilkinson for approximating first order statistics of a sum of log-normal components, and extends to incorporate second order statistics and the presence of random weights in moments' matching. As a relevant application, the method is used to calculate in an effective and accurate way the parameters of the signal-to-(noise+interference) ratio (S I N R) in a power-controlled wideband wireless system, where the random weights are binary RVs and denote the activity status of each interfering source. A collection of numerical results is presented, where performance predicted by the analysis is compared to that obtained from simulation.