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This paper provides a theoretical framework for understanding the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first introduce a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results demonstrate that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of $\mathcal {O}\left ({\log _{2}\left ({MN}\right)}\right)$ , where $M$ and $N$ are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of $\mathcal {O}\left ({\log _{2}\left ({MN^{2}}\right)}\right)$ ; 3) either $M$ or the transmit power can be reduced inversely proportional to $N$ , while maintaining a given rate. Furthermore, we propose two low-complexity majorization-minimization (MM)-based algorithms to optimize the sum user rate and the minimum user rate, respectively, where closed-form solutions are obtained in each iteration. Finally, simulation results validate the accuracy of all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user. [ABSTRACT FROM AUTHOR]

Very recently, signed quadrature spatial modulation (sQSM) is developed as a competent technique that expands the spatial constellation diagram of QSM system by adding a bipolar dimension. Despite the enhanced spectral efficiency, the existing optimal maximum likelihood (ML) presents a serious computational challenge for large scale sQSM systems. Therefore, developing a reduced complexity detector for sQSM schemes is of significant importance to enable implementing and enjoying the inherent advantages of this promising system. Toward this end, a Tree Search (TS) optimal low complexity detector, called TSopt, for sQSM Multiple Input Multiple Output (MIMO) system is proposed and analyzed in this paper. The proposed detector expands the computationally complex ML detector for sQSM into a tree-structure representation. The idea of the suggested algorithm is to employ an efficient searching strategy that can expeditiously find the branch corresponding to the minimum error without tracing the entire nodes as in the ML case. It is reported that the proposed TSopt algorithm achieves the exact error performance as ML detector but with substantial reduction in computational complexity. Besides, complexity analysis in terms of the number of visited nodes of the TSopt algorithm is analyzed and a closed-form expression for the expected complexity at high SNR values is derived. Reported results disclose agreement between simulation and expected analytical complexity with substantial gains of around 60–80% in complexity reduction for different system parameters. [ABSTRACT FROM AUTHOR]

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated. [ABSTRACT FROM AUTHOR]

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector. [ABSTRACT FROM AUTHOR]

Achieving adequate precision in deep learning based communications often requires large network architectures, which results into unacceptable time delay and power consumption. This paper introduces the dynamic neural network (DyNN) into the design of wireless communications systems. DyNN allocates different samples with computation resources on demand by preforming dynamic inferences, thereby reducing the redundant computational cost and enhancing the network efficiency. We design a dynamic depth architecture that allows samples to adaptively skip layers with various dynamic strategies, from which we further develop a confidence criterion based dynamic improved DetNet (CD-IDetNet) and a policy network based dynamic improved DetNet (PD-IDetNet) for multiple-input multiple-output (MIMO) detection. Specifically, in CD-IDetNet, a confidence criterion is adopted to control samples exiting early, while in PD-IDetNet, policy networks are trained by reinforcement learning to selectively skip layers for varying samples. Simulation results demonstrate that CD-IDetNet and PD-IDetNet detectors can respectively reduce 17.4% and 31.1% computational costs while preserving the full accuracy of IDetNet. Desirable tradeoffs between accuracy and computational complexity can be further achieved by fine-tuning the hyper-parameters of CD-IDetNet and PD-IDetNet. Moreover, over-the-air (OTA) tests are conducted to validate the effectiveness of the proposed detectors in practical systems. [ABSTRACT FROM AUTHOR]

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multi-hop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labeled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel. [ABSTRACT FROM AUTHOR]

Exploration of white spaces has been recognized as a promising way to improve the utilization of wireless spectrums. Especially, indoor white space exploration has been shown to be much more challenging than that in outdoor environment. Most of existing works on indoor white space exploration infer availabilities of TV channels based on the correlations among the channels and locations, which is learned from the training data measured beforehand. However, the process of training data collection normally requires considerable time and devices, as well as human power. In this paper, we perform a measurement of indoor white spaces and study their characteristics. Based on the in-depth understanding of the characteristics, we propose a Training-free Indoor white space exploration MEchanism (TIME). In TIME, we design an algorithm for channel state inference based on Bayesian compressive sensing, as well as an incremental method for deployment of spectrum detectors. Furthermore, we present an algorithm to determine a proper number of spectrum detectors in need. Extensive real-world experiments are conducted to evaluate TIME’s performance. The evaluation results demonstrate that TIME achieves competitive performances against the state-of-the-art training-based mechanisms. [ABSTRACT FROM PUBLISHER]

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI. [ABSTRACT FROM AUTHOR]

For a class of sensor networks, the task is to monitor an underlying physical phenomenon over space and time through an imperfect observation process. The sensors can communicate back to a central data collector over a noisy channel. The key parameters in such a setting are the fidelity (or distortion) at which the underlying physical phenomenon can be estimated by the data collector, and the cost of operating the sensor network. This is a network joint source-channel communication problem, involving both compression and communication. It is well known that these two tasks may not be addressed separately without sacrificing optimality, and the optimal performance is generally unknown. This paper presents a lower bound on the best achievable end-to-end distortion as a function of the number of sensors, their total transmit power, the number of degrees of freedom of the underlying source process, and the spatio-temporal communication bandwidth. Particular coding schemes are studied, and it is shown that in some cases, the lower bound is tight in a scaling-law sense. By contrast, it is shown that the standard practice of separating source from channel coding may incur an exponential penalty in terms of communication resources, as a function of the number of sensors. Hence, such code designs effectively prevent scalability. Finally, it is outlined how the results extend to cases involving missing synchronization and channel fading. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal sensing scheduling problem for an energy harvesting sensor. The objective is to strategically select the sensing time such that the long-term time-average sensing performance is optimized. In the sensing system, it is assumed that the sensing performance depends on the time durations between two consecutive sensing epochs. Example applications include reconstructing a wide-sense stationary random process by using discrete-time samples collected by a sensor. We consider both scenarios where the battery size is infinite and finite, assuming the energy harvesting process is a Poisson random process. We first study the infinite battery case and identify a performance limit on the long-term time average sensing performance of the system. Motivated by the structure of the performance limit, we propose a best-effort uniform sensing policy, and prove that it achieves the limit asymptotically, thus it is optimal. We then study the finite battery case, and propose an energy-aware adaptive sensing scheduling policy. The policy dynamically chooses the next sensing epoch based on the battery level at the current sensing epoch. We show that as the battery size increases, the sensing performance under the adaptive sensing policy asymptotically converges to the limit achievable by the system with infinite battery, thus it is asymptotically optimal. The convergence rate is also analytically characterized. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E2PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training. Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals. [ABSTRACT FROM AUTHOR]

In this paper, a novel terahertz (THz) spectroscopy technique and a new graphene-based sensor is proposed. The proposed sensor consists of a graphene-based metasurface (MS) that operates in reflection mode over a broad range of frequency band (0.2 – 6 THz) and can detect relative permittivity of up to 4 with a resolution of 0.1 and a thickness ranging from 5 μm to 600 μm with a resolution of 0.5 μm. To the best of author’s knowledge, such a THz sensor with such capabilities has not been reported yet. Additionally, an equivalent circuit of the novel unit cell is derived and compared with two conventional grooved structures to showcase the superiority of the proposed unit cell. The proposed spectroscopy technique utilizes some unique spectral features of a broadband reflection wave including Accumulated Spectral power (ASP) and Averaged Group Delay (AGD), which are independent to resonance frequencies and can operate over a broad range of spectrum. ASP and AGD can be combined to analyse the magnitude and phase of the reflection diagram as a coherent technique for sensing purposes. This enables the capability to distinguish between different analytes with high precision which, to the best of author’s knowledge, has been accomplished for the first time. [ABSTRACT FROM AUTHOR]

In this paper, we address a multiple-input multiple-output (MIMO) communication system based on generalized spatial modulation (GSM) and dual-polarized (DP) antennas in sub-TeraHertz (sub-THz) bands to improve the Spectral Efficiency (SE) while reducing the spatial correlation effect and the space occupancy. Moreover, the joint Maximum Likelihood (ML) detector and a Modified Ordered Block Minimum Mean-Squared Error (MOB-MMSE) detector algorithm are proposed to detect jointly the complex symbols and the virtual bits conveyed by the activated polarization and the transmit antenna combination index. MOB-MMSE can achieve near-ML performance with low complexity. The performance of DP-GSM system over correlated Rayleigh/Rician fading and indoor sub-THz channels is studied. Provided results show that DP-GSM system is robust to the spatial correlation effects under Rician fading channel. In addition, an average bit-error probability (ABEP) upper bounding is derived and analyzed for the DP-GSM system over correlated Rayleigh/Rician fading channels. Since the sub-THz band suffers from many technological limitations and severe RF-impairments such as low transmit output power and important phase noise (PN), the DP-GSM system is studied by considering the sub-THz impairments. Moreover, the Dual Polarized Spatial Multiplexing (DP-SMX) and the Uni-Polarized GSM (UP-GSM) are compared with the DP-GSM system. The results reveal that DP-GSM with low order modulation schemes as QPSK outperforms the UP-GSM. Besides, DP-GSM and DP-SMX provide a good performance up to medium PN, which is interesting for sub-THz bands, but DP-GSM can reduce the transceiver cost by using a lower number of RF chains when the used transmitter architecture is based on RF-switching. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mission-critical remote state estimation system with asynchronous massive access of the IoT sensors. We focus on remote state estimation stability of the system in the presence of asynchronous access of the sensors. Exploiting the sparsity in the observation matrix induced by the asynchronous access, we propose a low complexity 2-D message passing state estimation algorithm, where the cyclic loops in the 2-D factor graphs are removed based on the Gaussian-elimination-based quasi-diagonalization of the oversampled aggregated channel matrix of the IoT sensors. As a result, the proposed state estimation scheme is of low complexity and can achieve exact MAP estimation. Using Lyapunov drift analysis, we derive closed-form necessary and sufficient conditions for stability of the mission-critical remote state estimation system. We show that our proposed scheme can achieve significant performance gain over various state-of-the-art baselines for the large-scale system under asynchronous massive access. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) has been widely used in communication systems operating in the millimeter wave (mmWave) band to combat frequency-selective fading and achieve multi-Gbps transmissions, such as the IEEE 802.15.3c and the IEEE 802.11ad. For mmWave systems with ultra high sampling rate requirements, the use of low-resolution analog-to-digital converters (ADCs) (i.e., 1–3 bits) ensures an acceptable level of power consumption and system costs. However, orthogonality among subchannels in the OFDM system cannot be maintained because of the severe nonlinearity caused by low-resolution ADC, which renders the design of data detector challenging. In this paper, we develop an efficient algorithm for optimal data detection in the mmWave OFDM system with low-resolution ADCs. The analytical performance of the proposed detector is derived and verified to achieve the fundamental limit of the Bayesian optimal design. On the basis of the derived analytical expression, we further propose a power allocation (PA) scheme that seeks to minimize the average symbol error rate. In addition to the optimal data detector, we also develop a feasible channel estimation method, which can provide high-quality channel state information without significant pilot overhead. Simulation results confirm the accuracy of our analysis and illustrate that the performance of the proposed detector in conjunction with the proposed PA scheme is close to the optimal performance of the OFDM system with infinite-resolution ADC. [ABSTRACT FROM PUBLISHER]

Precise time synchronization of Phasor Measurement Units (PMUs) is critical for monitoring and control of smart grids. Thus, time synchronization attacks (TSAs) against PMUs pose a severe threat to smart grid security. In this paper we present an approach for detecting TSAs based on the interaction between the time synchronization system and the power system. We develop a phasor measurement model and use it to derive an accurate closed form expression for the correlation between the frequency adjustments made by the PMU clock and the resulting change in the measured phase angle, without an attack. We then propose one model-based and three data-driven TSA detectors that exploit the change in correlation due to a TSA. Using extensive simulations, we evaluate the proposed detectors under different strategies for implementing TSAs, and show that the proposed detectors are superior to state-of-the-art clock frequency anomaly detection, especially for unstable clocks. [ABSTRACT FROM AUTHOR]

This paper presents analysis of performance measurements from a MIMO-OFDM IEEE 802.11n hardware implementation at 5.2 GHz using four transmitters and four receivers. Two spatial multiplexing systems are compared; one which uses a zero-forcing (ZF) detector and the other a list sphere detector (LSD). We show that the measured results do not align with standard prediction based on simulation assuming uncorrelated receiver noise. We show that the discrepancy can be explained by the inclusion of transmitter noise into the channel model. This effect is not included in existing MIMO-OFDM channel models. The measured results from our hardware implementation show successful packet transmission at 600 Mb/s with 15 bits/s/Hz spectral efficiency at 73% coverage for ZF and 84% coverage for LSD with an average receiver signal to noise ratio (SNR) of 26 dB. [ABSTRACT FROM AUTHOR]

The conventional multiuser detector (MUD) based on the a posteriori probability (APP) algorithm has an exponential computational complexity in terms of the number of users. In this paper, we propose a low-complexity iterative multiuser receiver for synchronous turbo-coded code-division multiple-access (CDMA) systems. The proposed receiver is based on the Chase decoding algorithm that was previously used to decode turbo product codes. Simulation results show that the proposed receiver can significantly reduce the computational complexity with slight performance degradation compared with the APP MUD over highly correlated channels. Moreover, in this paper, we develop a numerical approach to analyze the convergence behavior of iteratively decoded CDMA channels based on density evolution technique. Analytical results are presented and shown to provide a reasonable match with what is observed in simulation. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are capable of collecting an enormous amount of data. Often, the ultimate objective is to estimate a parameter or function from these data, and such estimators are typically the solution of an optimization problem (e.g., maximum likelihood, minimum mean-squared error, or maximum a posteriori). This paper investigates a general class of distributed optimization algorithms for "in-network" data processing, aimed at reducing the amount of energy and bandwidth used for communication. Our intuition tells us that processing the data in-network should, in general, require less energy than transmitting all of the data to a fusion center. In this paper, we address the questions: When, in fact, does in-network processing use less energy, and how much energy is saved? The proposed distributed algorithms are based on incremental optimization methods. A parameter estimate is circulated through the network, and along the way each node makes a small gradient descent-like adjustment to the estimate based only on its local data. Applying results from the theory of incremental subgradient optimization, we find that the distributed algorithms converge to an approximate solution for a broad class of problems. We extend these results to the case where the optimization variable is quantized before being transmitted to the next node and find that quantization does not affect the rate of convergence. Bounds on the number of incremental steps required for a certain level of accuracy provide insight into the tradeoff between estimation performance and communication overhead. Our main conclusion is that as the number of sensors in the network grows, in-network processing will always use less energy than a centralized algorithm, while maintaining a desired level of accuracy. [ABSTRACT FROM AUTHOR]

Consider a decentralized estimation problem whereby an ad hoc network of K distributed sensors wish to cooperate to estimate an unknown parameter over a bounded interval [-U, U]. Each sensor collects one noise-corrupted sample, performs a local data quantization according to a fixed (but possibly probabilistic) rule, and transmits the resulting discrete message to its neighbors. These discrete messages are then percolated in the network and used by each sensor to form its own minimum mean squared error (MMSE) estimate of the unknown parameter according to a fixed fusion rule. In this paper, we propose a simple probabilistic local quantization rule: each sensor quantizes its observation to the first most significant bit (MSB) with probability 1/2, the second MSB with probability 1/4, and so on. Assuming the noises are uncorrelated and identically distributed across sensors and are bounded to [-U, U], we show that this local quantization strategy together with a fusion rule can guarantee a MSE of 4U²/K, and that the average length of local messages is bounded (no more than 2.5 bits). Compared with the worst case Cramer-Rao lower bound of U²/K (even for the centralized counterpart), this is within a factor of at most 4 to the minimum achievable MSE. Moreover, the proposed scheme is isotropic and universal in the sense that the local quantization rules and the final fusion rules are independent of sensor index, noise distribution, network size, or topology. In fact, the proposed scheme allows sensors in the network to operate identically and autonomously even when the network undergos changes in size or topology. [ABSTRACT FROM AUTHOR]

In this paper, we study a binary decentralized detection problem in which a set of sensor nodes provides partial information about the state of nature to a fusion center. Sensor nodes have access to conditionally independent and identically distributed observations, given the state of nature, and transmit their data over a wireless channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature. Specifically, we extend existing asymptotic results about large sensor networks to the case where the network is subject to a joint power constraint, and where the communication channel from each sensor node to the fusion center is corrupted by additive noise. Large deviation theory is used to show that having identical sensor nodes, i.e., each node using the same transmission scheme, is asymptotically optimal. Furthermore, a performance metric by which sensor node candidates can be compared is established. We supplement the theory with examples to illustrate how the results derived in this paper apply to the design of practical sensing systems. [ABSTRACT FROM AUTHOR]

Editorial. Introduces a series of articles which deals with multiuser detection (MUD) techniques. Linear detectors and the mutual effect of code orthogonality and adaptive implementation; Aspects of MUD for code division multiple access systems; Receiver synchronization in multiple-access systems.

Two-dimensional magnetic recording promises to increase areal density through the joint detection of multiple tracks of interest. This paper concerns the problem of joint detection of multiple tracks that are written asynchronously, meaning that neither the bit boundaries (phase) nor the bit rate (frequency) are aligned between neighboring tracks. We propose the rotating-target algorithm for jointly detecting multiple asynchronous tracks from one or more readback waveforms. The proposed approach is based on the joint Viterbi algorithm and a time-varying target that results when the asynchrony of the tracks is absorbed into the underlying target. Timing estimation for the tracks being detected is embedded inside the joint Viterbi detector using per-survivor processing. Performance results show that the proposed algorithm closely matches the performance of a fictitious system in which neighboring tracks are synchronous, and further that it significantly outperforms a previously reported detector that separately detects the two tracks. [ABSTRACT FROM PUBLISHER]

Shingled bit-patterned media (BPM) recording is a promising candidate to improve the magnetic recording density above 1 Tb/in2 while keeping the conventional writer design. However, shingled magnetic recording (SMR) leads to a narrower track pitch, and thus increased inter-track interference motivating the investigation of array readers and 2-D sectors. In this paper, we investigate SMR on BPM with 2-D sectors at the channel density of 3.71 Tb/in2. Use of 2-D sectors allows the possibility of using either hard or soft information from already detected tracks in that 2-D sector to improve the detection performance on the current track. An important question in this context is whether all tracks in a 2-D sector should be of the same nominal width or if there is a potential benefit to using different widths for adjacent tracks. Based on our investigation, we show that alternating track widths [i.e., one less-trimmed (fat) track followed by one more-trimmed (narrow) track] are beneficial to a bit error rate (BER) perspective. In particular, we propose and investigate the use of a bidirectional decision feedback modified Viterbi (BD-DFMV) detector to improve the BER performance. Our simulations indicate that for a hypothetical 2-D sector with 13 tracks and 5% media noise and at the target BER of 10−2, using alternating track widths and readback with a single reader, the BD-DFMV algorithm can provide about 1.6 dB signal-to-noise ratio (SNR) gain, compared with using the same detection algorithm but on a 2-D sector with uniform track widths. When using a reader array instead of a single reader, this SNR gain improves to about 2.0 dB. The alternating track width structure also appears to provide more robustness to track mis-registration compared with the 2-D uniform track width case. [ABSTRACT FROM PUBLISHER]

Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPS-denied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high-frequency radio signals. This has led to a plethora of competing solutions targeted toward a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles such as walls, floors, and people that heavily impact the performance of competing approaches are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel censoring policy for energy-efficient transmissions in energy-harvesting sensors. The problem is formulated as an infinite-horizon Markov Decision Process (MDP). The objective to be optimized is the expected sum of the importance (utility) of all transmitted messages. Assuming that such importance can be evaluated at the transmitting node, we show that, under certain conditions on the battery model, the optimal censoring policy is a threshold function on the importance value. Specifically, messages are transmitted only if their importance is above a threshold whose value depends on the battery level. Exploiting this property, we propose a model-based stochastic scheme that approximates the optimal solution, with less computational complexity and faster convergence speed than a conventional Q-learning algorithm. Numerical experiments in single-hop and multi-hop networks confirm the analytical advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper we derive the optimal detector for multilevel cell (MLC) flash memory channels with intercell interference (ICI). We start with the MLC channel model proposed by Dong et al. and just slightly alter the model to guarantee mathematical tractability of the optimal detectors (maximum likelihood and maximum a-posteriori sequence and symbol detectors). The optimal detector is obtained by computing branch metrics using Fourier transforms of analytically computable characteristic functions (corresponding to likelihood functions). We derive the detectors for both simple one-dimensional (1D) channel models and more realistic page-orientated two-dimensional (2D) channel models. Simulation results show that the hard-output bit error rate (BER) performance matches some previously known detectors, but that the soft-output detector outperforms previously known detectors by 0.35 dB. [ABSTRACT FROM PUBLISHER]

Efficient design of cognitive radios (CRs) calls for secondary users implementing adaptive resource allocation schemes that exploit knowledge of the channel state information (CSI), while at the same time limiting interference to the primary system. This paper introduces stochastic resource allocation algorithms for both interweave (also known as overlay) and underlay cognitive radio paradigms. The algorithms are designed to maximize the weighted sum-rate of orthogonally transmitting secondary users under average-power and probabilistic interference constraints. The latter are formulated either as short- or as long-term constraints, and guarantee that the probability of secondary transmissions interfering with primary receivers stays below a certain pre-specified level. When the resultant optimization problem is non-convex, it exhibits zero-duality gap and thus, due to a favorable structure in the dual domain, it can be solved efficiently. The optimal schemes leverage CSI of the primary and secondary networks, as well as the Lagrange multipliers associated with the constraints. Analysis and simulated tests confirm the merits of the novel algorithms in: i) accommodating time-varying settings through stochastic approximation iterations; and ii) coping with imperfect CSI. [ABSTRACT FROM AUTHOR]

In this paper we study opportunistic transmission strategies for cognitive radios (CR) in which causal noisy observation from a primary user (PU) state is available. The PU is assumed to be operating in a slotted manner, according to a two-state Markov model. The objective is to maximize utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by CR, subject to interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, below a certain level. We introduce an a-posteriori LLR-based cognitive transmission strategy and show that this strategy is optimum in the sense of maximizing UR given a certain maximum allowed IR. Two methods for calculating threshold for this strategy in practical situations are presented. One of them performs well in higher SNRs but might have too large IR at low SNRs and low PU activity levels, and the other is proven to never violate the allowed IR at the price of a reduced UR. In addition, an upper-bound for the UR of any CR strategy operating in the presence of Markovian PU is presented. Simulation results have shown a more than 116% improvement in UR at SNR of -3dB and IR level of 10% with PU state estimation. Thus, this opportunistic CR mechanism possesses a high potential in practical scenarios in which there exists no information about true states of PU. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various research papers within the issue on topics including distributed planning strategies to ensure network connectivity, Robotic Sensor Networks and wireless mobile networks.

Failure detection (FD) is an important issue for supporting dependability in distributed healthcare systems to guarantee continuous, safe, secure, and dependable operation, and often is an important performance bottleneck in the event of node failure. FD can be used to manage the health status of communication for delivering telemedicine services, and then to help distributed healthcare system reduce fatal accident rate and increase the reliability and safety of systems. Ensuring acceptable quality of service (QoS) is made difficult by the relative unpredictability of the network environment. In this paper, first, we compare QoS metrics of several adaptive FDs, discuss their properties and their relation, and then propose one optimization over the existing methods, called tuning adaptive margin failure detector (TAM FD), which significantly improves QoS, especially in the aggressive range and when the network is unstable. Second, we address the problem of most adaptive schemes, namely their need for a large window of samples. So we also analyze the impact of memory size on the performance of FDs, and then prove that the presented scheme is designed to use a fixed and very limited amount of memory for the distributed system. Our experimental results over several kinds of networks (Cluster, WiFi, LAN, Intercontinental WAN) show that the properties of the existing adaptive failure detectors, and demonstrate that the optimization is reasonable and acceptable. Furthermore, the extensive experimental results show what is the effect of memory size on the overall QoS of each adaptive failure detector. For our TAM FD, the effect of window size on their QoS is very small and can be negligible. [ABSTRACT FROM AUTHOR]

This paper investigates the opportunities and challenges in the use of dynamic radio transmit power control for prolonging the lifetime of body-wearable sensor devices used in continuous health monitoring. We first present extensive empirical evidence that the wireless link quality can change rapidly in body area networks, and a fixed transmit power results in either waster energy (when the link is good) or low reliability (when the link is bad). We quantify the potential gains of dynamic power control in body-worn devices by benchmarking off-line the energy savings achievable for a given level of reliability. We then propose a class of schemes feasible for practical implementation that adapt transmit power in real-time based on feedback information from the receiver. We profile their performance against the off-line benchmark, and provide guidelines on how the parameters can be tuned to achieve the desired trade-off between energy savings and reliability within the chosen operating environment. Finally, we implement and profile our scheme on a MicaZ mote based platform, and also report preliminary results the ultra-low-power integrated healthcare monitoring platform we are developing at Toumaz Technonology [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Markov-chain Monte-Carlo methods have been demonstrated to offer an attractive alternative to the design of approximate (near optimal) maximum a-posteriori (MAP) detectors for synchronous direct-sequence code-division multiple access (DS-CDMA) and multi-input, multi-output (MIMO) multiple antenna applications. Central to evaluating these method is understanding their convergence properties. In other works, this has been established via simulation, and the underlying theoretical basis has been identified. The contribution of this paper is to extend the theoretical understanding by rigorously establishing both convergence and convergence rate results for a wide class of Metropolis-Hastings methods. [ABSTRACT FROM AUTHOR]

This paper considers ultra-wideband (UWB)-based sensor networks and studies the following problem: given a set of source sensor nodes in the network each generating a certain data rate, is it possible to relay all these rates successfully to the base station? We will show that such problem is intrinsic cross-layer, and subsequently we formulate an optimization problem, with joint consideration of link-layer scheduling, power control, and network-layer routing. For large-sized networks, we propose an efficient heuristic algorithm by partitioning the given network into a core centered around the base station and a boundary edge. For the network core, we formulate a nonlinear programming problem, which can be solved by branch-and-bound approach. For data generated at network edge, we propose an algorithm to connect it to the network core. We use simulation results to demonstrate the efficacy of the proposed solution procedure, as well as the importance of cross-layer considerations. [ABSTRACT FROM AUTHOR]

The recent Federal Communications Commission regulations for ultra-wideband (UWB) transmission systems have sparked a surge of research interests in the UWB technology. One of the important application areas of UWB is wireless sensor networks. The proper operations of many UWB sensor networks rely on the knowledge of physical sensor locations. However, most existing localization algorithms developed for sensor networks are vulnerable to attacks in hostile environments. As a result, attackers can easily subvert the normal functionalities of location-dependent sensor networks by exploiting the weakness of localization algorithms. In this paper, we first analyze the security of existing localization techniques. We then develop a mobility-assisted secure localization scheme for UWB sensor networks. In addition, we propose a location-based scheme to enable secure authentication in UWB sensor networks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have received a lot of attention recently due to its wide applications. An accurate and synchronized clock time is crucial in many sensor network applications. Several clock synchronization schemes have been proposed for wireless sensor networks recently to address the resource constraints in such networks. However, most of these techniques assume benign environments, but cannot survive malicious attacks in hostile environments, especially when there are compromised nodes. As an exception, a recent work attempts to detect malicious attacks against clock synchronization, and aborts when an attack is detected. Though this approach can prevent incorrect clock synchronization due to attacks, it will lead to denial of clock synchronization in such situations. This paper adopts a model where all the sensor nodes synchronize their clocks to a common source, which is assumed to be well synchronized to the external clock. This paper seeks techniques to provide redundant ways for each node to synchronize its clock with the common source, so that it can tolerate partially missing or false synchronization information provided by compromised nodes. Two types of techniques are developed using this general method: level-based clock synchronization and diffusion-based clock synchronization. Targeted at static sensor networks, the level-based clock synchronization constructs a level hierarchy initially, and uses (or reuses) this level hierarchy for multiple rounds of clock synchronization. The diffusion-based clock synchronization attempts to synchronize all the clocks without relying on any structure assumptions and, thus, can be used for dynamic sensor networks. This paper further investigates how to use multiple clock sources for both approaches to increase the resilience against compromise of source nodes. The analysis in this paper indicates that both level-based and diffusion-based approaches can tolerate up to s colluding malicious source nodes and t colluding malicious nudes among the neighbors of each normal node, where s and t are two system parameters. This paper also presents the results of simulation studies performed to evaluate the proposed techniques. These results demonstrate that the level-based approach has less overhead and higher precision, but less coverage, than the diffusion-based approach. [ABSTRACT FROM AUTHOR]

In this paper, we present new analytical, simulated, and experimental results on the performance of relative location estimation in multihop wireless sensor networks. With relative location, node locations are estimated based on the collection of peer-to-peer ranges between nodes and their neighbors using a priori knowledge of the location of a small subset of nodes, called reference nodes. This paper establishes that when applying relative location to multihop networks the resulting location accuracy has a fundamental upper bound that is determined by such system parameters as the number of hops and the number of links to the reference nodes. This is in contrast to the case of single-hop or fully connected systems where increasing the node density results in continuously increasing location accuracy. More specifically, in multihop networks for a fixed number of hops, as sensor nodes are added to the network the overall location accuracy improves converging toward a fixed asymptotic value that is determined by the total number of links to the reference nodes, whereas for a fixed number of links to the reference nodes, the location accuracy of a node decreases the greater the number of hops from the reference nodes. Analytical expressions are derived from one-dimensional networks for these fundamental relationships that are also validated in two-dimensional and three-dimensional networks with simulation and UWB measurement results. [ABSTRACT FROM AUTHOR]

In this paper, we study how to reduce energy consumption in large-scale sensor networks, which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs--typically related to proximity--and, perhaps surprisingly, it is independent of the distribution of data sources. Next, we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes, and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8%-28% energy savings depending on the compression ratio. [ABSTRACT FROM AUTHOR]

Sensor networks have emerged as a fundamentally new tool for monitoring spatial phenomena. This paper describes a theory and methodology for estimating inhomogeneous, two-dimensional fields using wireless sensor networks. Inhomogeneous fields are composed of two or more homogeneous (smoothly varying) regions separated by boundaries. The boundaries, which correspond to abrupt spatial changes in the field, are nonparametric one-dimensional curves. The sensors make noisy measurements of the field, and the goal is to obtain an accurate estimate of the field at some desired destination (typically remote from the sensor network). The presence of boundaries makes this problem especially challenging. There are two key questions: 1) Given n sensors, how accurately can the field be estimated?; 2) How much energy will be consumed by the communications required to obtain an accurate estimate at the destination? Theoretical upper and lower bounds on the estimation error and energy consumption are given. A practical strategy for estimation and communication is presented. The strategy, based on a hierarchical data-handling and communication architecture, provides a near-optimal balance of accuracy and energy consumption. [ABSTRACT FROM AUTHOR]

Efficient resource planning in energy harvesting wireless sensor networks (WSNs) is of immense practical significance. However, it is encumbered by the sheer diversity of parameters that affect the performance of such WSNs. In this paper, we propose a unified framework that can be leveraged to efficiently design and deploy such large-scale networks. To this end, we propose an intuitive utility function to quantify the Quality of Monitoring (QoM). Based on this QoM, we formulate a long-term time-averaged joint resource allocation problem, whose optimal solution serves as a metric to compare different deployment scenarios. This resource allocation problem is subject to energy and capacity constraints inherent to WSNs. The capacity constraints, though very intuitive, require us to enumerate all the maximal independent sets in the network—a well known NP-hard problem. Therefore, for computational tractability, we replace the maximal independent set constraints with clique constraints. We also prove the sufficiency of the clique constraints, and present an algorithm that obtains an $\epsilon$-optimal solution to the original problem. Finally, we present numerical evaluations to validate the correctness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs’ locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the expressions of the distribution metrics of the aggregate received power at a typical SN. To design the charging radius for the optimal AD-WPT operation, we exploit the tradeoff between the power intensity of the energy beams and the number of SNs to be charged. Depending on different SN task requirements, the optimal AD-WPT can maximize the average received power or the active probability of the SNs, respectively. It is shown that both the maximum average received power and the maximum sensor active probability increase with the increased deployment density and transmit power of the PBs, and decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared with the traditional omnidirectional WPT. [ABSTRACT FROM AUTHOR]

This paper considers the impact of external noise sources, including interfering transmitters, on a diffusive molecular communication system, where the impact is measured as the number of noise molecules expected to be observed at a passive receiver. A unifying model for noise, multiuser interference, and intersymbol interference is presented, where, under certain circumstances, interference can be approximated as a noise source that is emitting continuously. The model includes the presence of advection and molecule degradation. The time-varying and asymptotic impact is derived for a series of special cases, some of which facilitate closed-form solutions. Simulation results show the accuracy of the expressions derived for the impact of a continuously-emitting noise source, and show how approximating old intersymbol interference as a noise source can simplify the calculation of the expected bit error probability of a weighted sum detector. [ABSTRACT FROM PUBLISHER]

Protocol feature words are byte subsequences within traffic payload that can distinguish application protocols, and they form the building blocks of many constructions of deep packet analysis rules in network management, measurement, and security systems. However, how to systematically and efficiently extract protocol feature words from network traffic remains a challenging issue. Existing approaches like those based on $n$-gram or Common String (CS), which simply breaks payload into equal-length pieces or attempts to find a frequent itemset, are ineffective in capturing the hidden statistical structure of the payload content. In this paper, we propose ProWord, an unsupervised approach that extracts protocol feature words from traffic traces. ProWord builds on two nontrivial algorithms. First, we propose an unsupervised segmentation algorithm based on the modified Voting Experts algorithm, such that we break payload into candidate words according to entropy information and provide more accurate segmentation than existing $n$-gram and CS approaches. Second, we propose a ranking algorithm that incorporates different types of well-known feature word retrieval heuristics, such that we can build an ordered structure on the candidate words and select the highest ranked ones as protocol feature words. We compare ProWord and existing prior approaches via evaluation on real-world traffic traces. We show that ProWord captures true protocol feature words more accurately and performs significantly faster. [ABSTRACT FROM AUTHOR]

In this paper, we study the stability property of a cognitive radio system comprised of a set of source-destination pairs having different priorities. In particular, we focus attention on the effect of imperfect sensing on the stability region of the system, which has been overlooked in most of related previous work. The adopted cognitive access protocol allows the secondary user not only to exploit the idle slots of the primary user but also to transmit along with the primary user with some probability. This is aimed at achieving the full utilization of the shared channel with capture, i.e., a transmission can be correctly decoded at the destination, even in the presence of other transmissions, if the received signal-to-interference-plus-noise ratio (SINR) exceeds a certain threshold for successful decoding. The abolition of strong primacy, however, requires the secondary user to properly regulate its multi-access probability in order not to impede the primary user's stability guarantee. To this end, the maximum stability region of the system is characterized which describes the theoretical limit on rates that can be pushed into the system while maintaining the queues stable. Interestingly, we found that even with non-zero sensing error rates, there exists a condition for which we can achieve the identical stability region that is achieved with perfect sensing. This is when the destinations enjoy fairly strong capture, and if then sensing errors do not affect the stability region for the queueing system. For the case when the specified condition does not hold, we precisely quantify the loss due to the imperfect sensing in terms of the size of the stability region. Finally, we study the problem of controlling the operating point of the sensing device over its receiver operating characteristic (ROC) and summarize some key aspects observed in the control. [ABSTRACT FROM AUTHOR]

In wireless body area network (BAN), node authentication is essential for trustworthy and reliable gathering of patient's critical health information. Traditional authentication solutions depend on prior trust among nodes whose establishment would require either key pre-distribution or non-intuitive participation by inexperienced users. Most existing non-cryptographic authentication schemes require advanced hardware or significant modifications to the system software, which are impractical for BANs. In this paper, for the first time, we propose a lightweight body area network authentication scheme BANA. Different from previous work, BANA does not depend on prior-trust among nodes and can be efficiently realized on commercial off-the-shelf low-end sensors. We achieve this by exploiting a unique physical layer characteristic naturally arising from the multi-path environment surrounding a BAN, i.e., the distinct received signal strength (RSS) variation behaviors among on-body channels and between on-body and off-body communication channels. Based on distinct RSS variations, BANA adopts clustering analysis to differentiate the signals from an attacker and a legitimate node. We also make use of multi-hop on-body channel characteristics to enhance the robustness of our authentication mechanism. The effectiveness of BANA is validated through extensive real-world experiments under various scenarios. It is shown that BANA can accurately identify multiple attackers with minimal amount of overhead. [ABSTRACT FROM AUTHOR]

Reliable spectrum sensing is a key functionality of a cognitive radio network. Cooperative spectrum sensing improves the detection reliability of a cognitive radio system but also increases the system energy consumption which is a critical factor particularly for low-power wireless technologies. A censored truncated sequential spectrum sensing technique is considered as an energy-saving approach. To design the underlying sensing parameters, the maximum average energy consumption per sensor is minimized subject to a lower bounded global probability of detection and an upper bounded false alarm rate. This way both the interference to the primary user due to miss detection and the network throughput as a result of a low false alarm rate are controlled. To solve this problem, it is assumed that the cognitive radios and fusion center are aware of their location and mutual channel properties. We compare the performance of the proposed scheme with a fixed sample size censoring scheme under different scenarios and show that for low-power cognitive radios, censored truncated sequential sensing outperforms censoring. It is shown that as the sensing energy per sample of the cognitive radios increases, the energy efficiency of the censored truncated sequential approach grows significantly. [ABSTRACT FROM PUBLISHER]

In this paper, cognitive routing coupled with spectrum sensing and sharing in a multi-channel multi-hop cognitive radio network (CRN) is investigated. Recognizing the spectrum dynamics in CRN, we propose an opportunistic cognitive routing (OCR) protocol that allows users to exploit the geographic location information and discover the local spectrum access opportunities to improve the transmission performance over each hop. Specifically, based on location information and channel usage statistics, a secondary user (SU) distributedly selects the next hop relay and adapts its transmission to the dynamic spectrum access opportunities in its neighborhood. In addition, we introduce a novel metric, namely, cognitive transport throughput (CTT), to capture the unique properties of CRN and evaluate the potential relay gain of each relay candidate. A heuristic algorithm is proposed to reduce the searching complexity of the optimal selection of channel and relay. Simulation results are given to demonstrate that our proposed OCR well adapts to the spectrum dynamics and outperforms existing routing protocols in CRN. [ABSTRACT FROM AUTHOR]

Since, current widely available network protocols/ systems are mainly throughput-oriented designs, meeting stringent delay requirements of new applications such as virtual reality and vehicle-to-vehicle communications on cellular network requires new network protocol/system designs. C2TCP is an effort toward that new design direction. C2TCP is inspired by in-network active queue management designs such as RED and CoDel and motivated by lack of a flexible end-to-end approach which can adapt itself to different applications’ QoS requirements without modifying any network devices. It copes with unique challenges in cellular networks for achieving ultra-low latency (including highly variable channels, deep per-user buffers, self-inflicted queuing delays, and radio uplink/downlink scheduling delays) and intends to satisfy stringent delay requirements of different applications while maximizing the throughput. C2TCP works on top of classic throughput-oriented TCP and accommodates various target delays without requiring any channel prediction, network state profiling, or complicated rate adjustment mechanisms. We have evaluated C2TCP in both real-world environment and extensive trace-based emulations and compared its performance with different TCP variants and state-of-the-art schemes including PCC-Vivace, Google’s BBR, Verus, Sprout, TCP Westwood, and Cubic. Results show that C2TCP outperforms all these schemes and achieves lower average delay, jitter, and 95th percentile delay for packets. [ABSTRACT FROM AUTHOR]