This study presents a neural network-based method for static Bluetooth Low Energy (BLE) localization, using phase measurements and carrier frequency per data packet, captured from a single (or multiple) multi-antenna receiver(s). Deterministic phase-based techniques fail to accurately estimate the position of a tag using a single multi-antenna receiver with closely spaced antennas. This is due to the random and unknown carrier phase offset (CPO) at each antenna element, introduced during BLE’s inherent frequency hopping between consecutive packets. Our long short-term memory (LSTM) model learns to handle the unknown distribution of CPO and suppress phase noise, enabling robust localization with just one multi-antenna receiver. Among the tested architectures, the LSTM model showed notable resilience to phase noise and achieved higher accuracy during high-rate tag transmissions compared to the feedforward (FF) and convolutional neural network (CNN) models. All models were trained on simulated or real data and tested on real data from various environments, where they outperformed deterministic techniques—even in cases where those techniques either excelled or failed to estimate the tag’s position.

This work establishes a relaxed formulation for the problem of user association with energy efficiency (EE) in the downlink of heterogeneous networks. A distributed solver is offered, based on belief propagation (BP)-based message passing between (micro or macro) base stations. In addition, correctness and convergence guarantees are provided for the employed loopy BP and computation load is analyzed and reduced (with careful precomputations). Numerical results indicate that the proposed approach offers higher EE geometric mean, while not sacrificing spectral efficiency, compared to prior art.

This work offers design and implementation of in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, in-network message passing problem, often at the expense of increased total delay. Examples from Gaussian belief propagation and average consensus (AC) are provided, along with the derivation of a statistical convergence metric for the latter case. A k-means method is offered that maps the elements of the calculated vector to the different WSN terminals and overall execution delay (in number of iterations) is quantified. Interestingly, it is shown that there are divergent instances of the in-network message passing algorithms that become convergent, under asynchronous operation. Ambient solar energy harvesting availability is also studied, controlling the probability of successful (or not) message passing. Hopefully, this work will spark further interest for asynchronous message passing algorithms and technologies that enable in-network inference, toward ambiently powered, batteryless Internet of Things-That-Think.

This work exploits ultra-low cost, commodity, radio frequency identification (RFID) tags as elements of a reconfigurable intelligent surface (RIS). Such batteryless tags are powered and controlled by a software-defined radio reader, with properly modified software, so that a source-destination link is assisted, operating at a different band. Signal model includes small-scale and large-scale fading, direct link, as well as specific parameters relevant to reflection (i.e., backscatter) radio, such as antenna structural mode and reflection efficiency, typically overlooked in the literature. An algorithm is offered that computes the optimal RIS configuration with complexity of$\mathcal {O}(M\log {M})$in number of elements$M$, instead of intractable exponential complexity of exhaustive search, while accommodating any number$K\geq 2$of loads. With the proposed algorithm, it is shown that performance gains reach a plateau for constant element spacing and increasing number of elements, suggesting that the weak, passive nature of backscattered links limits the performance gains, even with perfect channel estimation. Channel estimation with linear minimum mean squared error (LMMSE) estimator is shown to be effective, provided that there are sufficient number of pilot symbols. A concrete way is offered to design and prototype a wireless, batteryless, RF-powered, reconfigurable surface and a proof-of-concept is experimentally demonstrated.

Backscatter radio utilizes the reflection that an electromagnetic wave undergoes when it impinges an unmatched-to-a-load antenna, in order to achieve ultra-low-power communication. This work exploits principles commonly found in the ultra-low-power, backscatter communication literature, offering multi-element array functionalities to a single-antenna receiver. A small number of simple, switching backscattering tags deployed in space, emulate a distributed, multi-element antenna array by copying a transmitter’s signal to distinct frequency bands. A receiver can then obtain independent observations of the same signal by discriminating said bands. Using the backscatter tag-based array, the direction of arrival (DoA) estimation problem is addressed without the cost of multiple RF front ends or hardware modifications, at either end of a wireless communication link. The feasibility of the idea was examined through both simulations and experimental deployments. An absolute error of$\approx 20$degrees was observed when utilizing 5 custom-built backscattering tags, while simulations showed that more than 10 tags can offer error of less than 5 degrees.

Is it possible to build ultra-low power wireless sensor networks (WSN) that exploit the inherent parallel and distributed nature of powerful message passing/inference algorithms, embrace ultra-low power communication principles and make autonomous, in-network decisions, solely powered by the environment? While edge and cloud computing emerge, this work points towards the opposite direction, inspired by the fact that ambient energy, either from radio frequency (RF), sun, motion, temperature or even living organisms, has fixed (on average) density per surface (or volume). It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, message passing problem. Examples from Gaussian Belief Propagation and Average Consensus are offered; ambient energy harvesting and availability are quantified, controling the probability of successful (or not) message passing. Such interrupted communication requires distributed algorithms robust to asynchrony, at the expense of increased overall delay. Simulation and experimental validation are offered in a WSN testbed with solar energy harvesting. Future work will focus on overall delay minimization.

This work exploits commodity, ultra-low cost, com-mercial radio frequency identification tags (RFID) as the elements of a reconfigurable surface. Such batteryless tags are powered and controlled by a software-defined (SDR) reader, with properly modified software, so that a source-destination link is assisted, operating at a different carrier frequency. In terms of theory, the optimal gain and corresponding best element configuration is offered, with tractable polynomial complexity (instead of exponential) in number of elements. In terms of practice, a concrete way to design and prototype a wireless, batteryless, RF-powered, reconfigurable surface is offered and a proof-of-concept is experimentally demonstrated. It is also found that even with perfect channel estimation, the weak nature of backscattered links limits the performance gains, even for large number of surface elements. Impact of channel estimation errors is also studied. Future extensions at various carrier frequencies could be directly accommodated, through simple modifications in the antenna and matching network of each RFID tag/surface element.

Conventional direction of arrival (DoA) techniques employ multi-antenna receivers with increased complexity and cost. This work emulates a multi-antenna system using a singleantenna receiver and exploiting the beauty and simplicity of backscatter radio. More specifically, a number of simple backscatter radio tags offer copies of the hidden RF source, relayed in space and shifted in frequency, while requiring minimal time-synchronisation. DoA of a hidden RF source was estimated with an error of less than 5 degrees, exploiting a small number of simple, ultra-low-cost backscattering tags.

Commercial radio frequency identification (RFID) readers have to resolve collisions between tags without sacrificing throughput. This work proposes a Viterbi joint sequence detector and a 2-symbol joint tag information detector that can resolve collision between two tags in the physical layer. In sharp contrast to prior art, the proposed closed-form signal model takes into account the asynchrony level between the two collided tag responses, due to the low-cost hardware of commercial RFID tags that follow industry’s Gen2 protocol. The asynchrony is considered as the time offset between the beginnings of the two tags’ RN16 responses and is modeled through a derived shaping matrix that depends on the delayed tag information. Performance evaluation of the proposed detectors shows improved performance compared to prior art, under different operating regimes. It is also found that for different time offset values, bit error rate (BER) does not present a monotonic behavior. Finally, it is shown that clustering techniques on the filtered received signal should explicitly take into account the time offset, since the latter can modify the number of observed clusters. Asynchronous detection has been overlooked in classic digital communications; in the era of batteryless, ultra-low cost tags, more work is clearly needed.

Commercial radio frequency identification (RFID) readers have to resolve collisions between tags, without sacrificing throughput. This work proposes a Viterbijoint sequence detectoras well as a 2-symbol joint tag information detector that can resolve a collision between two tags in the physical layer. In sharp contrast to prior art, the proposed closed-form signal model takes into account the asynchrony level between the two collided tag responses, which is not uncommon with commercial, low-cost RFID tags that follow industry’s Gen2 protocol. The asynchrony is considered as the time offset$\tau $between the beginnings of the two tags’ responses and is modeled through a derivedshapingmatrix that depends on the delayed tag information. Performance evaluation of the proposed detectors with simulated data under Ricean fading, as well as experimental data with software-defined radio, reveals improved performance compared to prior art, under various operating regimes. It is also shown that for different values of the parameter$\tau $, BER does not present a monotonic behaviour. As a collateral dividend, it is found that clustering techniques on thefilteredreceived signal should explicitly take into account the time offset$\tau $, since the latter modifies the number of observed clusters.

Passive Gen2 radio frequency identification (RFID) tags work thanks to the utilization of line codes that balance their operation between two opposite states: absorbing RF (for energy harvesting) and reflecting RF (for backscattering/communications). Given the current RF harvesting technology, batteryless tags need to be located very close to an active illuminator in order to harvest sufficient energy and operate. To tackle this limitation, prior art has tried to bring the illuminator closer to the tags by designing proprietary illuminating architectures. Our solution comes in two parts. First, we offer a novel, Gen2-compliant, near-optimal, noncoherent sequence detection algorithm with linear complexity (in the sequence length) for Miller line codes. We leverage the robustness of this algorithm to overcome issues inherent in multistatic setups, such as carrier frequency offset. Second, we propose a modular multistatic architecture that makes use of low-cost commodity software defined radios (SDR) and omnipresent Ethernet infrastructure. Simulations and experimental results in a monostatic, bistatic, or multistatic SDR testbed with commercial RFIDs, corroborate the low-cost, real-time and near-optimal flavor of our solution.

This work studies inference-based resource allocation in ultra low-power, large-scale backscatter sensor networks (BSNs). Several ultra-low cost and power sensor devices (tags) are illuminated by a carrier and reflect the measured information towards a wireless core that uses conventional Marconi radio technology. The development of multi-cell BSNs requires few multi-antenna cores and several low-cost scatter radio devices, targeting at maximum possible coverage. The average signal-to-interference-plus-noise ratio (SINR) of maximum-ratio combining (MRC) and zero-forcing (ZF) linear detectors is found and harnessed for frequency sub-channel allocation at tags, exploiting long-term SINR information. The resource allocation problem is formulated as an integer programming optimization problem and solved through the Max-Sum message-passing algorithm. The proposed algorithm is fully parallelizable and adheres to simple message-passing update rules, requiring mainly addition and comparison operations. In addition, the convergence to the optimal solution is attained within very few iteration steps. Judicious simulation study reveals that ZF detector is more suitable for large scale BSNs, capable to cancel out the intra-cell interference. It is also found that the proposed algorithm offers at least an order of magnitude decrease in execution time compared to conventional convex optimization methods.

Line coding in batteryless, RF energy harvesting RFID/IoT tags is critical, as it balances two conflicting operations: RF energy absorption (for batteryless powering) and RF energy reflection for backscattering (communication). This work studies Miller line coding, one of the two line codes utilized in commercial RFID, deriving rules for zero-centered or not Miller flat fading signal models. A novel, linear complexity in the sequence length algorithm is proposed for noncoherent Miller sequence detection that can operate within a quarter of a dB from the optimal coherent. Simulation results are corroborated by experimentation in a Gen2 RFID SDR-based testbed. This work provides evidence in favor of more lightweight protocols, free of preamble or pilot bits for future RFID/IoT tags, potentially offering a 10% reading speed gain over the contemporary Gen2 protocol or future short-packet IoT protocols.

Motivated by the extensive use of simplified models in the recent ambient backscatter literature, this work will demonstrate the importance of utilizing realistic models when deriving appropriate detectors. For this purpose, two recent works on ambient backscatter will be evaluated under realistic channel models, accounting for all communication parameters. It is shown that assuming a complex normal illuminator (i.e., ignoring illuminator's signal structure) leads to significant performance losses compared to explicitly considering illuminator's modulation (e.g., FM). Based on FM illumination, the importance of the latter is further highlighted by deriving a high performance, fully noncoherent sequence detector. In most cases, switching techniques (e.g., SBPSK) are shown to outperform conventional techniques (OOK), at the expense of increased complexity.

This work offers both analog and digital tag modulation schemes and respective receiver designs, for ultra-low power, high performance, ambient backscatter communications. All proposed techniques are based on simple, but careful, switching frequency control at the tag, allowing for the easy frequency-domain multiple access. First, a digital modulation scheme is offered, namely pseudo-frequency shift keying, assuming illumination from constant envelope-modulated signals and a fully coherent detector is derived along with closed-form probability of error. A second digital modulation scheme is also offered, based on a frequency-shifted form of binary phase shift keying (S-BPSK), relaxing the constant-envelope requirement for the illuminator and an illumination-agnostic detector is derived. Based on S-BPSK, short packet error correction coding is utilized for ambient backscatter communication, for the first time in the literature. It is shown that the proposed coded scheme under modulated ambient signal illumination & wireless channel variation, offers tremendous performance gains, i.e., modulation of the ambient signal is helpful. Finally, a third, purely analog, modulation scheme is analyzed, based on FM remodulation principles. A low-cost tag is implemented, demonstrating tag-to-receiver ranges up to 26 meters outdoors, power consumption of 24 μWatts in continuous operation, able to be interrogated by any conventional frequency modulation (FM) receiver. The proposed techniques cover a large variety of omnipresent wireless industry systems, enabling universal ambient backscatter and relevant wireless information and power transfer (WIPT) applications.

This paper studies both limited sensitivity and nonlinearity of far field RF energy harvesting observed in reality and quantifies their effect, attempting to fill a major hole in the simultaneous wireless information and power transfer (SWIPT) literature. RF harvested power is modeled as an arbitrary nonlinear, continuous, and non-decreasing function of received power, by considering limited sensitivity and saturation effects. RF harvester's sensitivity may be several dBs worse than communications receiver's sensitivity, potentially rendering RF information signals useless for energy harvesting purposes. Given finite number of datapoint pairs of harvested (output) power and corresponding input power, a piecewise linear approximation is applied and the statistics of the harvested power are offered, as a function of the wireless channel fading statistics. Limited number of datapoints is needed and accuracy analysis is also provided. Case studies include duty-cycled (non-continuous), as well as continuous SWIPT, comparing with industry-level, RF harvesting. The proposed approximation, even though simple, offers accurate performance for all studied metrics. On the other hand, linear models or nonlinear-unlimited sensitivity harvesting models deviate from reality, especially in the low-input-power regime. The proposed methodology can be utilized in the current and future SWIPT research.

Scatter radio, i.e., communication by means of reflection, has been recently proposed as a viable ultra-low power solution for wireless sensor networks (WSNs). This paper offers a detailed comparison between monostatic and multistatic scatter radio architectures. In monostatic architecture, the reader consists of both the illuminating transmitter and the receiver of signals scattered back from the sensors. The multistatic architecture includes several ultra-low cost illuminating carrier emitters and a single reader. Maximum-likelihood coherent and noncoherent bit error rate (BER), diversity order, average information, and energy outage probability comparison is performed, under dyadic Nakagami fading and filling a gap in the literature. It is found that: 1) diversity order, BER, and tag location-independent performance bounds of multistatic architecture outperform monostatic; 2) energy outage due to radio frequency (RF) harvesting for passive tags, is less frequent in multistatic than monostatic architecture; and 3) multistatic coverage is higher than monostatic. Furthermore, a proof-of-concept digital multistatic scatter radio WSN with a single receiver, four low-cost emitters, and multiple ambiently-powered low-bitrate tags, perhaps the first of its kind, is experimentally demonstrated (at 13 dBm transmission power), covering an area of 3500 m2. Research findings are applicable in the industries of WSNs, RF identification, and emerging Internet-of-Things.

This work proposes a new limited feedback channel estimation framework. The proposed approach exploits a sparse representation of the double directional wireless channel model involving an over complete dictionary that accounts for the antenna directivity patterns at both base station (BS) and user equipment (UE). Under this sparse representation, a computationally efficient limited feedback algorithm that is based on single-bit compressive sensing is proposed to effectively estimate the downlink channel. The algorithm is lightweight in terms of computation, and suitable for real-time implementation in practical systems. More importantly, under our design, using a small number of feedback bits, very satisfactory channel estimation accuracy is achieved even when the number of BS antennas is very large, which makes the proposed scheme ideal for massive MIMO 5G cellular networks. Judiciously designed simulations reveal that the proposed algorithm outperforms a number of popular feedback schemes in terms of beam forming gain for subsequent downlink transmission, and reduces feedback overhead substantially when the BS has a large number of antennas.

Scatter radio, i.e., communication by means of reflection, has been recently proposed as a promising technology for low-power wireless sensor networks (WSNs). Specifically, this paper offers noncoherent receivers in scatter radio frequency-shift keying, for either channel-coded or uncoded scatter radio reception, in order to eliminate the need for training bits of coherent schemes (for channel estimation) at the packet preamble. Noncoherent symbol-by-symbol and sequence detectors based on hybrid composite hypothesis test (HCHT) and generalized likelihood-ratio test, for the uncoded case and noncoherent decoders based on HCHT, for small block-length channel codes, are derived. Performance comparison under Rician, Rayleigh, or no fading, taking into account fixed energy budget per packet is presented. It is shown that the performance gap between coherent and noncoherent reception depends on whether channel codes are employed, the fading conditions (e.g., Rayleigh versus Rician versus no fading), as well as the utilized coding interleaving depth; the choice of one coding scheme over the other depends on the wireless fading parameters and the design choice for extra diversity versus extra power gain. Finally, experimental outdoor results at 13-dBm transmission power corroborate the practicality of the proposed noncoherent detection and decoding techniques for scatter radio WSNs.

Orthogonal modulation, for example, frequency-shift keying (FSK) or pulse-position modulation (PPM), is primarily used in relatively-low-rate communication systems that operate in the power-limited regime. Optimal noncoherent detection of orthogonally modulated signals takes the form of sequence detection and has exponential (in the sequence length) complexity when implemented through an exhaustive search among all possible sequences. In this work, for the first time in the literature, we present an algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of orthogonally modulated signals in flat fading with log-linear (in the sequence length) complexity. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-likelihood (ML) noncoherent sequence detector. Simulation studies indicate that the optimal noncoherent FSK detector attains coherent-detection performance when the sequence length is on the order of 100, offering a 3-5 dB gain over the typical energy (single-symbol) detector. While the conventional exhaustive-search approach becomes infeasible for such sequence lengths, the proposed implementation requires a log-linear only number of operations, opening new avenues for practical deployments. Finally, we show that our algorithm also solves efficiently the optimal noncoherent sequence detection problem in contemporary radio frequency identification (RFID) systems.

Frequency-shift keying (FSK) is an orthogonal modulation technique that is primarily used in relatively low-rate communication systems that operate in the power-limited regime. Optimal noncoherent detection of FSK takes the form of sequence detection and has exponential complexity in the sequence length when implemented through an exhaustive search among all possible sequences. In this work, for the first time in the literature, we present an algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of orthogonally modulated signals in flat fading with log-linear complexity in the sequence length. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-likelihood (ML) noncoherent sequence detector. Finally, we show that our algorithm also solves efficiently the optimal noncoherent sequence detection problem in contemporary radio-frequency-identification (RFID) systems.

Distributed cooperative network location estimation in motionless and mobile narrowband wireless sensor networks (WSNs) is studied, where agents (to be localized) are several hops away from the anchors (with a priori known location). This work proposes a reduced-communication cooperative and distributed particle filtering (CoopPF) approach based on variational inference (VI) Gaussian mixture modeling (GMM), where network nodes exchange information locally, i.e. only with neighboring terminals; each node transmits the parameters of the estimated Gaussian mixture, instead of the whole posterior density, offering tremendous reduction in communication overhead, as required in narrowband applications (e.g. underwater communications). The proposed VI approach jointly estimates the number of required Gaussians and their parameters, in sharp contrast to standard expectation-maximization techniques, where the number of components must be estimated first with other techniques (e.g. clustering). Accuracy comparable to state-of-the-art PF cooperative localization is demonstrated, with an order of magnitude reduction in communication overhead.

Scatter radio, i.e., communication by means of reflection, has emerged as a potential key-enabling technology for ultra low-cost, large-scale, ubiquitous sensor networking. This work studies bistatic scatter radio, where carrier emitter is dislocated from the software defined radio receiver. The ultimate goal of this work is to extend the communication range. Towards that goal, noncoherent channel coding is incorporated in bistatic scatter radio. Short block length channel codes are proposed with ultra low-complexity encoding, ideal for resource-constraint scatter radio tags. A novel composite hypothesis testing decoding rule is designed, that achieves high diversity order through interleaving. Simulation results corroborate the efficiency of the proposed noncoherent schemes over Rician fading and demonstrate that noncoherent setups offer comparable bit error rate (BER) performance with respect to coherent counterparts.

With low monetary cost and minimal energy consumption, communications by means of reflection and scatter radio have emerged as key enabler for low-cost, large-scale and dense ubiquitous wireless sensor network applications. This work maximizes scatter radio communication range by (a) proposing a novel coherent receiver of frequency-shift keying (FSK) modulation for the bistatic scatter radio channel (i.e., carrier emitter and receiver are dislocated) and (b) employing specific short block-length cyclic error-correcting codes. Despite the presence of three unknown channel links due to the bistatic setup and multiple unknown scatter radio-related parameters, the proposed receiver vastly improves BER performance compared to state-of-the-art bistatic scatter radio receivers. Experimental corroborating results are offered, with a commodity software-defined radio (SDR) reader, a custom scatter radio tag and omnidirectional antennas. Tag-to-reader ranges up to 150 meters are reported with as little as 20 milliWatt transmission power, offering range extension of approximately 10 additional meters compared to state-of-the-art bistatic receivers.

On-off keying (OOK) is a simple orthogonal modulation technique that is primarily used in the noncoherent mode, that is, when the propagation channel is unknown at the receiver. Although the noncoherent OOK detector is usually operated as a simple single-symbol (one-shot) energy detector, it does not take into account memory that is induced by the channel. Hence, optimal noncoherent detection of OOK takes the form of sequence detection and has exponential complexity in the sequence length when implemented through an exhaustive search among all possible sequences. In this work, we present a novel algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of OOK signals in flat fading with cubic (in the sequence length) complexity. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-a-posteriori (MAP) noncoherent sequence detector. Due to its polynomial complexity, the proposed algorithm allows implementation of the optimal sequence detector for large sequence lengths, for which the conventional exhaustive-search approach becomes infeasible. Interestingly, with a large enough sequence length, the noncoherent detector attains nearly-coherent performance, although it does not utilize any knowledge about the propagation channel.

With rapid advances of scatter radio systems, the principle of reflection rather than active transmission employed by backscatter sensor networks, has emerged as a potential key enabler for low-cost, large-scale and dense ubiquitous sensor networks. Despite the presence of three different unknown channel links due to the bistatic setup (i.e., carrier emitter and receiver are dislocated), as well as multiple unknown scatter radio-related parameters, this work offers a novel coherent receiver of frequency-shift keying (FSK) modulation for the bistatic scatter radio channel. Furthermore, with the objective of range maximization, specific short block-length cyclic channel codes are utilized. The proposed approach requires minimum encoding complexity, ideal for resource-constrained, ultra-low power (e.g. microcontroller unit-based), low-bit rate scatter radio tags, adheres to simple low-complexity decoding at the receiver and achieves high-order signal diversity. Analysis is followed by experimental validation with a commodity software-defined radio (SDR) reader and a custom scatter radio tag; tag-to-reader ranges up to 150 meters are demonstrated with as little as 20 milliWatt transmission power, increasing sensing ranges by approximately 10 additional meters, compared to state-of-the-art bistatic scatter radio receivers. With the imminent emergence of backscatter sensor networks, this work serves as a small step forward towards the realization of low-cost, low-power, increased-range, wireless sensing applications.

As wireless sensor networks (WSNs) become denser, simultaneous transmissions (on the same time slot and frequency channel of two or more terminals) may cause severe interference. Appropriate interference-aware allocation is a complex problem and distributed frequency allocation is even harder. This work studies the problem of assigning frequency channels for a given WSN routing tree, such that: a) time scheduling and frequency allocation are performed in a distributed way, i.e. information exchange is only performed among neighboring terminals, and b) detection of potential interfering terminals is simplified. The algorithm imprints space, time and frequency constraints, assuming half-duplex, single-antenna radios into a loopy factor graph (FG) and performs iterative message passing. Convergence to a valid solution is addressed based on appropriate modifications of the resulting message passing inference algorithm. The proposed algorithm is compared with two distributed frequency allocation algorithms, based on game-theory or min-max interference control. It is shown that the proposed distributed algorithm offers comparable performance with state-of-the-art, even though it utilizes simplified interfering terminals set detection.

We consider the problem of distributed cooperative location estimation in motionless and mobile narrowband wireless sensor networks (WSNs), where network terminals (to be localized) are several hops away from the anchor nodes (with a priori known location). Such problem is inspired by practical setups where communication bandwidth is limited, as in underwater systems with narrow-band acoustic modems. A cooperative and distributed particle filter (Coop-PF) is designed, where network nodes exchange information locally, i.e. only with neighboring terminals. However, each node does not communicate particles. Instead, a combination of clustering, mixture models and parameterization of the communicated information is explored. The proposed approach improves performance compared to utilizing a fixed Gaussian mixture model and demonstrates accuracy comparable to state-of-the-art wideband cooperative localization, while achieving a two orders of magnitude reduction in communication. As a collateral dividend, a computationally-efficient version is also offered.

This work studies the recently proposed geometric monitoring (GM) method in order to reduce the amount of necessary channel state information (CSI) in network (i.e., distributed) setups. Specifically, a network of amplify-and-forward (AF) relays is studied and the GM method is appropriately adapted. The basic idea is that GM could flag time instances where CSI exchange is unnecessary and, thus, the relay network could abstain from CSI exchange. It is found that, compared to alternative approaches, our GM approach can achieve significant CSI reduction in a variety of network setups.

Scatter radio achieves communication by reflection and requires low-cost and low-power RF front-ends. However, its use in wireless sensor networks (WSNs) is limited, since commercial scatter radio (e.g. RFID) offers short ranges of a few tens of meters. This work redesigns scatter radio systems and maximizes range through non-classic bistatic architectures: the carrier emitter is detached from the reader. It is shown that conventional radio receivers may show a potential 3dB performance loss, since they do not exploit the correct signal model for scatter radio links. Receivers for on-off-keying (OOK) and frequency-shift keying (FSK) that overcome the frequency offset between the carrier emitter and the reader are presented. Additionally, non-coherent designs are also offered. This work emphasizes that sensor tag design should accompany receiver design. Impact of important parameters such as the antenna structural mode are presented through bit error rate (BER) results. Experimental measurements corroborate the long-range ability of bistatic radio; ranges of up to 130 meters with 20 milliwatts of carrier power are experimentally demonstrated, with commodity software radio and no directional antennas. Therefore, bistatic scatter radio may be viewed as a key enabling technology for large-scale, low-cost and low-power WSNs.

This work is motivated by the reachback connectivity scenario in resource-constrained wireless sensor networks (WSNs): a single terminal at maximum power cannot establish a reliable communication link with the intended destination. Thus, neighboring distributed transmitters should contribute their radios and transmission power, in order to achieve reliable transmission of a common message. This work is particularly interested in low-SNR scenarios with unreliable feedback channels, no channel state information (CSI), and commodity radios, where carrier phase/frequency synchronization is not possible. Concrete non-coherent maximum likelihood and energy detection receivers are developed for zero-feedback distributed beamforming. The proposed receivers are compared with non-coherent energy harvesting reception, based on simple time-division multiple access. It is shown that the proposed zero-feedback distributed beamforming receivers overcome connectivity adversities at the low-SNR regime. This is achieved by exploiting signals' alignment of$M$distributed transmitters (i.e., beamforming), even with commodity radios, at the expense of network (total) power consumption. Application scenarios include resource-constrained WSNs or emergency radio situations.

For applications that require large numbers of wireless sensors spread in a field, backscatter radio can be utilized to minimize the monetary and energy cost of each sensor. Commercial backscatter systems such as those in radio frequency identification (RFID), utilize modulation designed for the bandwidth limited regime, and require medium access control (MAC) protocols for multiple access. High tag/sensor bitrate and monostatic reader architectures result in communication range reduction. In sharp contrast, sensing applications typically require the opposite: extended communication ranges that could be achieved with bitrate reduction and bistatic reader architectures. This work presents non-coherent frequency shift keying (FSK) for bistatic backscatter radio; FSK is appropriate for the power limited regime and also allows many RF tags/sensors to convey information to a central reader simultaneously with simple frequency division multiplexing (FDM). However, classic non-coherent FSK receivers are not directly applicable in bistatic backscatter radio. This work a) carefully derives the complete signal model for bistatic backscatter radio, b) describes the details of backscatter modulation with emphasis on FSK and its corresponding receiver, c) proposes techniques to overcome the difficulties introduced by the utilization of bistatic architectures, such as the carrier frequency offset (CFO), and d) presents bit error rate (BER) performance for the proposed receiver and carrier recovery techniques.

We present the setup of a complete software-defined radio (SDR) testbed for non-coherent zero-feedback distributed beamforming. Three custom-built, embedded RF transceivers along with a commodity, low-cost SDR commercial receiver are deployed in an indoors lab environment. In sharp contrast with prior art on collaborative beamforming, the proposed scheme assumes no feedback between receiver and transmitters and no access to the transmitters' local oscillators for carrier phase adjustments. Quite interestingly, frequency offsets are exploited in this work. Zero-feedback beamforming with unsynchronized carriers is experimentally validated in terms of bit-error-rate (BER) and compared with simulation results. To the best of our knowledge, this is the first testbed for demonstrating and evaluating zero-feedback, channel state information (CSI)-free, distributed beamforming.

This work derives and evaluates single-antenna detection schemes for collided radio frequency identification (RFID) signals, i.e. simultaneous transmission of two RFID tags, following FM0 (biphase-space) encoding. In sharp contrast to prior art, the proposed detection algorithms take explicitly into account the FM0 encoding characteristics, including its inherent memory. The detection algorithms are derived when error at either or only one out of two tags is considered. It is shown that careful design of one-bit-memory two-tag detection can improve bit-error-rate (BER) performance by 3dB, compared to its memoryless counterpart, on par with existing art for single-tag detection. Furthermore, this work calculates the total tag population inventory delay, i.e. how much time is saved when two-tag detection is utilized, as opposed to conventional, single-tag methods. It is found that two-tag detection could lead to significant inventory time reduction (in some cases on the order of 40%) for basic framed-Aloha access schemes. Analytic calculation of inventory time is confirmed by simulation. This work could augment detection software of existing commercial RFID readers, including single-antenna portable versions, without major modification of their RF front ends.

This work studies zero-feedback distributed beamforming; we are motivated by scenarios where the links between destination and all distributed transmitters are weak, so that no reliable communication in the form of pilot signals or feedback messages can be assumed. Furthermore, we make the problem even more challenging by assuming no specialized software/hardware for distributed carrier synchronization; we are motivated by ultra-low complexity transceivers. It is found that zero-feedback (i.e. blind), constructive, distributed signal alignment at the destination is possible; the proposed scheme exploits lack of carrier synchronization among M distributed transmitters and provides beamforming gains. Possible applications include reachback communication in low-cost sensor networks with simple (i.e. conventional, no carrier frequency/phase adjustment capability) radio transceivers.

This work evaluates opportunistic relaying in the presence of thermal noise as well as interference, when channel sensing is conducted reactively, in slow fading environments. The studied scenario employs a single gateway that provides access towards several destinations with weak links and exploits a network of intermediate relays. In sharp contrast to prior art, no inter-relay channel state information or communication is assumed, no network coding is needed, while low-complexity receivers at each destination are employed. It is shown that information can be relayed without delay, while harvesting benefits of cooperative diversity, even at the presence of interference. The participating relays are required to offer strong paths towards source and destination, while at the same time they are as "isolated" as possible from each other. From that perspective, the notion of relay "usefulness" is redefined in both noise and interference-limited environments, under opportunistic relaying.

In this work, we extend low-complexity, slow-fading opportunistic relaying in both noise and interference-limited setups in order to harvest cooperation benefits without sacrificing overall delay. In particular, we show that information can be reliably relayed without delay, at the presence of interference, while harvesting benefits of cooperative diversity due to the opportunistic protocol nature. Under interference-aware opportunistic relaying, relays become useful provided that they offer strong paths towards source and destination, while at the same time they are as "isolated" as possible from each other (e.g. when directive antennas are utilized). This work could potentially assist reduced-delay, cooperative relaying applications (e.g. wireless access) with low-complexity receivers.

Abstract Simple bibliometric indicators, such as average number of citations per publication per researcher, or the recently proposed Hirsch index (h‐index), are nowadays tracked by online repositories, including Web of Science (WOS), and often affect critical decision making. This work proposes appropriate scaling of the h‐index based on its probability distribution that is calculated for any underlying citation distribution. The proposed approach outperforms existing index estimation models that have focused on the expected value only (i.e., first moment). Furthermore, it is shown that average number of citations per publication per scientific field, total number of publications per researcher, as well as researcher's h‐index measured value, expected value, and standard deviation constitute the minimum information required for meaningful h‐index ranking campaigns; otherwise contradicting ranking results emerge. This work may potentially shed light to (current or future) large‐scale, h‐index‐based bibliometric evaluations.

Sensor collision (interference) is studied in a large network of low bit-rate sensors that communicate via backscatter, i.e. modulate the reflection of a common carrier transmitted by a central reader. Closed-form analysis is provided, quantifying sensor collision (interference) in high-density, backscatter sensor networks (BSN), as a function of number of tags and aggregate bandwidth. Analysis is applicable to a broad class of sensor subcarrier modulations, propagation environments and reader antenna directivity patterns. It is discovered that anti-collision performance in high-density backscatter sensor networks is feasible provided that appropriate modulation is used at each sensor. That is due to the round-trip nature of backscatter communication as well as the extended target range, which both impose stringent requirements on spectrum efficiency, not easily met by all modulations. Furthermore, aggregate bandwidth savings for given anti-collision performance are quantified, when simple division techniques on subcarrier (modulating) frequency and space (via moderately directive hub antenna) are combined.

In this paper, we provide low-complexity interference-aware opportunistic relaying (IaOR), suitable for reduced-delay wireless access. In particular, we show that reactive, periodic and distributed spectrum sensing by a set of cooperative relays can offer improved reliability, even in both noise and interference-limited scenarios with low-complexity receivers. This work could potentially assist reduced-delay, high node-density wireless access, as in modern urban environments.

Practical cooperative diversity protocols often rely on low-cost radios that treat multiple in-band signals as noise and thus require strictly orthogonal transmissions. We analyze the performance of a class of opportunistic relaying protocols that employ simple packet level feedback and strictly orthogonal transmissions. It is shown that the diversity-multiplexing tradeoff of the proposed protocols either matches or outperforms the multi-input-single-output (MISO), zero-feedback performance. These gains indicate that low complexity radios and feedback could be an appealing architecture for future user cooperation protocols.

Backscatter radio is proposed for sensor networks. In that way, the transmitter for each sensor is simplified to a transistor connected to an antenna and therefore, the cost for each sensor's communicator becomes negligible, while energy used for wireless communication per sensor is minimized. A software-defined transceiver is built to transmit a carrier, receive the reflections from various sensors and extract their transmitted messages. This work presents a thorough model of the backscatter radio link, the system architecture and a set of data extraction techniques for each sensor's information, testing in practice a sensor communicating through backscatter at a range of approximately 15 meters indoors, with 5 milliwatt transmission power at 10 bits per second. This work highlights the idiosyncrasies of the backscatter channel and provides a new communication perspective in the fertile area of scalable sensor networks, especially when low bit-rate, ultra-low cost sensors are required.

We provide an overview of our experimental system, testing in practice a sensor communicating through backscatter at a range of approximately 15 meters indoors, with 5 mW transmission power at 10 bits per second. Our system is designed for simultaneous reception of signals continuously radio-backscattered from several ultra low-cost sensors. This work highlights the idiosyncracies of the backscatter channel and presents a proof- of-concept demonstration of backscatter radio for wireless sensor networks, especially when low bit-rate, ultra low-cost sensors are required.

In this paper, we present simple opportunistic relaying with decode-and-forward (DaF) and amplify-and-forward (AaF) strategies under an aggregate power constraint. In particular, we consider distributed relay-selection algorithms requiring only local channel knowledge. We show that opportunistic DaF relaying is outage-optimal, that is, it is equivalent in outage behavior to the optimal DaF strategy that employs all potential relays. We further show that opportunistic AaF relaying is outage-optimal among single-relay selection methods and significantly outperforms an AaF strategy based on equal-power multiple-relay transmissions with local channel knowledge. These findings reveal that cooperation offers diversity benefits even when cooperative relays choose not to transmit but rather choose to cooperatively listen; they act as passive relays and give priority to the transmission of a single opportunistic relay. Numerical and simulation results are presented to verify our analysis.

We study the diversity-multiplexing tradeoff in cooperative diversity systems involving multiple relays. We focus on low complexity architectures that do not require simultaneous transmissions on the same frequency band and therefore are amenable to practical implementation with low-cost radios. We show that smart relay selection protocols achieve the same performance as previously proposed protocols that rely on multi-terminal space-time coding. Our study includes both analog and digital relays under a variety of relay selection criteria, and considers the availibility of decision feedback in the network. Our results present an alternative to distributed space-time codes for realizing the potential gains in multiple relay cooperative systems and open new avenues for fruitful interaction between routing and cooperative diversity.

In this paper, we present single-selection-opportunistic-relaying with decode-and-forward (DaF) and amplify-and-forward (AaF) protocols under an aggregate power constraint. We show that opportunistic DaF relaying is equivalent to the outage bound of the optimal DaF strategy using all potential relays. We further show that opportunistic AaF relaying is outage-optimal with single-relay selection and significantly outperforms an AaF strategy with multiple-relay (MR) transmissions, in the presence of limited channel knowledge. These findings reveal that cooperative diversity benefits (under an aggregate power constraint) are useful even when cooperative relays choose not to transmit but rather choose to cooperatively listen; they act as passive relays and give priority to the transmission of a single opportunistic relay

Cooperative diversity has been recently proposed as a way to form virtual antenna arrays that provide dramatic gains in slow fading wireless environments. However, most of the proposed solutions require distributed space-time coding algorithms, the careful design of which is left for future investigation if there is more than one cooperative relay. We propose a novel scheme that alleviates these problems and provides diversity gains on the order of the number of relays in the network. Our scheme first selects the best relay from a set of M available relays and then uses this "best" relay for cooperation between the source and the destination. We develop and analyze a distributed method to select the best relay that requires no topology information and is based on local measurements of the instantaneous channel conditions. This method also requires no explicit communication among the relays. The success (or failure) to select the best available path depends on the statistics of the wireless channel, and a methodology to evaluate performance for any kind of wireless channel statistics, is provided. Information theoretic analysis of outage probability shows that our scheme achieves the same diversity-multiplexing tradeoff as achieved by more complex protocols, where coordination and distributed space-time coding for M relay nodes is required, such as those proposed by Laneman and Wornell (2003). The simplicity of the technique allows for immediate implementation in existing radio hardware and its adoption could provide for improved flexibility, reliability, and efficiency in future 4G wireless systems.

Time information is critical for a variety of applications in distributed environments that facilitate pervasive computing and communication. This paper describes and evaluates a novel Kalman filtering algorithm for end-to-end time synchronization between a client computer and a server of "true" time (e.g. a GPS source) using messages transmitted over packet switched networks, such as the Internet. The messages exchanged have the NTP format and the algorithm evaluated, is performed only at the client side. The Kalman filtering algorithm is compared to two other techniques widely used, based on linear programming and statistical averaging and the experiments involve independent consecutive measurements (gaussian case) or measurements exhibiting long-range dependence (self-similar case). Performance is evaluated according to the estimation error of frequency offset and time offset between client and server clock, the standard deviation of the estimates and the number of packets used for a specific estimation. The algorithms can exploit existing NTP infrastructure and a specific example is presented.