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 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.

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.

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.