Rainbow-Link is a recently proposed millimeter-wave multiple access protocol tailored for IoT networks, utilizing a single-RF-chain base station and a true-time-delay (TTD) antenna array to generate a rainbow beam capable of simultaneously serving spatially distributed devices via frequency-domain multiple access. While prior work established the feasibility of this approach, it assumed perfect beam training had been performed in advance and left the initial access stage unaddressed. In this paper, we tackle this open challenge by leveraging the aliasing effect inherent in narrowband IoT receivers. We propose a wideband transmitted pilot structure and a corresponding receiver-side algorithm to enable efficient one-shot initial beam training. Simulation results show that the proposed pilot structure together with the algorithm can achieve sub-millisecond-level latency with reliable beam training accuracy.

Reconfigurable intelligent surfaces (RISs) are a promising candidate to extend coverage in a cellular network, or to mitigate eavesdropping in a physical layer security system. Their ability to improve propagation boasts many benefits from augmenting multiple input multiple output (MIMO) systems to interference suppression. These benefits cannot be fully realized if the RIS's hardware degrades over time. Because of a RIS's inexpensive design, they are susceptible to random component degradation affecting all phase shifters, ultimately reducing the array's performance. This necessitates a calibration method that can estimate all phase and amplitude states and produce beamformers that account for these deviations. We develop a calibration method that can meet these needs by adapting to imperfect hardware while achieving higher beamforming gain than an uncalibrated system. This can be done with fewer measurements and at a lower measurement signal to noise ratio (SNR) than other naive calibration techniques. We propose an estimation method and beamforming method to address these random phase and amplitude impairments across all phase shifters. The calibration method uses a base station (BS) to take measurements by designing beamformers that point towards a calibration source (CS). These measurements are used to perform least squares estimation. A beamforming algorithm then designs quality directional beamformers, using the estimated discrete weights alphabet, that have a small least squares distance from an SNR maximizing continuous beamformer.

In this paper we consider a wideband millimeterwave and terahertz (THz) beamforming to support simultaneous data communication with multiple spatially separated users. The beamforming objective is to orthogonalize users across subbands by deploying frequency-dependent beams with a subband-specific spatial response. Analog True-Time-Delay (TTD) antenna arrays are a promising architecture for generating subband beams using a single radio frequency RF chain. An analog TTD array can realize subband beams however the beams cannot achieve flat gain response in each subband and uniform gain distribution across subbands, limiting their utility in practical deployments. Hybrid TTD arrays offer additional degrees of freedom to improve the beamforming design. This work proposes the optimization of the Hybrid TTD codebooks to achieve the desired sub-band beams. We propose an alternating minimization algorithm and a low-complexity heuristic solution for subband beam design. Simulation results show beamforming performance improvement as a function of RF chains, and demonstrate that scaling RF chains proportional to half of the number of users is sufficient to achieve near-digital beamforming performance.

Recently, a new multiple access protocol, called Rainbow-Link, has been proposed as a promising candidate to support various Internet of Things (IoT) networks. In Rainbow-Link, the base station (BS) is equipped with an analog true-time-delay (TTD) array that can create frequency-dependent rainbow beams in the millimeter-wave (mmW) band. The rainbow beams enable fast beam training and can support a large number of IoT devices at the same time thus reducing the latency in channel access. However, link establishment through initial access (IA) procedure using rainbow beams is not straightforward due to asymmetry in radio front-end and processing capabilities between IoT base station and terminals. Also, the superposition of multiple delayed signals introduced by the TTD array needs to be handled carefully in the synchronization process. In this paper, we propose a low latency Rainbow-Link IA procedure based on the new rainbow beam design suitable for narrowband IoT processing. We design joint beam training and timing synchronization algorithms for narrowband IoT transceivers that are robust to severe multipath delay spread introduced by the TTD array. We perform a comprehensive performance analysis under different system parameters including the number of antennas, the number of subcarriers, and the bandwidth ratio between BS and IoT terminals. Simulation results show that the proposed Rainbow-Link IA procedure can achieve millisecond-level latency with highly reliable synchronization accuracy.

This paper introduces a novel method for decoding uplink messages in Internet of Things (IoT) networks that utilize packet repetition, such as Sigfox and LoRa. These protocols use packet repetition at different pseudo-random frequency hops to avoid collisions and channel fades, but they do not coherently combine signals across these random (a priori unknown) hops. This paper introduces a novel frequency alignment strategy that leverages the frequency-hopped repetition structure to decode signals at lower transmission power levels without requiring channel state information, providing a significant advantage over techniques that rely on channel estimates. The proposed approach is validated through extensive simulations and real-world experiments examining its effectiveness in both single-user and multi-user environments under conditions of high system load and interference. The results demonstrate that the proposed approach not only enhances the performance and reliability of IoT networks but also contributes to more energy-efficient communication for IoT Low Power Wide Area Networks (LPWANs), thus offering a substantial advancement in dependable IoT communications.

True-time-delay (TTD) arrays can implement frequency-dependent rainbow beams and enable fast beam alignment in wideband millimeter-wave (mmWave) systems. In this paper, we consider 3D rainbow beam training with planar TTD arrays. We first design a planar TTD-array-based codebook that can realize a partial or complete scan of the entire 3D sphere using a single Orthogonal Frequency Division Multiplexing (OFDM) symbol. We demonstrate that a rainbow-beam codebook with a proper configuration of delays and phase shifts, and a sufficient number of subcarriers, can simultaneously probe all angular directions in the desired coverage sector with the required beamforming gain. Leveraging this full coverage codebook, we propose two frequency-domain power-based 3D beam training algorithms for fast estimation of the azimuth and elevation angles corresponding to the dominant propagation direction. The proposed algorithms take into account practical hardware-imposed delay range constraints. We numerically evaluate the performance of the proposed algorithms in mmWave channels as a function of the TTD codebook and system parameters. We show that the proposed 3D beam training offers considerable overhead reduction relative to the state-of-the-art methods with planar phased antenna arrays (PAA).

Millimeter-wave (mmWave) systems require a large number of antennas, which makes the beam training challenging and time-consuming for conventional phased arrays. Recently, a true-time-delay (TTD) array-based beam training algorithm has been shown as an effective solution to overcome the training overhead in large arrays. In this paper, we present a custom-built over-the-air (OTA) testbed to study the effects of hardware impairments on the TTD-based beam training and verify its feasibility in a real system. We proposed an orthogonal matching pursuit (OMP) based reconstruction algorithm along with a phase calibration dictionary to combat nonidealities such as strong frequency selectivity and phase misalignment in the received raw IQ signal. Post-processing results showed that with the nonideality effects properly handled, the 3D TTD beam training algorithm can achieve high AOA estimation accuracy.

In this work, we examine ultra-narrowband IoT networks in the case of basestation (BS) over-deployment, where we necessitate smartly turning on/off BSs for an energy-efficient network. We consider a dynamic environment and varying network traffic, where the optimal choice of BSs changes frequently. Therefore, we want to often re-evaluate if BSs should be on/off. Addressing this problem, we find a low time-complexity algorithm minimizing the number of active BSs while maintaining a high network coverage, measured by the network's packet decoding probability (PDP). The proposed strategy is based on solving an integer convex optimization problem maximizing an approximate term for PDP using collected signal-to-interference-and-noise ratio (SINR) values and constrains the number of BSs remaining on. Therefore, the comprehensive proposed algorithm includes our development of a model to estimate how many BSs should minimally be on to satisfy our high PDP value, denoted the “coverage constraint.” Our algorithm has a complexity scaling linearly with the BSs on, O(B), far better than the exhaustive search complexity to find the optimal choice of BSs, O(2B), and the greedy algorithm complexity used as an on/off strategy in the static case of cell-free MIMO wireless networks, which has O(B3). The proposed strategy's low complexity not only aids in the dynamic case-our proposed algorithm performs closer to the optimal when the number of available BSs is high, which is also when greedy and exhaustive methods become infeasible due to their complexity. As a result, our algorithm is also advantageous specifically for excessive over-deployment of BSs.

Wireless localization and sensing are increasingly important capabilities when the networks are evolving towards the $6^{t h}$ generation era. While the physics-inspired geometrical models are known to perform well in line-of-sight (LoS) dominant scenarios, harnessing the power of artificial intelligence (AI) to improve robustness, efficiency, and performance in more complex propagation scenarios is an intriguing prospect. To this end, the hypernetwork (HN) is an emerging neural network (NN) architecture, where one model is used to parameterize the weights of the other, promising dynamic weight adaptation among other performance improvements. In this work, we propose the concept of Hypernetwork Localization (HypLoc) - a hybrid HN-based architecture for localization in beamforming millimeter-wave (mmWave) networks, while combining angle-of-arrival (AoA), time-of-flight (ToF), and received power (RP) as representative measurements. Considering a realistic urban vehicular environment, we first demonstrate the baseline effectiveness of HypLoc with a fixed and known gNodeB (gNB) deployment scenario. We then also study a scenario where the factory pre-training covers multiple different gNB deployment constellations and show that the proposed HypLoc clearly outperforms the traditional NNs. Finally, we also show that the HypLoc adapts faster and requires less training data when adapting to a previously unseen deployment scenario. Overall, the proposed approach facilitates efficient factory pre-training when operating under multiple different gNB deployment options.

Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.

Hybrid arrays enable millimeter-wave (mmW) base stations and users to steer multiple beams simultaneously. However, in dense mmW networks with small inter-site distances and many users, a large number of serving beams can lead to significant inter- and intra-cell interference that prevents data-hungry users from satisfying their rate requirements. In this work, we address this problem by designing a linear multi-step optimization framework for user association and beam scheduling with interference management. In the first step, the framework aims to maximize the number of users with fully satisfied rate requirements by scheduling a minimal number of non-interfering beams. In the second step, any remaining non-interfering beams are distributed among other users to maximize the number of them with at least partially satisfied requirements. Hybrid precoders and combiners are then designed to remove any excess sidelobe interference among the scheduled beams. Finally, power allocation is optimized on a network level to boost the data rates of the partially satisfied users. Given that the framework includes NP-hard optimization problems, we propose an algorithm that attains a sub-optimal solution in polynomial-time. The proposed framework is numerically evaluated in realistic mmW channels and the results reveal its advantages over the baseline user association schemes.

Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users. It is desirable to orthogonalize users across sub-bands by deploying frequency-dependent beams with a sub-band-specific spatial response. True-Time-Delay (TTD) antenna arrays are a promising wide band architecture to implement sub-band-specific dispersion of beams across space using a single radio frequency (RF) chain. This paper proposes a structured design of analog TTD codebooks to generate beams that exhibit quantized sub-band-to-angle mapping. We introduce a structured Staircase TTD codebook and analyze the frequency-spatial behaviour of the resulting beam patterns. We develop the closed-form two-stage design of the proposed codebook to achieve the desired sub-band-specific beams and evaluate their performance in multi-user communication networks.

When conventional phase shifter based arrays are used in millimeter-wave systems, the angle of departure (AoD) and angle of arrival (AoA) estimates are obtained through high-overhead exhaustive beam sweeping (EBS). Recently, true-time-delay arrays re-emerged as a promising architecture for fast angle estimation. In this work, we develop an algorithm for joint AoD and AoA estimation using only one Orthogonal Frequency Division Multiplexing (OFDM) symbol and frequency-dependent beams that can be synthesized by fully digital and true-time-delay arrays. We compare the developed algorithm with wideband single-carrier based EBS in terms of the misalignment probability and required training overhead. Numerical simulations in millimeter-wave channels reveal the advantages of the proposed algorithm.

In this paper, we introduce a runtime-efficient radio frequency (RF) map prediction method based on UNet convolutional neural networks (CNNs), trained on a large-scale 3D maps dataset. The proposed method calculates the line-of-sight maps and feeds them as input for the UNet CNN. A special Kullback–Leibler divergence loss function is adopted, enabling the proposed method to minimize both error’s mean and variance. The performance of our model is evaluated in the context of the 2023 IEEE ICASSP Signal Processing Grand Challenge, namely, the First Pathloss Radio Map Prediction Challenge. The evaluation results demonstrate that the proposed method achieves an average normalized root-mean-square error (RMSE) of 0.045 with an average of 14 milliseconds (ms) runtime.

This work revisits the classic robust adaptive beamforming that is widely adopted for interference suppression. A first-order method is proposed to solve the beamformers for large arrays. The method uses proximal gradient descent along with Nesterov’s acceleration. It has ${\mathcal{O}}\left( {{N^2}} \right)$ computational complexity per iteration where N is the array size. For sparse linearly constrained adaptive beamforming, the proposed method achieves performances comparable to the conjugate gradient method. For sparse robust adaptive beamforming with conic constraints, the proposed method is much more efficient than the standard interior point solver.

In millimeter-wave (mmW) networks, beam training enables the base station and user to estimate the dominant angle of departure (AoD) and angle of arrival (AoA) and align their directional beams. With conventional phased arrays, the training requires an exhaustive beam sweeping (EBS), which imposes a large overhead. Alternative array architectures, including digital and true-time-delay arrays, enable simultaneous probing of different angular directions using different frequency components of the signal, which can speed up the training. In this work, we propose a beam training procedure based on frequency-dependent angle probing that uses a single Orthogonal Frequency-Division Multiplexing (OFDM) symbol to jointly estimate the AoD and AoA. We describe the design of the required training codebooks and frequency-domain power-based algorithm. Further, assuming an error-free array at the base station, we analyze how practical hardware impairments in the user's array affect the receive beamforming gain and misalignment probability in beam training. The proposed beam training is evaluated in realistic mmW channels and compared with existing beam training approaches in terms of the misalignment probability, angle estimation accuracy, required overhead, and computational complexity. The results indicate that the proposed algorithm requires a single OFDM symbol and low complexity to achieve a performance comparable to that of the EBS with arrays based on phase shifters.

Spatia1 prediction of the radio propagation environment (henceforth ‘radio environment’ for brevity) of a transmitter can assist and improve various aspects of wireless networks. The majority of research in this domain can be categorized as ‘reactive’ spatial prediction, where the predictions are made based on a small set of measurements from an active transmitter whose radio environment is to be predicted. Emerging spectrum-sharing paradigms would benefit from ‘proactive’ spatial prediction of the radio environment, where the spatial predictions must be done for a transmitter for which no measurement has been collected. This paper proposes a novel, supervised deep learning-based framework, ProSpire, that enables spectrum sharing by leveraging the idea of proactive spatial prediction. We carefully address several challenges in ProSpire, such as designing a framework that conveniently collects training data for learning, performing the predictions in a fast manner, enabling operations without an area map, and ensuring that the predictions do not lead to undesired interference. ProSpire relies on the crowdsourcing of transmitters and receivers during their normal operations to address some of the aforementioned challenges. The core component of ProSpire is a deep learning-based image-to-image translation method, which we call RSSu-net. We generate several diverse datasets using ray tracing software and numerically evaluate ProSpire. Our evaluations show that RSSu-net performs reasonably well in terms of signal strength prediction, $\approx$ 5dB mean absolute error, which is comparable to the average error of other relevant methods. Importantly, due to the merits of RSSu-net, ProSpire creates proactive boundaries around transmitters such that they can be activated with $\approx$ 97% probability of not causing interference. In this regard, the performance of RSSu-net is 19% better than that of other comparable methods.

Distributed transmit beamforming enables cooperative radios to act as one virtual antenna array, extending their communications’ range beyond the capabilities of a single radio. Most existing distributed beamforming approaches rely on the destination radio sending feedback to adjust the transmitters’ signals for coherent combining. However, relying on the destination radio's feedback limits the communications range to that of a single radio. Existing destination-feedback-free approaches rely on phase synchronization and knowing the node locations with sub-wavelength accuracy, which becomes impractical for radios mounted on high-mobility platforms like UAVs. In this article, we propose and demonstrate a destination-feedback-free distributed beamforming approach that leverages the radio's mobility and coarse location information in a dominant line-of-sight channel. In the proposed approach, one radio acts as a guide and moves to point the beam of the remaining radios towards the destination. We specify the radios’ position requirements and verify their relation to the combined signal at the destination using simulations. A proof of concept demo was implemented using software defined radios, showing up to 9 dB SNR improvement in the beamforming direction just by relying on the coarse placement of four radios.

Cooperating radios can extend their communication range by adjusting their signals to ensure coherent combining at a destination radio. This technique is called distributed transmit beamforming. Beamforming (BF) relies on the BF radios having frequency synchronized carriers and phases adjusted for coherent combining. Both requirements are typically met by exchanging preambles with the destination. However, since BF aims to increase the received power, the individually transmitted preambles are typically at low SNR and their lengths are constrained by the channel coherence time. These noisy preambles lead to errors in frequency and phase estimation, which result in randomly changing BF gains. To build reliable distributed BF systems, the impact of estimation errors on the BF gains need to be considered in the design. In this work, assuming a destination-led BF protocol and Kalman filter for frequency tracking, we optimize the number of BF radios and the preamble lengths to achieve reliable BF gain. To do that, we characterize the relations between the BF gains distribution, the channel coherence time, and design parameters like the SNR, preamble lengths, and the number of radios. The proposed relations are verified using simulations and via experiments using software-defined radios in a lab and on UAVs.

Sub-terahertz (sub-THz) antenna array architectures significantly impact power usage and communications capacity in multi-user multiple-input multiple-output (MU-MIMO) systems. In this work, we compare the energy efficiency and spectral efficiency of three MU-MIMO capable array architectures for base station receivers. We provide a sub-THz circuits power analysis, based on our review of state-of-the-art D-band and G-band components, and compare communications capabilities through wideband simulations. Our analysis reveals that digital arrays can provide the highest spectral efficiency and energy efficiency, due to the high power consumption of sub-THz active phase shifters or when SNR and system spectral efficiency requirements are high.

Baseband adaptive array processing is a traditional approach to mitigating interference at a digital receiver. In the presence of sufficiently strong interference, this mitigation fails if receiver (RX) front-end components, such as amplifiers and ADCs, are driven into non-linear operation or if the RX responds by decreasing its gain and losing sensitivity to the signal. Instead of requiring costly RX components with high dynamic-range, we explore a novel Multiple-Input Multiple-Output (MIMO) scheme that uses hybrid analog-digital beamforming to mitigate a strong interferer in wideband line-of-sight (LOS) channels. This scheme uses true-time delay analog beamforming within antenna subarrays to mitigate most of the interference ahead of the RX front-end and then linear spatial equalization to mitigate residual interference at baseband. Alamouti space-frequency coding at the transmitter and MIMO receive processing are used to exploit the frequency-selective channel imposed by the analog beamforming stage. We show that this architecture can outperform traditional Single-Input Multiple-Output (SIMO) schemes under strong interference using low complexity processing in a relatively low-complexity hardware implementation. The specific case of inter-vehicular (V2V) LOS channels is considered in detail.

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose the dual path network (DPN). It consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters. By interconnecting the paths over several recovery stages, later stages benefit from the recovered signals and reuse all the previously extracted features. The proposed design is demonstrated to provide 5% improvement in modulation classification compared to alternative designs lacking either feature sharing or access to recovered signals. The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset. An over-the-air software-defined-radio capture was used to verify DPN results at high SNRs. DPN design can process variable length inputs and is shown to outperform relying on fixed length inputs with prediction averaging on longer signals by up to 15% in modulation classification.

The millimeter-wave (mmW) communications is a key enabling technology in 5G to provide ultra-high throughput. Current mmW technologies rely on analog phased arrays to realize beamforming gain and overcome high path loss. However, due to a limited number of simultaneous beams that can be created with analog/hybrid phased antenna arrays, the overheads of beam training and beam scheduling become a bottleneck for emerging networks that need to support a large number of users and low latency applications. This paper introduces rainbow-link, a novel multiple access protocol, that can achieve low latency and massive connectivity by exploiting wide bandwidth at mmW frequencies and novel analog true-time-delay array architecture with frequency dependent beamforming capability. In the proposed design, the network infrastructure is equipped with the true-time-delay array to simultaneously steer different frequency resource blocks towards distinct directions covering the entire cell sector. Users or devices, equipped with a narrowband receiver and either a single antenna or small phased antenna array, connect to the network based on their angular positions by selecting frequency resources within their rainbow beam allocation. Rainbow-link is combined with a contention-based grant-free access to eliminate the explicit beam training and user scheduling. The proposed design and analysis show that rainbow-link grant-free access is a potential candidate for latency-critical use cases within massive connectivity. Our results show that, given less than 10−5probability of packet loss, a rainbow-link cell, over 1 GHz bandwidth using 64 element antenna array, attains sub-millisecond user-plane latency and Mbps user rates with an approximate 400m line-of-sight coverage and a density of up to 5 active single antenna users per second per m2.

We consider a novel ultra-narrowband (UNB) low-power wide-area network (LPWAN) architecture design for uplink transmission of a massive number of Internet of Things (IoT) devices over multiple multiplexing bands. An IoT device can randomly choose any of the multiplexing bands to transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. Our main objective is to maximize the packet decoding probability (PDP) by optimizing the placement of the BSs and frequency assignment of BSs to multiplexing bands. We develop two online approaches that adapt to the environment based on the statistics of (un)successful packets at the BSs. The first approach is based on a predefined model of the environment, while the second approach is measurement-based model-free approach, which is applicable to any environment. The benefit of the model-based approach is a lower training complexity, at the risk of a poor fit in a model-incompatible environment. The simulation results show that our proposed approaches to band assignment and BS placement offer significant improvement in PDP over baseline random approaches and perform closely to the theoretical upper bound.

Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies are of increasing interest for future high-data rate networks. One critical challenge faced by phased array systems at these high frequencies is the efficiency of the initial beam alignment, typically using only phase-less power measurements due to high frequency oscillator phase noise. Traditional methods for beam alignment require exhaustive sweeps of all possible beam directions, thus scale communications overhead linearly with antenna array size. For better scaling with the large arrays required at high mmW bands, compressive sensing methods have been proposed as their overhead scales logarithmically with the array size. However, algorithms utilizing machine learning have shown more efficient and more accurate alignment when using real hardware due to array impairments. Additionally, few existing phase-less beam alignment algorithms have been tested over varied secondary path strength in multipath channels. In this work, we introduce a novel, machine learning based algorithm for beam alignment in multipath environments using only phase-less received power measurements. We consider the impacts of phased array sounding beam design and machine learning architectures on beam alignment performance and validate our findings experimentally using 60 GHz radios with 36-element phased arrays. Using experimental data in multipath channels, our proposed algorithm demonstrates an 88% reduction in beam alignment overhead compared to an exhaustive search and at least a 62% reduction in overhead compared to existing compressive methods.

RF devices can be identified by unique imperfections embedded in the signals they transmit called RF fingerprints. The closed set classification of such devices, where the identification must be made among an authorized set of transmitters, has been well explored. However, the much more difficult problem of open set classification, where the classifier needs to reject unauthorized transmitters while recognizing authorized transmitters, has only been recently visited. So far, efforts at open set classification have largely relied on the utilization of signal samples captured from a known set of unauthorized transmitters to aid the classifier learn unauthorized transmitter fingerprints. Since acquiring new transmitters to use as known transmitters is highly expensive, we propose to use generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We develop two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Experiments conducted on a dataset captured from a WiFi testbed indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small.

Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators have increasingly been proposed to classify devices using these fingerprints, as they achieve higher accuracies compared to traditional approaches. However, it has been shown in domains such as computer vision that adding carefully crafted perturbations to legitimate inputs can fool such classifiers. This can undermine the security provided by the authenticator. Unlike adversarial attacks applied in other domains, an adversary has no control over the propagation environment. Therefore, to investigate the severity of this type of attack in wireless communications, we consider an unauthorized transmitter attempting to have its signals classified as authorized by a deep learning-based authenticator. We demonstrate a reinforcement learning-based attack where the impersonator—using only the authenticator’s binary authentication decision—distorts its signals in order to penetrate the system. Extensive simulations and experiments on a software-defined radio testbed indicate that at appropriate channel conditions and bounded by a maximum distortion level, it is possible to fool the authenticator reliably at a success rate of more than 90%.

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networks (DNNs) to predict the radio signal strength field in an urban environment. Our algorithm relies on samples of signal strength collected across the prediction space and a 3D map of the environment, which enables it to predict the scattering of radio waves through the environment. While already extensive body of research exists in spatial signal strength prediction, our approach differs from most existing approaches in that it does not require the knowledge of the transmitter location, it does not require side channel information such as attenuation and shadowing parameters, and it is the first work, to the best of our knowledge, to use 3D maps to accomplish the task of signal strength prediction.

A swarm of cooperating UAVs communicating with a distant multiantenna ground station can leverage MIMO spatial multiplexing to scale the capacity. Due to the line-of-sight propagation between the swarm and the ground station, the MIMO channel is highly correlated, leading to limited multiplexing gains. In this paper, we optimize the UAV positions to attain the maximum MIMO capacity given by the single user bound. An infinite set of UAV placements that attains the capacity bound is first derived. Given an initial swarm placement, we formulate the problem of minimizing the distance traveled by the UAVs to reach a placement within the capacity maximizing set of positions. An offline centralized solution to the problem using block coordinate descent is developed assuming known initial positions of UAVs. We also propose an online distributed algorithm, where the UAVs iteratively adjust their positions to maximize the capacity. Our proposed approaches are shown to significantly increase the capacity at the expense of a bounded translation from the initial UAV placements. This capacity increase persists when using a massive MIMO ground station. Using numerical simulations, we show the robustness of our approaches in a Rician channel under UAV motion disturbances.

Ultra-narrowband (UNB) communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices within a given band. It also improves robustness to interference, extending the coverage of the network. In this article, we study the coexistence capability of UNB networks and their scalability to enable massive access. To this end, we develop a stochastic geometry framework to analyze and model UNB networks on a large scale. The framework captures the unique characteristics of UNB communications, including the asynchronous time-frequency access, signal repetition, and the absence of base station (BS) association. Closed-form expressions of the transmission success probability and network connection density are presented for several UNB protocols. We further discuss multiband access for UNB networks, proposing a low-complexity protocol. Our analysis reveals several insights on the geographical diversity achieved when devices do not connect to a single BS, the optimal number of signal repetitions, and how to utilize multiple bands without increasing the complexity of BSs. Simulation results are provided to validate the analysis, and they show that UNB communications enables a single BS to connect thousands of devices even when the spectrum is shared with other networks.

The best beam steering directions are estimated through beam training, which is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel array architectures and signal processing techniques are required to avoid prohibitive beam training overhead associated with large antenna arrays and narrow beams. In this work, we leverage recent developments in true-time-delay (TTD) arrays with large delay-bandwidth products to accelerate beam training using frequency-dependent probing beams. We propose and study two TTD architecture candidates, including analog and hybrid analog-digital arrays, that can facilitate beam training with only one wideband pilot. We also propose a suitable algorithm that requires a single pilot to achieve high-accuracy estimation of angle of arrival. The proposed array architectures are compared in terms of beam training requirements and performance, robustness to practical hardware impairments, and power consumption. The findings suggest that the analog and hybrid TTD arrays achieve a sub-degree beam alignment precision with 66% and 25% lower power consumption than a fully digital array, respectively. Our results yield important design trade-offs among the basic system parameters, power consumption, and accuracy of angle of arrival estimation in fast TTD beam training.

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions like frequency and timing errors, and outperformed classical signal processing techniques with sufficient training. However, deep learning approaches typically require hundreds of thousands of floating points operations for inference, which is orders of magnitude higher than classical signal processing approaches and thus do not scale well for long sequences. Additionally, they typically operate as a black box and without insight on how their final output was obtained, they can't be integrated with existing approaches. In this paper, we propose a novel neural network architecture that combines deep learning with linear signal processing typically done at the receiver to realize joint modulation classification and symbol recovery. The proposed method estimates signal parameters by learning and corrects signal distortions like carrier frequency offset and multipath fading by linear processing. Using this hybrid approach, we leverage the power of deep learning while retaining the efficiency of conventional receiver processing techniques for long sequences. The proposed hybrid approach provides good accuracy in signal distortion estimation leading to promising results in terms of symbol error rate. For modulation classification accuracy, it outperforms many state of the art deep learning networks.

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. The hardware constraint is mainly due to the complexity of performing fast Fourier transform (FFT) at a very small sampling interval over the multiplexing bands in order to counter the uncertainty of IoT device frequency and synchronize onto transmissions. The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP). We develop a learning-based algorithm based on a sub-optimal solution to PDP maximization. The simulation results show that our approach to band assignment achieves near-optimal performance in terms of PDP, while at the same time, significantly exceeding the performance of random assignment. We also develop a heuristic algorithm with no learning overhead based on the locations of the BSs that also outperforms random assignment and serves as a performance reference to our learning-based algorithm.

In this work, we propose the use of hybrid offloading of computing tasks simultaneously to edge servers (vertical offloading) via LTE communication and to nearby cars (horizontal offloading) via V2V communication, in order to increase the rate at which tasks are processed compared to local processing. Our main contribution is an optimized resource assignment and scheduling framework for hybrid offloading of computing tasks. The framework optimally utilizes the computational resources in the edge and in the micro cloud, while taking into account communication constraints and task requirements. While cooperative perception is the primary use case of our framework, the framework is applicable to other cooperative vehicular applications with high computing demand and significant transmission overhead. The framework is tested in a simulated environment built on top of car traces and communication rates exported from the Veins vehicular networking simulator. We observe a significant increase in the processing rate of cooperative perception sensor frames when hybrid offloading with optimized resource assignment is adopted. Furthermore, the processing rate increases with V2V connectivity as more computing tasks can be offloaded horizontally.

Under line-of-sight (LOS) network conditions, multi-input multi-output (MIMO) wireless communications can increase the channel capacity between a team of robots and a multi-antenna array at a stationary base station. This increased capacity can result in greater data throughput, shortening the time necessary to complete channel-limited data aggregation tasks. To take advantage of this higher capacity channel, the robots in the team must be positioned to maximize complex channel orthogonality between each robot and receiver antenna. Using geometrically motivated assumptions, we derive transmitter spacing rules that can be easily be added on to existing path plans to improve backhaul throughput for data offloading from the robot team, with minimal impact on other system objectives. We demonstrate the effectiveness of the approach- both in ideal as well as realistic channels outside the domain of our simplifying assumptions-with numerical examples of robot-coordinated path plans in two example environments, achieving up to 42% improvement in task completion times.

Data aggregation has become an emerging paradigm to support massive Internet-of-things (IoT), a new and critical use case for fifth-generation new radio (5G-NR). Indeed, data aggregators can complement cellular base stations and process IoT traffic to reduce network congestion. In this paper, we consider using mobile data aggregators, e.g., drones, that collect IoT traffic and aggregate them to the network. Specifically, we first discuss how the spectrum can be shared between cellular users (UEs) and IoT devices in the presence of drones, proposing a time-division duplexing protocol. We use stochastic geometry to analyze this protocol, comparing it to the standard spectrum sharing and orthogonal allocation protocols. We then formulate a stochastic optimization problem to optimize the nominal IoT transmit power, maximizing the average energy-efficiency (EE) of the IoT device subject to interference constraints to protect UEs. Simulations are presented to validate the theoretical insights and the effectiveness of the proposed protocol. It is shown that using drones, to aggregate IoT traffic, improves the EE of IoT devices, yet the EE degrades as their altitudes increases. Equally important, optimizing the transmit power is critical to further improve the EE, while ensuring fair coexistence with UEs.

In this paper, an agile smart attacker model in spectrum sensing of cognitive radio network (CRN) is introduced. This smart attacker does not make the channel busy all the time, instead it senses spectrum and when a primary user leaves, it occupies the spectrum by mimicking the signal characteristics of the primary users. To model such a smart attacker, we use a dependent Markov chain to model the primary user and primary user emulation attacker activities, simultaneously. We derive the transition probabilities for the assumed dependent Markov model. Then, the effect of the primary user and attacker traffic on a secondary user's throughput under Rayleigh flat fading channel is investigated and closed form expressions are derived for the average probability of detection and false alarm. Furthermore, the impact of this attacker on the performance of a CRN and the throughput of the secondary user network is studied analytically. We also derive a test rule based on the generalized likelihood ratio test, to detect the legitimate user from smart illegitimate user. In addition, launching primary user emulation attacker in the conventional and proposed smart attacking procedures are compared, where the results demonstrate that by using smart attacking, the deterioration in throughput of the secondary user's network is considerable. In fact, it is shown that under proper selection of the traffic parameters, the secondary user's network throughput may tend toward zero. Finally, the accuracy of the obtained results are analyzed and verified by the simulation results.

Cooperative communication using unmanned aerial vehicles (UAVs) is a promising technology for infrastructureless wireless networks. One of the key challenges in UAV based communications is the backhaul throughput. In this paper, we propose optimization of the UAV swarm positions to achieve a high mulitplexing gain in line-of-sight (LoS) MIMO back-haul. We develop two distributed algorithms to position the UAVs such that each UAV moves a minimal distance to realize the highest capacity LoS MIMO channel. The first approach uses iterative gradient descent (GD) and the second uses iterative brute force (BF). Simulations show that both algorithms can achieve up to 6 times higher capacity compared to the approach relying on random UAV placement, earlier proposed in the literature. BF has the advantage of not requiring any location information, while GD is less sensitive to errors in motion.

In this paper, we propose a dynamic spectrum sensing-based architecture to provide connectivity for a massive number of Internet-of-things (IoT) objects over the unlicensed spectrum. Specifically, the architecture relies on deploying sensing access points (SAPs), e.g., small cells with sensing capabilities, that aim to (i) identify a large number of narrowband channels in a wideband spectrum, as many massive IoT applications have low-rate requirements, and (ii) aggressively reuse the unlicensed channels at the SAPs' locations as IoT devices typically transmit at low power, occupying a small spatial footprint. Instead of enforcing each SAP to sense the entire spectrum, we develop a sensing assignment scheduler that ensures each one senses a subset of the spectrum. We then develop a distributed spatio-spectral cooperative sensing algorithm that enables each SAP to have local information about the occupancy of the entire spectrum. We present numerical simulations to validate the effectiveness of the proposed system in the presence of WiFi access points (APs). It is shown that the proposed system outperforms non-cooperative and centralized schemes in terms of reliably identifying more available spatio-spectral blocks with a lower misdetection of transmitting WiFi APs.

Data aggregation is an effective solution to enable cellular support of Internet-of-things (IoT) communications. Indeed, it helps alleviate channel congestion, reduce the communication range, and extend battery-lifetime. In this paper, we use stochastic geometry to analyze the performance of uplink cellular IoT using different deployment strategies of aggregators, including terrestrial and aerial ones, e.g., drones or unmanned aerial vehicles. We focus on IoT-specific performance metrics, that are typically used by 3GPP. Specifically, we derive closed-form expressions of the average transmit power consumption, which is key to determine the lifetime of IoT devices, as well as the maximum coupling loss, which is essential to determine the maximum coverage the cellular system can support. Simulation results are presented to validate the derived theoretical expressions. It is shown that aerial aggregators can significantly extend the device lifetime and provide superior coverage compared to other deployment strategies. In addition, random deployment performs well when aggregators are densely deployed, whereas optimizing the location of a single terrestrial aggregator is beneficial when devices are more clustered.

We formulate two statistical optimization frameworks for multi-antenna heterogeneous networks (HetNets). The first one maximizes coverage, via user association, and the second one maximizes a rate utility function by jointly optimizing user association and the spectrum allocated to each tier. Since both performance metrics depend on the coverage probability, we first derive the exact analytical coverage expression for a frequency-partitioned multi-antenna HetNet. We then present a simple closed-form approximation using the Gil-Pelaez inversion theorem, making both optimization frameworks concave in the optimizing variables. We show that to maximize coverage, the user should connect to the tier with the highest ratio of the array gain to the multiplexing gain, highlighting that max-power association becomes far from optimal in terms of coverage. To maximize the rate utility, we show that the optimal resource allocation of a tier is equal to the portion of users connected to that tier. Furthermore, maximizing the rate utility breaks down into maximizing two objectives: coverage and the number of users to be served in a resource block. Several theoretical insights are presented and further validated via simulations, showing that both frameworks provide considerable coverage and rate gains compared to the standard max-power and small-cell range expansion schemes.

Millimeter-wave (mmWave) systems require massive antennas at both transmitter and receiver to reach desirable link budget. Analog-digital hybrid beamforming is a promising architecture since it significantly reduces the hardware cost while approaches the performance of digital beamforming. However, the nonlinear power amplifier (PA) introduces intermodulation interference and degrades the spectral efficiency. In this work, we use memory polynomial model for modelling mmWave PA and study the inter-beam and inter-symbol interference in hybrid precoding architecture. We also propose a digital predistortion (DPD) algorithm to mitigate interference. The proposed DPD reduces interference power by an order of magnitude and compared with system without predistortion, the proposed system can achieve up to 60% spectral efficiency.

With the advent of cognitive radio (CR) and dynamic spectrum access techniques, where multiple signals may coexist within the same frequency band, multiuser modulation classification problem becomes a vital issue, which has not been sufficiently investigated. In this paper, we consider a cooperative multiuser modulation classification problem, in the presence of unknown multipath channels. A likelihood-based (LB) classifier using the expectation-maximization (EM) algorithm is proposed, which enables to find the maximum likelihood estimates (MLEs) iteratively. Numerical results show that the proposed algorithm achieves significant improvement on the classification performance with a small number of samples when compared to the conventional methods, which demonstrates its reliability and efficiency of identifying modulations of multiple users under the multipath scenarios.

Wireless devices are ubiquitous nowadays and, since most of them use the same unlicensed frequency bands, the high number of packet losses due to interference and collisions degrade performance. Reliability, energy consumption, and latency are key challenges for future dense networks. Allowing the transmitter to take action, i.e., vacating the channel, as soon as a collision or interference is detected is crucial in improving these metrics. In-band full duplex radios enable the transmitter to simultaneously transmit packets and sense the spectrum for collisions and interference. This paper studies two important questions regarding transmitter-based collision and interference detection: (1) from an overall system perspective, does such detection outperform receiver-based detection and (2) which test statistic is the most accurate and sensitive at detecting collisions and interference. First, NS-3 simulations are used to show that transmitter-based detection reduces the energy consumption while improving the throughput in a typical star topology network. Next, we present a measurement-based study of four different techniques for transmitter-based collision and interference detection. In particular, we compare the energy detector with three goodness-of-fit tests in terms of probability of detection and false alarm. Our analysis shows that transmitter-based detection can detect between 80% to 100% of the collisions and interference occurring at the receiver, depending on the distance between the transmitter and the receiver. Of those detectable by the transmitter, our measurement results show that goodness-of-fit tests can detect nearly 100% of the collisions and have at least 10 dB better sensitivity as compared to the commonly proposed energy detection test. In general, the proposed techniques can detect interfering signals that are up to 25 dB below the remaining self-interference power.

Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

Due to the ubiquitous presence of cellular networks and their exemplary success, they have been pushed forward to provide connectivity for Internet-of-things (IoT) applications with mass deployments of sensors and machines. Nevertheless, existing transmission protocols, e.g., orthogonal allocation or spectrum sharing, can be detrimental for both existing cellular users and IoT devices due to increased congestion, interference, or resource splitting. To this end, we propose to complement cellular networks with authorized mobile data aggregators, e.g., drones, that collect data from IoT devices and aggregate them to the cellular network. The proposed cellular architecture is supported by a novel transmission protocol and load-aware power control. The former enables IoT devices to operate over the same channel with existing cellular users, increasing the available spectral resources, whereas the latter constrains the interference on these users. Simulation results are presented to illustrate the performance of the proposed architecture compared to orthogonal allocation and spectrum sharing. It is shown that the proposed architecture significantly improves the energy efficiency of IoT devices, with minimal degradation on the spectral efficiency of existing cellular users.

In this paper, we investigate the cooperative modulation classification problem under multipath scenarios with blind channel information. Multipath channels cause severe degradation on the modulation classification performance, which has not yet been thoroughly solved in the existing literature. To address this issue, a likelihood-based classifier using the expectation-maximization algorithm is proposed, which is capable of finding the maximum likelihood estimates of unknown parameters in a tractable way. Furthermore, to evaluate the upper bound performance of the proposed algorithm, the Cramér-Rao lower bounds of the joint estimates of unknown parameters are derived. Extensive simulations show that the classification performance of the proposed algorithm with good initialization scheme is close to the performance upper bound in the high signal-to-noise ratio region. The results also demonstrate that the proposed algorithm provides significant performance improvement in the multipath channels compared with conventional approaches.

We study the optimality of almost blank subframes (ABSs) that are used to limit interference by muting macro base stations (BSs) in multi-antenna heterogeneous networks (HetNets). In particular, we formulate a linear program to find the optimal blanking decision that maximizes the average rate of a typical user. A closed-form condition is derived, which depends on the multi-antenna transmission schemes, i.e., the number of antennas used and the number of users served simultaneously in one resource block, as well as network statistics such as the BSs' density in a tier and the coverage probability. This motivates an online implementation that uses instantaneous measurements of the number of BSs and users in the network. We prove that muting macro BSs is generally optimal in single-antenna HetNets but not in multi-antenna networks, where blanking must consider the transmission scheme. Simulation results validate the analysis and show that the throughput significantly decreases when a massive multiple-input-multiple-output (MIMO) macro BS is blanked. This renders the conventional ABSs inefficient to use for fifth-generation (5G) networks which are expected to utilize massive MIMO technology.

We study the joint optimization of resource allocation and user association in downlink multi-antenna heterogeneous networks. The resource allocation is done orthogonally in the spectrum while the user association is implemented using cell range extension. The objective is to maximize a user utility function that depends on the rate of the typical user. We resort to the Gil-Pelaez Inversion Theorem to approximate the coverage probability and present a concave formulation of the joint optimization problem. By interpreting the problem as a multi-criterion one, we propose a suboptimal user association policy. We show that the optimal resource allocation factor of each tier is equal to the optimal association probability, which can be efficiently computed using a standard convex optimization solver. Simulation results show significant (up to three times) rate gains when resource allocation is jointly optimized with user association in comparison with merely optimizing resource allocation with max-power user association.

In this work, we consider the wideband sensing problem of using a single antenna receiver for sensing an unknown number of incumbent signals occupying frequency bands that may be partially overlapping. Many communications standards today, such as the IEEE 802.11 and LTE-Advanced, use frequency bands that overlap amongst themselves or do not have a frequency guard band. Current sensing methods for a single antenna receiver sense for signal energy at individual frequencies and do not differentiate between signals. However, sensing each signal separately is necessary to make inferences about the higher layers of the incumbent communication system. Hence, in this work, signals are sensed as signal energy in a set of frequency bins instead of individual frequency bins. The number of signals and the frequency bands occupied by each are estimated. To achieve this, the temporal dimension is exploited by using periodic power measurements as input and the fact that radios transmit intermittently and not continually. Two algorithms are proposed. First, repeated non- negative matrix factorization is used to estimate the number of signals and the frequency support of each. Secondly, energy detection results on adaptively chosen frequency bins are used to detect the start and end of the frequency band occupied by each signal. Novel performance metrics are proposed since this is simultaneously a model size and parameter estimation problem. Systems with signals occupying adjacent partially overlapping frequency bands are simulated to evaluate the performance of both algorithms.

Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on, we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity.

In this paper we study the problem of identifying active subcarriers in an OFDM signal from compressive measurements sampled at sub-Nyquist rate. The problem is of importance in Cognitive Radio systems when secondary users (SUs) are looking for available spectrum opportunities to communicate over them while sensing at Nyquist rate sampling can be costly or even impractical in case of very wide bandwidth. We first study the effect of timing offset and derive the necessary and sufficient conditions for signal recovery in the oracle-assisted case when the true active sub-carriers are assumed known. Then we propose an Orthogonal Matching Pursuit (OMP)- based joint sparse recovery method for identifying active subcarriers when the timing offset is known. Finally we extend the problem to the case of unknown timing offset and develop a joint dictionary learning and sparse approximation algorithm, where in the dictionary learning phase the timing offset is estimated and in the sparse approximation phase active subcarriers are identified. The obtained results demonstrate that active subcarrier identification can be carried out reliably, by using the developed framework.

Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the underlying DoA estimator is of uttermost importance. In this paper, we therefore propose a novel high performance DoA estimator for sectorized antennas that does not require cooperation between the transmitter and the localizing network. The proposed DoA estimator is broadly applicable with different sectorized antenna types and signal waveforms, and has low computational complexity. Using computer simulations, we show that our algorithm approaches the respective Cramer-Rao lower bound for DoA estimation variance if the signal-to-noise ratio (SNR) is moderate to large and also outperforms the existing estimators. Moreover, we also derive analytical error models for the underlying DoA estimation principle considering both free space as well as multipath propagation scenarios. Furthermore, we also address the fusion of the individual DoA estimates into a location estimate using the Stansfield algorithm and study the corresponding localization performance in detail. Finally, we show how to implement the localization in practical systems and demonstrate the achievable performance using indoor RF measurements obtained with practical sectorized antenna units.

While cooperative spectrum sensing improves sensing reliability when secondary users (SUs) have independent measurements, these gains are limited in the presence of correlated or malicious SUs. In this paper, we propose algorithms to improve spectrum sensing performance when the system contains both honest and malicious SUs all of whom may be experiencing correlated fading channels. We show that, when the SUs' reports at each time slot are independent and identically distributed, it is impossible to distinguish between malicious collaboration, i.e., collusion, and correlations caused by the environment. Thus, the optimal test statistic for cooperative spectrum sensing depends on the correlations in the SU reports but not on the source of these correlations. We propose two algorithms: one to identify SUs whose reports are correlated and another to infer the spectrum occupancy by using the learned structure of correlations. Groups of SUs whose reports are correlated are identified by learning the structure of the underlying Bayesian network model. This structure is implemented as a factor graph to infer the spectrum occupancy using a loopy belief propagation algorithm. We derive an upper bound of the error probability of the proposed structure learning algorithm and prove convergence of our loopy belief propagation algorithm.

Dynamic spectrum access (DSA) for secondary networks improves the spectrum utilization by finding spectrum opportunities and exploiting them efficiently. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, thus increasing the transmission throughput. However, in a multi-channel system, the problem becomes more complex since a sensing schedule, indicating to each user the channel that it must sense at different sensing moments, must be created to optimize system performance. In this paper, we first propose a general sensing strategy to schedule the users according to network parameters. We propose three sensing strategies, and within each one of them several solutions striking a balance between throughput performance, memory usage, and computational complexity are proposed. In addition, we show that the proposed sequential sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, the parallel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel sensing strategy achieves the best performance in all scenarios at the cost of extra complexity.

We consider the problem of localizing spectrally overlapped sources in cognitive radio networks. A new weighted centroid localization algorithm (WCL) called Cyclic WCL is proposed, which exploits the cyclostationary feature of the target signal to estimate its location coordinates. In order to analyze the algorithm in terms of root-mean-square error (RMSE), we model the location estimates as the ratios of quadratic forms in a Gaussian random vector. With analysis and simulation, we show the impact of the interférer location and its modulation scheme on the RMSE. We also study the RMSE performance of the algorithm for different power levels of the target and the interference. Further, the comparison between Cyclic WCL and WCL w/o cyclostationarity is presented. It is observed that the Cyclic WCL provides significant performance gain over WCL.

Consider spectrum sensing systems where the presence of the primary user of the spectrum is detected by secondary users (SUs) in a centralized cooperative fashion. The sensing results can be correlated due to environmental reasons. SUs may or may not be honest. If dishonest, they could be colluding. Existing methods to fuse the SU reports either ignore the correlation in the SU reports, or they need to know the source of correlation. In this paper, we propose a belief propagation based fusion algorithm to exploit the correlations in reports of groups of SUs irrespective of cause. We show that identifying the groups of SUs having correlated reports reduces the probability of error of spectrum sensing. Our method is based on modeling the probability distribution underlying the SU reports as a Bayesian network. The process of learning the Bayesian network also shows that it is theoretically impossible to identify collusion.

In vehicular safety systems, two types of safety messages are required: Emergency Safety Message (ESM) and Periodic Beacon Message (PBM). The ESM has to be disseminated within a specified area with stringent delay and delivery ratio requirements, while the PBM does not need to meet these requirements. For exchanging the safety messages in Vehicular Ad-hoc NETwork (VANET), Inter-Vehicle Communication (IVC) is necessary whose de facto standard is Dedicated Short-Range Communications (DSRC). However, the effective transmission range in the DSRC-based IVC is short since a signal can be attenuated due to blocking by obstacles. In order to cover a large dissemination area in the DSRC-based IVC, multi-hop dissemination is required, which however causes channel collision and network congestion. Moreover, the coexistence with PBMs aggravates the collision and the congestion, which make it hard to satisfy the requirements of the ESM dissemination. To overcome the limitation of the DSRC, we utilize an extra TV White Space (TVWS) band that has a large communication range for ESM disseminations, and exploit a DSRC band for 1) the exchange of control data and 2) the compensation of ESM reception errors. In this paper, we propose and analyze a distributed channel usage framework that exploits advantages of DSRC and TVWS bands for ESM dissemination under the existence of PBMs. Our scheme employs TVWS Channel Rendezvous Algorithm (TCRA), ensuring that vehicles within a dissemination area select the same channel with the ESM sender. To compensate ESM reception failures in a TVWS band, our scheme adopts Two-Way Recovery Algorithm (TWRA) that uses DSRC and TVWS bands for ESM retransmission. Further, we establish an analytical delivery ratio model that considers a delay bound of an ESM for optimal parameter selections. To the best of our knowledge, this is the first attempt to propose a distributed channel usage scheme that leverages the strengths of TVWS and DSRC bands for safety message dissemination. Through an in-depth simulation study, we show that the proposed scheme satisfies ESM requirements for latency and packet delivery ratio, and outperforms previous approaches in various vehicular scenarios.

This paper focuses on analytical studies of the primary user (PU) traffic classification problem. colorblack{Observing} that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.

Recently, academic researchers and car manufactures have directed major efforts to develop active safety systems for reducing car accidents. In particular, for work zones they proposed to use smart cones that send a warning radio message to vehicles when the smart cones detect the possibility of an accident. The dominant protocol for vehicular communication is Dedicated Short Range Communications (DSRC), where Enhanced Distributed Channel Access (EDCA) and Hybrid Coordination Function (HCF) Controlled Channel Access (HCCA) are the MAC protocol options. Even though two MAC protocols are specified in the standard, there has been no attempt to use two protocols cooperatively nor to study the performance of such hybrid system in an actual vehicular network. In this paper, we examine for the first time the performance of a system that exploits EDCA and HCCA concurrently in vehicular networks. In particular, we concentrate on interaction between two MAC protocols and how much the interaction affects system performance. In addition, we investigate whether the system is feasible for work zone safety applications, and suggest guidelines for improvements based on our simulation findings.

We propose a primary user (PU) traffic distribution classifier for dynamic spectrum access networks based on multi-hypothesis sequential probability ratio test (MSPRT). In specific, we propose two classifiers: (i) an estimate-then-classify classifier, and (ii) a modified MSPRT classifier based on the average likelihood function considering partial knowledge of the PU traffic parameters. Using the sequential algorithm, we show that our proposed classifiers can achieve higher classification performance compared to the traditional maximum likelihood classifier using constant number of samples.

In this work we investigate the bounds on the estimation accuracy of Primary User (PU) traffic parameters with exponentially distributed busy and idle times. We derive closed-form expressions for the Cramér-Rao bounds on the mean squared estimation error for the blind joint estimation of the PU traffic parameters, specifically, the duty cycle, and the mean arrival and departure rates. Moreover, we present the corresponding maximum-likelihood estimators for the traffic parameters and discuss the effect of sensing errors in the joint estimation of PU traffic.

We present results on the system level performance of the IEEE 802.22 standard with sensing functionality, using a highly detailed implementation of the IEEE 802.22 protocol stack in the NS-2 simulator. Our attention is focused on the effect of spatio-temporal wireless microphone (WM) activity on the performance of the IEEE 802.22 network with spectrum sensing considered. In general we find that the frequency of WM appearance and activity duration should be quite high in all channels not used by TV broadcasters to reduce IEEE 802.22 throughput, for example about 50% WM occupancy in each of total of four channels. Impact on WM performance is found to be low in general using the two-stage spectrum sensing strategy with frequent sensing stages.

Wireless microphones operating in the TV white spaces often appear at specific venues such as schools or churches, and at specific times. Hence, their location and appearance pattern can be predicted from spectrum sensing statistics. In this paper we propose and evaluate three spectrum selection functions that utilize sensing results to provide long-term spectrum usage statistics as basis for channel selection to enhance performance by reducing interference and increasing throughput. To evaluate performance of the spectrum selection functions, these are implemented in a detailed system level simulator for the IEEE 802.22 standard. We find that the spectrum selection function that uses statistics about channel idle and busy periods performs best when primary user activity is high, and that the spectrum selection function that uses predictions about location and distance to primary users performs best when IEEE 802.22 radio users are mobile and the primary user activity is low.

This paper presents a mathematical analysis of the accuracy of estimating Primary User's (PU's) mean duty cycle u, as well as the mean off- and on-times, where the estimation accuracy is expressed in terms of the Cramer-Rao bound on the mean squared estimation error. For estimating u, we derive the mean squared estimation error for uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood (ML) estimation. The estimation accuracy of the mean PU off- and on-times is studied when ML estimation is employed. Besides, the impact of spectrum sensing errors on the estimation accuracy is studied analytically for the averaging estimators, while simulation results are used for the ML estimators. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions.

In this paper we present new results on the problem of finding the best channel sensing order for multi-channel Dynamic Spectrum Access (DSA) networks. We start with the general assumption that all Secondary Users (SUs) cooperatively sense each Primary User (PU) channel at one time. Then, the SU sensing results are reported to a DSA base station that schedules SU transmissions in order to maximize DSA network throughput. We then assume that PU traffic parameters are not perfectly known to DSA network and change over time, and propose a novel PU channel sensing order scheme based on the quality of PU traffic estimation. We adopt a maximum likelihood estimator to estimate the traffic statistics of PU channels and derive the Cramer-Rao (CR) bounds for the PU traffic estimation performance. Based on the CR bound and its Gaussian approximation, we analyze the impact of the estimation error on the DSA network throughput by computing a new metric called sensing order confidence, i.e., the probability that the best selected sensing order is not affected by PU traffic estimation errors. Finally, we formulate a convex optimization problem to determine the minimum number of PU channel state samples required for estimating PU traffic parameters after determining a certain constraint on the sensing order confidence metric to achieve the best sensing order.

In this paper, we propose and analyze a spectrum sensing method based on cyclostationarity specifically targeted for receivers with multiple antennas. This detection method is used for determining the presence or absence of primary users in cognitive radio networks based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. Analytical asymptotic expressions for the probability of detection and probability of false-alarm under both spatially uncorrelated and spatially correlated noise are derived and verified by simulations. The detection performance in a Rayleigh flat-fading environment is found and verified through simulations. One of the advantages of the proposed method is that the detection threshold is shown to be independent of both the number of samples and the noise covariance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown to provide higher detection probability and better robustness to noise uncertainty than existing multiple antenna cyclostationary-based spectrum sensing algorithms under both AWGN as well as a quasi-static Rayleigh fading channel.

We present an analytical framework for modeling a priority-based load balancing scheme in cellular networks based on a new algorithm called direct retry with truncated offloading channel resource pool (DRK). The model, developed for a baseline case of two cell network, differs in many respects from previous works on load balancing. Foremost, it incorporates the call admission process, through random access. In specific, the proposed model implements the Physical Random Access Channel used in 3GPP network standards. Furthermore, the proposed model allows the differentiation of users based on their priorities. The quantitative results illustrate that, for example, cellular network operators can control the manner in which traffic is offloaded between neighboring cells by simply adjusting the length of the random access phase. Our analysis also allows for the quantitative determination of the blocking probability individual users will experience given a specific length of random access phase. Furthermore, we observe that the improvement in blocking probability per shared channel for load balanced users using (DRK) is maximized at an intermediate number of shared channels, as opposed to the maximum number of these shared resources. This occurs because a balance is achieved between the number of users requesting connections and those that are already admitted to the network. We also present an extension of our analytical model to a multicell network (by means of an approximation) and an application of the proposed load balancing scheme in the context of opportunistic spectrum access.

OFDM and other multicarrier waveforms are in general very sensitive to RF non-idealities, such as phase noise and IQ imbalance, of transmitting and receiving devices. Extensive work has been carried out in the open literature in analyzing the performance of OFDM radio link under such RF impairments in terms of detection error rate and mostly concentrating on one impairment at a time. However, there is only very limited work on analytical investigations of mutual information and rate loss expressions, the heart of communication theory, as functions of RF impairment levels. In this article, we derive two closed-form mutual information expressions, in the form of infinite series representation, for an arbitrary subcarrier of a general OFDM radio link impaired with transceiver phase noise and IQ imbalance in frequency-selective Rayleigh distributed block-fading radio channel, covering both uncorrelated as well as fully correlated mirror subcarrier scenarios. We also show that the mutual information saturates to a finite value due to the inherent RF impairments even in the case that the symbol-to-noise ratio approaches infinity. Extensive comparisons with results obtained from full OFDM radio link simulations are also provided to illustrate and verify the accurate match between analytical and simulated mutual information behavior.

Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.

In this paper we propose an analytical framework which allows the investigation of the average channel throughput at the medium access control (MAC) layer for opportunistic spectrum access (OSA) networks with channel bonding enabled. We show that channel bonding is beneficial in certain cases only (e.g. low primary user traffic), though the extent of the benefit depend on the features of the OSA network including secondary network size and the total number of channels available for bonding.

In order to fully exploit the availability of primary users' (PUs) unused spectrum by secondary users, traffic prediction can be used to increase system throughput. In this paper we propose a PU traffic prediction algorithm based on estimated PU traffic state transition probabilities. The probabilities are obtained via constrained-time PU traffic parameters estimation assuming exponentially distributed PU ON/OFF channel utilization intervals. Moreover, we define prediction regions for the estimated parameters where optimal traffic prediction is possible. Finally, we theoretically quantify the prediction confidence as a function of the prediction time, the total estimation time period and the number of samples used for estimation.

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.

Knowledge about the location of licensed primary users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we discuss the problem of localizing multiple PUs based on direction-of-arrival (DoA) measurements. The key problem in such scenario is the difficulty and complexity of associating multiple DoA measurements to different PUs. We propose an iterative method that uses the probability of associating each DoA to a PU as the weighting coefficient of a modified Stansfield algorithm for DoA fusion. Results show the proposed algorithm obtains better accuracy than previous algorithms [1]-[3], and its complexity increases linearly with the number of CRs, compared with the exponential rate for algorithms based on exhaustive search of all possible associations.

Detecting the presence of licensed users and avoiding interference to them is vital to the proper operation of a Cognitive Radio (CR) network. Operating in a wideband channel requires high Nyquist sampling rates, which is limited by the state-of-the-art A/D converters. Compressive sampling is a promising solution to reduce sampling rates required in modern wideband communication systems. Among various signal detectors, feature detectors which exploit a signal cyclostationarity are robust against noise uncertainties. In this paper, we exploit the sparsity of the two-dimensional spectral correlation function (SCF), and propose a reduced complexity reconstruction method of the Nyquist SCF from the sub-Nyquist samples. The reconstruction optimization is formulated as a regularized least squares problem, and its closed form solution is derived. We show that for a given spectrum sparsity, there exists a lower bound on sampling rates that allows reliable SCF reconstruction.

Transmission over multiple frequency bands combined into one logical channel speeds up data transfer for wireless networks. On the other hand, the allocation of multiple channels to a single user decreases the probability of finding a free logical channel for new connections, which may result in a network-wide throughput loss. While this relationship has been studied experimentally, especially in the WLAN configuration, little is known on how to analytically model such phenomena. With the advent of Opportunistic Spectrum Access (OSA) networks, it is even more important to understand the circumstances in which it is beneficial to bond channels occupied by primary users with dynamic duty cycle patterns. In this paper we propose an analytical framework which allows the investigation of the average channel throughput at the medium access control layer for OSA networks with channel bonding enabled. We show that channel bonding is generally beneficial, though the extent of the benefits depend on the features of the OSA network, including OSA network size and the total number of channels available for bonding. In addition, we show that performance benefits can be realized by adaptively changing the number of bonded channels depending on network conditions. Finally, we evaluate channel bonding considering physical layer constraints, i.e. throughput reduction compared to the theoretical throughput of a single virtual channel due to a transmission power limit for any bonding size.

Direction-of-arrival (DoA)-based localization is a suitable approach for estimating the position of primary users in cognitive radio networks, as it does not require knowledge of transmission power or propagation channel. In this paper we consider a scenario where secondary users obtain DoA estimates by multiple antennas or virtual antenna arrays. We express the Cramer Rao bound of the localization error as a function of the problem geometry and of array-specific parameters. Then, we investigate how the bound scales with the number of sensors and the number of antennas per sensor, and we address the following question: is it better to have more sensors with fewer antennas, or fewer sensors with more antennas?

In this paper we address the problem of statistical spectrum sensing attacks, where misbehaving nodes falsify their sensing reports with a certain probability in order to artificially increase or reduce the throughput of a cognitive network. Instead of trying to identify unreliable nodes and exclude them from the decision process, we propose a novel approach where spectrum sensing and estimation of type/probability of the attacks are performed jointly. Our method is based on a Bayesian formulation and is implemented using belief propagation on factor graphs. The performance of the proposed method is then evaluated by analytical results and by simulations.

In this paper, we propose a reduced-complexity modulation classifier using multi-cycle features extracted from the Spectral Correlation Function (SCF) in order to distinguish among QAM, BPSK, MSK and AM modulation schemes. We analytically derive SCF statistics of the noise and signal features used for classification for finite number of samples, and use Chebyshev inequality to upper bound the minimum number of spectral averages required to attain a predetermined correct classification probability. Both theoretical and simulation results show that the proposed classifier requires on the order of 50 spectral averages to achieve a correct classification probability of 0.9 at SNR = 5 dB. The algorithm and corresponding analysis presented in this paper can be extended to classify other modulation schemes.

Spectrum sensing over a wide bandwidth increases the probability of finding unutilized spectrum for cognitive radios. However, energy-efficient VLSI realization of wideband sensing algorithms is challenging due to complex signal processing and real-time requirement. In addition, strong primary users introduce spectral leakage in adjacent unused bands, resulting in sensing performance degradation. To address these challenges, we propose a cascaded filter-bank channelization scheme and its reconfigurable VLSI architecture that can be optimized for power, area, sensing time, and detection performance. In addition, the spectral leakage due to strong interference is compensated with an interference cancellation method. Compared to the conventional PSD-based energy detection, the proposed channelization scheme supports reliable wideband signal detection with 2.5× less power. Given a 0.5ms sensing time under 30-dB adjacent-band interference-to-noise ratio, a 30× sensing-time improvement is achieved while maintaining a false- alarm probability of 0.1 and a detection probability of 0.9. The energy efficiency is improved from lower power consumption and reduced sensing time.

We present an analytical framework to assess the link layer throughput of multichannel Opportunistic Spectrum Access (OSA) ad hoc networks. Specifically, we focus on analyzing various combinations of collaborative spectrum sensing and Medium Access Control (MAC) protocol abstractions. We decompose collaborative spectrum sensing into layers, parametrize each layer, classify existing solutions, and propose a new protocol called Truncated Time Division Multiple Access (TTDMA) that supports efficient distribution of sensing results in “\kappa out of N” fusion rule. In case of multichannel MAC protocols, we evaluate two main approaches of control channel design with 1) dedicated and 2) hopping channel. We propose to augment these protocols with options of handling secondary user (SU) connections preempted by primary user (PU) by 1) connection buffering until PU departure and 2) connection switching to a vacant PU channel. By comparing and optimizing different design combinations, we show that 1) it is generally better to buffer preempted SU connections than to switch them to PU vacant channels and 2) TTDMA is a promising design option for collaborative spectrum sensing process when \kappa does not change over time.

We present two probabilistic estimation techniques for identifying the most likely number of frequency-hopping transmitters for a range of different scenarios and compare their performances. In the first technique, cumulative estimation, a Gaussian approximation methodology is developed based on a single integrated measurement over the observation time window. In the second technique, time-distributed estimation, a maximum-likelihood formulation is adopted, and time-specific observation data is used. We give specific analytical consideration to the potential that not all transmitters that are present will be always detected, and explore the effects of the probability of misdetection on the overall estimation process. Simulation results confirm that the approaches presented here can lead with high probability to a correct decision regarding the number of transmitters.

Information about primary transmitter location is crucial in enabling several key capabilities in cognitive radio networks, including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. Compared to other proposed non-interactive localization algorithms, the weighted centroid localization (WCL) scheme uses only the received signal strength information, which makes it simple to implement and robust to variations in the propagation environment. In this paper we present the first theoretical framework for WCL performance analysis in terms of its localization error distribution parameterized by node density, node placement, shadowing variance, correlation distance and inaccuracy of sensor node positioning. Using this analysis, we quantify the robustness of WCL to various physical conditions and provide design guidelines, such as node placement and spacing, for the practical deployment of WCL. We also propose a power-efficient method for implementing WCL through a distributed cluster-based algorithm, that achieves comparable accuracy with its centralized counterpart.

We present an analytical framework for modeling a priority-based load balancing scheme in cellular networks. The model differs in many respects from previous work on load balancing, in particular by incorporating the call admission process, through random access; allowing differentiation of users based on their priorities; and by incorporating the received signal properties. The analysis illustrates that, for example, ignoring channel quality has a non- negligible impact on the performance of load balancing. Moreover, the system model allows for the scenario-based determination of whether blocking probability or collision probability (during call admission) is the more dominant factor in the degradation of the performance of UEs affected by load balancing.

Dramatic increase in demand for spectrum access opportunities has triggered a deficiency of conventional single frequency band sensing. Moreover, methods needing large amounts of observations are also inefficient. This has motivated the proposition of an adaptive multitaper spectral detector (AMTSD) for wideband sensing based on Locally Most Powerful Test (LMPT). By taking advantage of multitaper gains, the proposed detector provides a higher degree of freedom (DoF) for the improvement of detection performance. Besides, the detector performs two phase sensing with adjustable spectral-resolution. This makes the system robust to strong interference environments and compatible with wideband spectrum sensing applications. Numerical simulations show that the proposed detector outperforms the cooperative autocorrelation-based and energy detectors in single band sensing by more than 30% in detection performance under SNR=0 dB and P_{FA}=10^{-3}. Moreover, by the provided detector, weak PU (-5 dB) detection can be achieved with multitaper gains even with adjacent strong interference (30 dB).

We propose an analytical model to assess the throughput and call blocking rate of Opportunistic Spectrum Orthogonal Frequency Division Multiple Access network constituted of multiple classes of Secondary Users (SUs) and temporarily active Primary Users (PUs) of different priorities. In the case of PUs we consider low activity wideband and high activity narrowband users. While in the case of SUs we consider subscribers transmitting real-time constant bit rate (CBR) and elastic variable bit rate (VBR) traffic. We conclude that with the increased activity of PUs, CBR traffic experiences larger throughput decrease than VBR. Moreover, blocking rate of CBR connections decreases exponentially with increased inter-arrival rate of PU, while we observe no direct correlation between obtained SU network throughput and CBR blocking rate.

We present an analytical framework for identifying the most likely number of frequency-hopping transmitters in the presence of potential misdetection. The problem is formulated for the case where there is a single, global misdetection probability characterizing all transmitter/sensor links, and for the case where the misdetection probabilities are permitted to be different across the different pairwise transmitter/sensor links. Simulation results confirm that the approach can lead with high probability to a correct decision regarding the number of interferers.

A common technique for cognitive radio wideband spectrum sensing is energy/power detection of primary users (PU) in frequency domain. Specifically, power spectrum estimation methods are combined with power detection statistics to test the PU presence. However, when detecting in a particular band of interest these techniques suffer from energy leakage and adjacent channel interference. In this paper, we derive a common matrix framework for the analytical performance of power detectors when FFT, windowed FFT, or multitap windowed FFT are used. Our matrix model is verified by simulations of modulated PU signals. We further propose a low-complexity compensation method to adapt the thresholds in the presence of large power difference between channels. By using both the multitap windowing and the constant false-alarm-rate method in the presence of strong signals, we demonstrate a 2-times increase in the detection rate performance as compared to existing methods. The proposed algorithm achieves similar PFAand PDas FFT at lower sample complexity, leading to reduced sensing times.

Cognitive radios (CRs) receive several users simultaneously. Therefore, the ADC of an CR requires a large dynamic range (DR) to guarantee adequate resolution per user. Thus far the ADC DR requirements have been prohibitive for the wide spread introduction of CR in the hand held market. The power consumption of an ADC reduces with an order of magnitude per decade. In the absence of disruptive new technologies, we expect this power trend to continue for the foreseeable future. Therefore, we propose an analog spatial filtering technique and present iterative methods to alleviate the ADC DR requirements and accelerate the overall power reduction of a CR. Simulation results indicate that for realistic scenarios the ADC resolution can be reduced by 4 bits per ADC, reducing the overall ADC power consumption with more than 90%.

The most common physical layer models for network simulations are the bit error rate and the SNR threshold model. In time-varying channels such as those experienced in vehicular networks, these models are assumed valid as long as the packet duration is less than the coherence time of the channel. The coherence time is a statistical measure of the channel invariance. It is independent of the signal parameters or the receiver structure. While it is convenient to decouple the system performance from the channel, this paper shows that this simplification may lead to inaccurate performance assessments and erroneous conclusions. This paper suggests a new metric, the normalized empirical coherence time (NETC), based on results from an extensive simulation campaign of a typical IEEE 802.11p system. The NETC delineates the minimum time (as a percentage of signal duration) over which the system achieves some performance threshold. The metric is explicitly a function of modulation, packet duration, and the traditional coherence time. This new metric could be used in place of the traditional coherence time as a constraint on the packet duration necessary to assume channel variation has negligible impact on performance.

Location aware applications benefit from accurate position information. The position uncertainty can be reduced through cooperative localization in distributed systems with accurate point-to-point ranging. Received signal strength (RSS) measurement enables low cost, low complexity distance estimation. The distance estimate suffers from two sources of error: RSS estimation error and propagation model error. This paper explores the contribution of each type of error to the overall point-to-point distance error. Prior research focused on the model error which establishes a floor on the distance error. However, the oft ignored RSS estimation error significantly degrades the distance estimation accuracy as the distance increases. By including the affect of RSS estimation error, practical estimation algorithms can be compared. Two proposed RSS estimation algorithms are compared in terms of their impact on distance error.

Characterizing the number and type of transmitters occupying a given frequency band is a critical aspect of spectrum sensing specifically and cognitive radio generally. We present an analytical framework based on probability to identify the number of frequency hopping transmitters of one specific type in a band of interest, and show that the probability mass functions associated with the different potential number of transmitters quickly becomes Gaussian as the number of channel observations increases. Simulation results confirm that the approach can lead with high probability to a correct decision regarding the number of interferers. Thus, the methods here can serve as a valuable complement to other spectrum sensing approaches.

Cross-layer optimization of the spectrum sensing (SS) sub-system in a cognitive radio (CR) is essential for achieving the best spectrum utilization because the three SS layers - sensing radio, sensing PHY and sensing MAC - jointly determine the overall performance of the SS sub-system. In this paper we introduce a function called detection efficiency that measures how much of the idle primary user spectra is actually utilized by the CR users as a function of regulatory constraints, network specifications and the parameters of the sensing radio, MAC and PHY. By optimizing these parameters to maximize detection efficiency of the SS system, the designer can compare and select the best SS design for a given wireless network requirement. By applying this design method to evaluate several design options for the SS layers, we show that cross-layer optimization can in some cases improve the detection efficiency (and therefore spectrum utilization) from 35% to as much as 99%.

Cognitive radio systems offer the opportunity to improve spectrum utilization by detecting unoccupied spectrum bands and adapting the transmission to those bands while avoiding the interference to primary users. This novel approach to spectrum access introduces unique functions at the physical layer: reliable detection of primary users and adaptive transmission over a wide bandwidth. In this paper, we address design issues involved in an implementation of these functions that could limit their performance or even make them infeasible. The critical design problem at the receiver is to achieve stringent requirements on radio sensitivity and perform signal processing to detect weak signals received by a wideband RF front-end with limited dynamic range. At the transmitter, wideband modulation schemes require adaptation to different frequency bands and power levels without creating interference to active primary users. We introduce algorithms and techniques whose implementation could meet these challenging requirements.

This paper presents a complete study and characterization of a real-time frequency-hopped, frequency shift-keyed testbed capable of transmitting data at 160 kb/s, with hopping rates of up to 80 Khops/s operating in the 900 MHz band. The system provides the highest hopping rate reported to date and sets a new trend for FHSS communications with superior low probability of interception/detection and anti-jamming (LPI/LPD/AJ) capabilities. The architecture features a direct digital frequency synthesizer to enable high-rate hopping, and a frequency correlator-based demodulator, plus all digital timing and frequency recovery algorithms to minimize complexity. Furthermore, single sideband modulation was used to achieve spectral efficiency. The testbed is software configured and provides the user with full control over the diversity combining techniques, symbol interleaving, packet structure, and acquisition protocols. A total of 5850 independent experiments were carried out under various receiver configurations and wireless environments. The results underscore the dramatic potential for a system that optimally combines high-rate hopping, interleaving, and equal gain combining to combat severe propagation conditions, including multipath fading and intentional jamming.

This paper describes the implementation of a novel direct digital frequency synthesizer (DDFS) based fast frequency-hopping frequency shift keyed (FSK) testbed capable of transmitting 160 kbps with a hopping rate up to 80 khops/s. The testbed is equipped with a variety of user-defined parameters in order to fully characterize its performance over the physical channel. Initial measurements using the testbed were carried out in different wireless environments and indicate that higher hopping rates can significantly improve radio link quality when proper diversity combining is utilized.