As the proliferation of devices on the Internet of Things (IoT) continues, efficient and sustainable power solutions are becoming increasingly essential, particularly for battery-less systems operating in dense networks. This work comprehensively defines the concepts in IoT devices and categorises them based on their power consumption levels. It introduces a practical framework for power redistribution using light energy harvesting and RF-based wireless power transfer. A novel energy donation mechanism is proposed that enables energy-abundant nodes equipped with photovoltaic (PV) panels to share excess harvested energy with nearby nodes via RF transmission. Through analytical modelling, link-budget analysis, and experimental validation, the study demonstrates the feasibility and effectiveness of the proposed architecture in maintaining reliable operation across densely deployed IoT nodes. To the best of our knowledge, this work presents the first experimentally validated framework that autonomously integrates light-to-RF energy donation, combining indoor light energy harvesting, short-range RF redistribution, and node placement optimisation within a single deployable system. This approach directly addresses the power sustainability challenge, paving the way toward fully autonomous and battery-less IoT ecosystems.

LoRa is a widely adopted physical layer technology for low-power wide-area IoT networks, yet its performance under concurrent LoRa transmissions is not fully understood. Most existing analytical models focus on payload interference and overlook the preamble, which is essential for LoRa nodes synchronization, as a potential contributor to interference effects. In this paper, we analyze the LoRa preamble as a source of interference by applying existing Symbol Error Rate (SER) models to the specific case of preamble transmissions. Building on this analysis, we derive a preamble-aware Frame Error Rate (FER) model that jointly accounts for preamble-based detection and payload symbol decoding. Interestingly, our analysis demonstrates that preamble interference has a greater impact than payload interference, a result validated through controlled experiments conducted using commercial LoRa transceivers. This asymmetry indicates that, in time-slotted LoRa networks, interference can be mitigated by controlling transmitter–receiver timing to avoid preamble–preamble overlap, leading to the experimentally validated concept of Pair Synchronization.

Respiration monitoring via radio signals enables contactless health sensing but suffers from interference caused by nearby motion. We propose a robust respiration sensing framework using Cell-free Massive MIMO (CF-mMIMO), which leverages spatial macro-diversity for interference resilience. Specifically, we analyze respiration sensing in single-antenna channels using Power Spectral Density (PSD) to reveal the impact of interference on the breathing channel’s movement spectrum. Based on this, we introduce a new metric, Sensing-Signal-to-Interference Ratio (SSIR), to evaluate local channel quality without requiring ground truth. Then, we design a Weighted Antenna Combining (WAC) method to prioritize reliable sensing links and suppress distortion. Experimental validation using a 64-antenna CF-mMIMO testbed with 100 Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers over an 18 MHz bandwidth confirms the framework’s robustness. In the presence of interference, the WAC method achieves a mean waveform correlation of 0.81 with ground truth, significantly outperforming single-antenna (0.52), averaging-based methods (0.53), and existing Wi-Fi approaches. Finally, we analyze the impact of time, frequency, and spatial resource allocation on both communication and sensing performance. Results show that increasing bandwidth and antenna count benefits both communication and sensing. With a sufficient number of antennas, respiration sensing remains accurate even with long coherence times (1 second) and narrow bandwidths (3 subcarriers), enabling its integration into communication systems with negligible overhead, making it practically “for free”. This makes CF-mMIMO a promising architecture for robust and scalable Integrated Sensing and Communication (ISAC) health monitoring.

Cell-free massive multiple-input multiple-output (CF mMIMO) networks, in which multiple antennas simultaneously serve multiple user equipments (UEs), offer significant spectral efficiency (SE) gains. However, their energy efficiency (EE) performance still requires further investigation. Traditional approaches to radio resource allocation in CF mMIMO systems focus on solving the two-dimensional UE-antenna precoding problem. In this work, we propose a novel resource allocation framework that addresses the four-dimensional UE-antennafrequency-time precoding allocation for EE maximization. Considering the frequency-domain fast fading variations and timedomain traffic dynamics, we develop algorithms for EE maximization. Our heuristic delayed scheduling algorithm enhances EE by up to 10% compared to the algorithm designed for sum SE maximization. Furthermore, we demonstrate that EE performance is highly sensitive to system load, achieving a 7.4% higher EE at 69% system load compared to full load under the simulation settings. Finally, we analyze the impact of UE load on IP packet delay, establishing a relationship between the maximum UE load and packet delay budget.

This paper presents a new transparent amplifying intelligent surface (TAIS) architecture for improving multi-user uplink performance in indoor-to-outdoor communications. Unlike passive reconfigurable intelligent surfaces (RIS) that primarily manipulate phase shifts, TAIS operates as an amplifier-based transmissive intelligent surface. It possesses the unique ability to refract and amplify signals from all users. Utilizing indium tin oxide coating and cutting-edge printing techniques, TAIS can be fabricated on windows without causing any visible alterations. This paper focuses on leveraging the TAIS to enhance the uplink spectral efficiency (SE) of multiple users in indoor-to-outdoor communications. Our analysis builds upon key assumptions, including the availability of perfect channel state information (CSI) and the use of a third-order memoryless polynomial model for tractable nonlinear PA characterization. The Bussgang decomposition is applied for nonlinear performance analysis, which remains approximately accurate for practical non-Gaussian signals. By collaboratively optimizing the refraction coefficient matrix of the TAIS and the combiner of the base station, the multi-user uplink SE maximization is achieved with consideration of the nonlinearity in the amplification process of TAIS. An efficient alternating optimization framework is developed to solve the non-convex problem approximately. Another important aspect is that we develop a zero-forcing-based successive convex approximation (ZF-SCA) algorithm to solve the problem with lower computational complexity. By employing the zero-forcing combiner at the BS, the problem is reduced to the refraction coefficient optimization at TAIS, using SCA techniques. Simulations demonstrate that the proposed TAIS system can significantly enhance SE by up to 39.1%, as compared to its alternative methods.

The time-modulated array is a simple array architecture in which each antenna is connected to an RF switch that serves as a modulator. The phase shift is achieved by digitally controlling the relative delay between the periodic modulating sequences of the antennas. Two factors limit the practical use of this architecture for communication and sensing. First, the switching frequency is high, as it must be a multiple of the sampling frequency. Second, the discrete modulating sequence introduces undesired harmonic replicas of the signal, which are out-of-band interference. This paper proposes the OFDM modulation with an appropriate precoder to facilitate the aliasing of the harmonic components to simultaneously reduce sideband radiation and switching frequency. The transmit signal has a repeated block structure in the frequency domain to facilitate coherent combining of the aliased signal blocks. As a result, a factor A reduction in switching frequency is achieved at the cost of a factor A reduction in communication capacity. Doubling A reduces sideband radiation by around 2.9 dB. The feasibility of the proposed method is experimentally validated for wideband signals. Full-wave simulations are performed to validate the beamforming performance based on the experimental results.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

sponsorship: This research is supported by iSEE-6G project under the Horizon Europe Research and Innovation program with Grant Agreement No. 101139291. (iSEE-6G project under the Horizon Europe Research and Innovation program|101139291)

This paper demonstrates the feasibility of respiration pattern estimation utilizing a communication-centric cell-free massive MIMO OFDM Base Station (BS). The sensing target is typically positioned near the User Equipment (UE), which transmits uplink pilots to the BS. Our results demonstrate the potential of massive MIMO systems for accurate and reliable vital sign estimation. Initially, we adopt a single antenna sensing solution that combines multiple subcarriers and a breathing projection to align the 2D complex breathing pattern to a single displacement dimension. Then, Weighted Antenna Combining (WAC) aggregates the 1D breathing signals from multiple antennas. The results demonstrate that the combination of space-frequency resources—specifically in terms of subcarriers and antennas—yields higher accuracy than using only a single antenna or subcarrier. Our results significantly improved respiration estimation accuracy by using multiple subcarriers and antennas. With WAC, we achieved an average correlation of 0.8 with ground truth data, compared to 0.6 for single antenna or subcarrier methods—a 0.2 correlation increase. Moreover, the system produced perfect breathing rate estimates. These findings suggest that the limited bandwidth (18 MHz in the testbed) can be effectively compensated by utilizing spatial resources, such as distributed antennas.

This work focuses on channel estimation in extremely large aperture array (ELAA) systems, where near-field propagation and spatial non-stationarity introduce complexities that hinder the effectiveness of traditional estimation techniques. A physics-based hybrid channel model is developed, incorporating non-binary visibility region (VR) masks to simulate diffraction-induced power variations across the antenna array. To address the estimation challenges posed by these channel conditions, a novel algorithm is proposed: Visibility-Region-HMM-Aided Polar-Domain Simultaneous Orthogonal Matching Pursuit (VR-HMM-P-SOMP). The method extends a greedy sparse recovery framework by integrating VR estimation through a hidden Markov model (HMM), using a novel emission formulation and Viterbi decoding. This allows the algorithm to adaptively mask steering vectors and account for spatial non-stationarity at the antenna level. Simulation results demonstrate that the proposed method enhances estimation accuracy compared to existing techniques, particularly in low-SNR and sparse scenarios, while maintaining a low computational complexity. The algorithm presents robustness across a range of design parameters and channel conditions, offering a practical solution for ELAA systems.

This paper explores the application of uplink fronthaul compression techniques within Open RAN (ORAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) systems. With the ever-increasing demand for high data rates and system scalability, the fronthaul load becomes a critical bottleneck. Our method uses ORAN compression techniques to efficiently compress the fronthaul signals. The goal is to greatly lower the fronthaul load while having little effect on the overall system performance, as shown by Block Error Rate (BLER) curves. Through rigorous link-level simulations, we compare our quantization strategies against a benchmark scenario with no quantization, providing insights into the trade-offs between fronthaul data rate reduction and link performance integrity. The results demonstrate that our proposed quantization techniques not only lower the fronthaul load but also maintain a competitive link quality, making them a viable solution for enhancing the efficiency of next-generation wireless networks. This study underscores the potential of quantization in O-RAN contexts to achieve optimal balance between system capacity and performance, paving the way for more scalable and robust DD-MIMO deployments.

This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed to maximize harvested energy under fluctuating environmental conditions. We validated our model against a prototype batteryless IoT node, conducting experiments under three distinct illumination scenarios. Results show a strong correlation between analytical and measured supercapacitor voltage profiles, confirming the proposed model’s accuracy.

This paper presents a novel multi-attribute decision-making approach for managing handovers in Optical Wireless Communication/Radio Frequency Heterogeneous Networks (OWC/RF HetNets). The proposed method systematically evaluates potential Access Points (APs) using multiple criteria, including fixed and variable handover costs, the ratio of the node's data rate demand to the achievable rate from APs, and the ratio of the utilized capacity of APs. Using local parameters, nodes assess these attributes over a finite future horizon and construct a decision matrix. The VIKOR method is then applied to decide whether to initiate a handover to a new AP or remain connected to the current one. Simulation results show that the proposed decentralized scheme effectively manages handovers, leading to improved load balancing and reduced handover latency in OWC/RF HetNets.

Accurate Probability of Line-of-Sight$(P_{\text{LoS}})$modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing$P_{L o S}$models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined builtup parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban Line-ofSight Simulator (ULS) to model$P_{\text{LoS}}$for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical$P_{L o S}$for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate$P_{L o S}$results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks.

This paper presents a distributed cell-free communication and radar system that operates in the uplink. The system schedules dedicated transmit (Tx) access points (APs) to transmit dedicated radar signals in the uplink together with the user equipment (UE). The receiving (Rx) APs decode the UE payloads while also detecting targets based on the Tx AP signals. To mitigate the added Tx AP interference, the Rx APs use multiuser processing to recover the UE payloads, while a combination of large processing gains, adaptive beamforming, spatial diversity, interference cancellation and power control is used to mitigate the UE interference impacting the radar. The radar introduces few changes to the physical layer and the additional computations needed are comparable to the communication system. The system is validated numerically by using Monte-Carlo simulations, where we highlight the inherent trade-offs between the various system parameters (such as the power control balancing, and the number of Tx APs scheduled and UEs cancelled) and show that both the communication and radar systems can be effectively integrated into the same network at a near optimal performance.

With the surge in IoT devices ranging from wearables to smart homes, prompt transmission is crucial. The Age of Information (AoI) emerges as a critical metric in this context, representing the freshness of the information transmitted across the network. This paper studies hybrid IoT networks that employ Optical Communication (OC) as a reinforcement medium to Radio Frequency (RF). We formulate a quadratic convex optimization that adopts a Pareto optimization strategy to dynamically schedule the communication between devices and select their corresponding communication technology, aiming to balance the maximization of network throughput with the minimization of energy usage and the frequency of switching between technologies. To mitigate the impact of dominant sub-objectives and their scale disparity, the designed approach employs a regularization method that approximates adequate Pareto coefficients. Simulation results show that the OC supplementary integration alongside RF enhances the network’s overall performances and significantly reduces the Mean AoI and Peak AoI, allowing the collection of the freshest possible data using the best available communication technology.

Utilizing the mmWave band can potentially achieve the high data rate needed for realistic and seamless interaction within a virtual reality (VR) application. To this end, beamforming in both the access point (AP) and head-mounted display (HMD) sides is necessary. The main challenge in this use case is the specific and highly dynamic user movement, which causes beam misalignment, degrading the received signal level and potentially leading to outages. This study examines mmWave-based coordinated multi-point networks for VR applications, where two or multiple APs cooperatively transmit the signals to an HMD for connectivity diversity. Instead of using omni-reception, we propose dual-beam reception based on the analog beamforming at the HMD, enhancing the receive beamforming gain towards serving APs while achieving diversity. Evaluation using actual HMD movement data demonstrates the effectiveness of our approach, showcasing a reduction in outage rates of up to 13% compared to quasi-omnidirectional reception with two serving APs, and a 17% decrease compared to steerable single-beam reception with a serving AP. Widening the separation angle between two APs can further reduce outage rates due to head rotation as rotations can still be tracked using the steerable multi-beam, albeit at the expense of received signal levels reduction during the non-outage period.

Domino-tiled phased arrays (DTPA), based on two-element single polarized sub arrays, are conceived for beyond 100 GHz multi-user multiple-input multiple-output (MIMO) communication. Compared to lower frequencies, the small wave-length poses stringent requirements on the connections between the integrated circuit (IC) and antenna array, as the antenna dimensions are comparable to IC size. To address this problem, the tiling configuration of DTPAs is optimized by a genetic algorithm (GA) while jointly maximizing the spectral efficiency (SE) and reducing side lobe levels (SLLs), considering above 100 GHz channel models for multiple users. As a proof-of-concept, optimized DTPAs for an array size of 8 × 10 are realized in full-wave EM simulator through a printed circuit board (PCB) manufacturing process. Our optimized DTPAs yield more than 16% impedance bandwidth, targeting the [120–140] GHz D-band. Moreover, the optimized DTPAs reach a SE and SLL up to 3.7 b/s/Hz and -13 dB for a two-user scenario.

This paper investigates the use of Neural Network (NN) nonlinear modelling for Power Amplifier (PA) linearization in the Walsh-Hadamard transceiver architecture. This novel architecture has recently been proposed for ultra-high bandwidth systems to reduce the transceiver power consumption by extensive parallelization of the digital baseband hardware. The parallelization is achieved by replacing two-dimensional quadrature modulation with multi-dimensional Walsh-Hadamard modulation. The open research question for this architecture is whether conventional baseband signal processing algorithms can be similarly parallelized while retaining their performance. A key baseband algorithm, digital predistortion using NN models for PA linearization, will be adapted to the parallel Walsh architecture. A straighforward parallelization of the state-of-the-art NN architecture is extended with a cross-domain Knowledge Distillation pre-training method to achieve linearization performance on par with the quadrature implementation. This result paves the way for the entire baseband processing chain to be adapted into ultrahigh bandwidth, low-power Walsh transceivers.

The sustainable design of Internet of Things (IoT) networks encompasses considerations related to energy efficiency and autonomy as well as considerations related to reliable communications, ensuring no energy is wasted on undelivered data. Under these considerations, this work proposes the design and implementation of energy-efficient Bluetooth Low Energy (BLE) and Light-based IoT (LIoT) batteryless IoT sensor nodes powered by an indoor light Energy Harvesting Unit (EHU). Our design intends to integrate these nodes into a sensing network to improve its reliability by combining both technologies and taking advantage of their features. The nodes incorporate state-of-theart components, such as low-power sensors and efficient System-on-Chips (SoCs). Moreover, we design a strategy for adaptive switching between active and sleep cycles as a function of the available energy, allowing the IoT nodes to continuously operate without batteries. Our results show that by adapting the duty cycle of the BLE and LIoT nodes depending on the environment’s light intensity, we can ensure a continuous and reliable node operation. In particular, measurements show that our proposed BLE and LIoT node designs are able to communicate with an IoT gateway in a bidirectional way, every 19.3 and 624.6 seconds, respectively, in an energy-autonomous and reliable manner.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for multi-user uplink enhancement in indoor-to-outdoor communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the multi-user uplink spectral efficiency (SE) of multiple users in indoor-to-outdoor communications. By jointly optimizing the TAIS's refraction coefficient matrix and combiner of the base station, The multi-user uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. An efficient alternating optimization framework is proposed to solve the non-convex problem approximately. Simulations show that our proposed TAIS can increase the SE by up to 39.1% as compared to its alternative methods.

We propose a deep Q-Learning (DQN) and heuristic-based radio resource scheduling (RRS) algorithms, which determine serving access points (APs), for maximum ratio transmission (MRT) precoder in the cell-free massive multiple-input multiple-output (CF mMIMO) networks. We show that, DQN-based algorithm yields up to 6% higher sum spectral efficiency (SE) than the heuristic and the reinforcement learning based algorithms for MRT precoder, but the heuristic algorithm yields 1000 times shorter computation time than the DQN-based algorithm. Thus, the low complexity MRT heuristic algorithms are the trade off between the performance and the cost.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS’s phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS’s amplification process. The key enabler is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important enabler is that we design a low-complexity algorithm to solve the optimization problem using the difference of convex programming. Moreover, the asymptotic spectral efficiency under nonlinear amplification and power scaling law with infinitely large elements under both the sparse and rich scattering channel models are analyzed. Simulation results show that our proposed TAIS can increase the SE by up to 24.7% as compared to its alternative methods.

Cell-free massive multiple-input multiple-output (CFmMIMO) is a paradigm that can improve users’ spectral efficiency (SE) far beyond traditional cellular networks. Increased spatial diversity in CFmMIMO is achieved by spreading the antennas into small access points (APs), which cooperate to serve the users. Sequential fronthaul topologies in CFmMIMO, such as the daisy chain and multi-branch tree topology, have gained considerable attention recently. In such a processing architecture, each AP must store its received signal vector in the memory until it receives the relevant information from the previous AP in the sequence to refine the estimate of the users’ signal vector in the uplink. In this paper, we adopt vector-wise and element-wise compression on the raw or pre-processed received signal vectors to store them in the memory. We investigate the impact of the limited memory capacity in the APs on the optimal number of APs. We show that with no memory constraint, having single-antenna APs is optimal, especially as the number of users grows. However, a limited memory at the APs restricts the depth of the sequential processing pipeline. Furthermore, we investigate the relation between the memory capacity at the APs and the rate of the fronthaul link.

Communication by jointly precoded transmission from many distributed access points (APs), called cell-free massive multiple-input multiple-output (CF mMIMO), is a promising concept for beyond 5G systems. One of the challenging aspects of CF mMIMO is the efficient management of the radio resources. We propose both reinforcement learning (RL)-based and heuristic precoding aware radio resource scheduling (RRS) algorithms aiming at maximizing sum spectral efficiency (SE). The proposed algorithms allocate resources for Maximum Ratio Transmission (MRT), Zero-Forcing (ZF), Regularised Zero-Forcing (RZF), and Optimized Zero Forcing (OZF) precoders. For the resource allocation, both the set of serving APs and the physical resource blocks are considered. In high noise scenarios, the proposed RL-based RRS algorithm combined with the MRT precoder shows 2.4 times higher sum SE than the standard Round Robin scheduler. Moreover, we demonstrate that the proposed heuristic algorithms offer similar sum SE while significantly reducing the complexity compared to the RL-based solution. We also show that the RZF and OZF precodings, which are superior to the ZF precoding in noisy environments, result overall in more transmitted power. Therefore, assuming the same radio resource schedule and precoding strategy in the neighboring cells, it will result in more inter-cell interference and an overall reduced performance.

Cell-free Massive MIMO has emerged as a promising solution for next-generation radio networks. Particularly the user-centric variant where users are served by a limited subset of access points, a so-called cluster, has garnered significant attention within the research community. Despite numerous proposed physical layer solutions, managing AP clusters in case of user mobility remains challenging. In this study, we first present a realistic method for modeling the temporal evolution of the channel in cell-free Massive MIMO. Next, we develop two clustering and handover strategies: 1) a fixed clustering strategy that computes the ideal cluster when a cluster handover threshold is exceeded and 2) an opportunistic strategy that opportunistically adds serving APs as the user moves. Moreover, we establish a connection between our findings and O-RAN, an emerging network architecture that offers open interfaces and network-wide control capabilities, thus, facilitating practical implementation of our research.

Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems with concurrent Channel State Information (CSI) represented in the training data set. Consequently, solutions for RIS-assisted wireless communication systems under time-varying environments are relatively unexplored. However, communication problems should be considered with realistic assumptions; for instance, in scenarios where the channel is time-varying, the policy obtained by reinforcement learning should be applicable for situations where CSI is not well represented in the training data set. In this paper, we apply Meta-Reinforcement Learning (MRL) to the joint optimization problem of active beamforming at the Base Station (BS) and phase shift at the RIS, motivated by MRL's ability to extend the DRL concept of solving one Markov Decision Problem (MDP) to multiple MDPs. We provide simulation results to compare the average sum rate of the proposed approach with those of selected forerunners in the literature. Our approach improves the sum rate by more than 60% under time-varying CSI assumption while maintaining the advantages of typical DRL-based solutions. Our study's results emphasize the possibility of utilizing MRL-based designs in RIS-assisted wireless communication systems while considering realistic environment assumptions.

Most spectrum surveys conducted worldwide demonstrate that the radio-electric spectrum in use at any given location and instant of time is below 25%. Current spectrum management policies and spectrum utilization inefficiency is becoming unsustainable for future development of radio technologies and services. In this context, dynamic spectrum access is a promising technique for improving spectrum utilization efficiency. A key scientific gap is identifying inaccurate spectrum data from hidden nodes that is not homogeneously distributed in the spatial domain and dynamically vary in time and frequency. For bridging this gap, our paper presents the research results of a spectrum feature extraction algorithm based on multi-correlation and Random Forest. Our algorithm is capable of estimating the spectrum utilization pattern in the spatial and frequency domain with a minimum reliability of 92% for a real heterogeneous networking scenario.

Communications between unmanned aerial vehicles (UAVs) play an important role in deploying aerial networks. Although some studies reveal that drone-based air-to-air (A2A) channels are relatively clear and thus can be modeled as free-space propagation, such an assumption may not be applicable to drones flying in low altitudes of built-up environments. In practice, low-altitude A2A channel modeling becomes more challenging in urban scenarios since buildings can obstruct the line-of-sight (LOS) path, and multipaths from buildings lead to additional losses. Therefore, we herein focus on modeling low-altitude A2A channels considering a generic urban deployment, where we introduce the evidence of the small-size first Fresnel zone at the millimeter-wave (mmWave) band to approximately derive the LOS probability. Then, the path loss under different propagation conditions is investigated to obtain an integrated path loss model. In addition, we incorporate the impact of imperfect beam alignment on the path loss, where the relation between path loss fluctuation and beam misalignment level is modeled as an exponential form. Finally, comparisons with the 3GPP model show the effectiveness of the proposed analytical model. Numerical simulations in different environments and heights provide practical deployment guidance for aerial networks.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS's phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. The key point is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important aspect is that we design a low-complexity algorithm to solve the problem using the difference of convex programming. Simulations show that our proposed TAIS can increase the SE by up to 32.6% as compared to its alternative methods.

This paper presents a multiple-access procedure for joint radar and communication systems. Dedicated radar antennas transmit uplink pilots in the same time-frequency resources as the user pilots over several coherence blocks. The radar and communication channels are subsequently estimated wherein the respective systems proceed as conventional. In order to facilitate pilot reuse and mitigate interchannel interference, the radar and user pilots are modulated in time and frequency so that the channels are orthogonal in the range-Doppler domain. This allows the respective channels to be separated without mutual interference or the need to allocate dedicated radar pilots. The main advantage is the time-division duplex compatibility and the near optimal radar performance. The performance trade-offs are discussed and the method is demonstrated with numerical simulations, demonstrating a relatively unaffected EVM while simultaneously recovering the radar channel for sensing.

Accurate network synchronization is a key enabler for services such as coherent transmission, cooperative decoding, and localization in distributed and cell-free networks. Unlike centralized networks, where synchronization is generally needed between a user and a base station, synchronization in distributed networks needs to be maintained between several cooperative devices, which is an inherently challenging task due to hardware imperfections and environmental influences on the clock, such as temperature. As a result, distributed networks have to be frequently synchronized, introducing a significant synchronization overhead. In this paper, we propose an online-LSTM-based model for clock skew and drift compensation, to elongate the period at which synchronization signals are needed, decreasing the synchronization overhead. We conducted comprehensive experimental results to assess the performance of the proposed model. Our measurement-based results show that the proposed model reduces the need for re-synchronization between devices by an order of magnitude, keeping devices synchronized with a precision of at least 10 microseconds with a probability 90%.

Despite the substantial success of deep learning for Automatic Modulation Classification (AMC), models trained on a specific transmitter configuration and channel model often fail to generalize well to other scenarios with different transmitter configurations, wireless fading channels, or receiver impairments such as clock offset. This paper proposes Contrastive Learning with Self-Reconstruction called CLSR-AMC to learn good representations of signals resilient to channel changes. While contrastive loss focuses on the differences between individual modulations, the reconstruction loss captures representative features of the signal. Additionally, we develop three data augmentation operators to emulate the impact of channel and hardware impairments without exhaustive modeling of different channel profiles. We perform extensive experimentation with commonly used realistic datasets. We show that CLSR-AMC outperforms its counterpart based on contrastive learning for the same amount of labeled data by significant average accuracy gains of 24.29%, 17.01%, and 15.97% in the Additive White Gaussian Noise (AWGN), Rayleigh, and Rician channels, respectively.

Multi-connectivity is considered a key enabler for 5G networks and beyond, aiming to enhance capacity by combining multiple communication links in the same or different bands. Similarly, in cell-free networks all \acpap jointly serve users in the same band, boosting capacity through enhanced spectral efficiency. Both approaches can be very effective in \acmmwave networks by addressing key issues of reliability and robustness due to the multiple simultaneous links. Furthermore, the use of narrow directional beams in \acmmwave spatially separates the signals, allowing for in-band multi-connectivity through local beamtraining. Such in-band multi-connectivity would be an alternative design to traditional cell-free networks that does not rely on phase-coherent processing or centralized methods for interference suppression. The physical layer processing and resource allocation problem then simplifies to a local beamtraining challenge, making these networks easier and simpler to implement and deploy, as any connection just has to train and maintain the local beam. We validate this approach by designing a multi-connectivity \acmmwave network with minimal network synchronization, relying solely on analog beamforming for spatial separation. Our evaluation results demonstrate that in-band multi-connectivity with 4 asynchronous and independent links can provide uninterrupted service even in dense, high-traffic scenarios, compared to up to 20% of service loss in a standard single-connectivity deployment. Distributing the traffic across multiple \acpap also had throughput gains of up to 30%, showing that multi-connectivity \acmmwave networks can provide a high-throughput, reliable and stable service for next-generation applications.

Millimeter-wave spectrum (30 to 300 GHz) is explored in order to provide higher throughput by exploiting the large bandwidth available. At those frequencies, multiple antenna systems are essential to combat severe path loss. Fully digital (FD) architectures, where each antenna connects to its own baseband chain, are considered the most energy-efficient at low frequencies while enabling multi-user multiplexing. However, unlike lower frequencies, channel propagation above 100 GHz is heavily line-of-sight dominated and the operating bandwidth is much larger which poses different constraints on signaling schemes and hardware components. The optimal beamforming architecture configuration is still an open problem above 100 GHz. To address this problem, we compare energy-efficient beamforming architectures for the D-band (110-170 GHz). We estimate the energy efficiency of future systems by following the technology trends in circuit implementation. We show that hybrid fully connected architecture is the most energy-efficient for the 7nm technology node and size-constrained antenna arrays. Hybrid partially connected architecture is the most energy-efficient for unconstrained antenna arrays. We show that FD architecture becomes energy-efficient for technology nodes better than 2nm.

Mm-wave spectrum (30 to 300 GHz) is explored in order to provide higher throughput by exploiting the large bandwidth available. Due to those higher frequencies, future systems are expected to have larger antenna arrays with more directive beams relying on line-of-sight propagation. Besides wider bandwidth, high-throughput systems also aim at serving multiple users in parallel. When those users are located at different ranges from the base station, they put very different requirements on the base station architecture and signals. This can translate, e.g., into architectures having different numbers of components and power consumption to optimally serve those users. Finding the architecture offering the best throughput vs. power consumption trade-off while serving multiple users at different distances is still an open problem in the literature. Typically, power optimization has been done considering a fixed operating distance and neglecting the user distribution which is crucial in future communication systems. To that end, we propose a novel range distribution aware (RDA) architecture dimensioning methodology that optimizes the power consumption of a transceiver architecture over the operating range by exploiting the user distribution. We show that the RDA design methodology can reduce the average power consumption of the transceiver by 20% in a typical scenario compared to traditional power optimization.

A batteryless Internet of Things (IoT) offers a sustainable alternative to battery-powered IoT devices, which produce billions of dead batteries every year. Devices are instead powered by a small supercapacitor, which is recharged by a renewable energy source. However, since IoT devices are often characterized by intermittent periods of high energy consumption followed by periods of reduced activity, conventional average energy consumption models cannot be used to assess if IoT devices can be powered by energy harvesters. Therefore, this article presents an alternative feasibility evaluation approach that focuses on modeling the worst case periods with peak energy consumption and short idle times, which pose the highest constraints on the capacitor’s behavior. This approach simplifies the characterization of the wireless technology energy consumption as these worst case periods can be determined by a few parameters. The methodology is then applied to combinations of popular IoT technologies (LoRaWAN, BLE Mesh, and 6TiSCH) and energy sources (solar, kinetic, and radio frequency energy) for two common IoT use cases. We show that the proposed parameters can be successfully extracted with power measurements for different network configurations and that the Power Management Unit configuration has a nonnegligible impact on the communication requirements. Finally, we discuss how to apply the model to other technologies and other use cases.

Wireless-based human activity recognition has become an essential technology that enables contact-free human–machine and human–environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets’ locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5$\text{m}\times 10$m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.

Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity. Sensing, and subsequently recognizing blockage types, is an initial step towards signal blockage avoidance. In this context, we investigate the feasibility of using human motion recognition as a surrogate task for blockage type recognition through a set of hypothesis validation experiments using both qualitative and quantitative analysis (visual inspection and hyperparameter tuning of deep learning (DL) models, respectively). A surrogate task is useful for DL model testing and/or pre-training, thereby requiring a low amount of data to be collected from the eventual JCAS environment. Therefore, we collect and use a small dataset from a 26 GHz cellular multi-user communication device with hybrid beamforming. The data is converted into Doppler Frequency Spectrum (DFS) and used for hypothesis validations. Our research shows that (i) the presence of domain shift between data used for learning and inference requires use of DL models that can successfully handle it, (ii) DFS input data dilution to increase dataset volume should be avoided, (iii) a small volume of input data is not enough for reasonable inference performance, (iv) higher sensing resolution, causing lower sensitivity, should be handled by doing more activities/gestures per frame and lowering sampling rate, and (v) a higher reported sampling rate to STFT during pre-processing may increase performance, but should always be tested on a per learning task basis.

Human blockage is one of the main challenges for mmWave communication networks in dynamic environments. The shadowing by a human body results in significant received power degradation and could occur abruptly and frequently. A shadowing period of hundred milliseconds might interrupt the communication and cause significant data loss, considering the huge bandwidth utilized in mmWave communications. An even longer shadowing period might cause a long-duration link outage. Therefore, a blockage prediction mechanism has to be taken to detect the moving blocker within the vicinity of mmWave links. By detecting the potential blockage as early as possible, a user equipment can anticipate by establishing a new connection and performing beam training with an alternative base station before shadowing happens. This paper proposes an early moving blocker detection mechanism by leveraging an extra guard beam to protect the main communication beam. The guard beam is intended to sense the environment by expanding the field of view of a base station. The blockage can be detected early by observing received signal fluctuation resulting from the blocker's presence within the field of view. We derive a channel model for the pre- shadowing event, design a moving blockage detection algorithm for the guard beam, and evaluate the performance of the guard beam theoretically and experimentally based on the measurement campaign using our mmWave testbed. Our results demonstrate that the guard beam can extend the detection range and predict the blockage up to 360 ms before the shadowing occurs.

This paper considers the cell-free (CF) massive multiple-input multiple-output (MIMO) architecture with an added virtual uplink radar integration. We consider joint communications and radar (JCR) in the uplink payload of the time-division duplex (TDD) frame and formulate a set of linear interference constraints in order to limit the user equipment (UE) interference imposed on the radar system at each accesspoint (AP) through power control. The constraints are incorporated into the sum spectral efficiency (SSE) and the sum-log-SNR (SLS) policies. Furthermore, a largest large-scale fading (LLSF) heuristic is formulated as an approximate low-complexity solution. Numerical simulations indicate that all methods provide similar performance in terms of spectral efficiency (SE) and that power control allows the communication system to be controlled to satisfy a given worst case radar performance.

The market size of Unmanned Aerial Vehicles (UAVs, a.k.a drones) can reach up to 10% of the global market value. In particular, drone delivery is one of the most attractive applications. The growing number of drones requires appropriate traffic management systems that will rely on cellular networks. However, it has been shown in the literature that these networks cannot provide reliable communication due to low coverage probability and frequent handovers. This article presents a potential solution targeting these problems while requiring no modifications of the existing infrastructure. Namely, equipping the UAV with multiple cellular modems to connect to different providers’ networks introduces network diversity resulting in 98% coverage probability at the flight altitude of 100 meters. In contrast, one network ensures only 80% coverage. At the same time, the size of the outage zones becomes up to ten times smaller and the frequency of harmful handovers is reduced to zero. The results are obtained with a physical-layer simulator utilizing a real urban 3D environment, cellular network parameters (e.g., site locations, antenna orientation and gains), and specific aerial channel models.

This paper considers the cell-free (CF) massive MIMO architecture from a joint communication and radar point of view. We propose a protocol for communication and sensing, where the network allocates a set of access-points (APs) to participate in the uplink together with the users (UEs) to serve the network with a radar signal. This realizes the radar system as a distributed bistatic radar system, where the objective is to recover the radar echoes from the multiuser interference. The imposed cost on the communication system is the loss of one AP and the need to schedule one additional virtual UE per allocated AP. We present two modes of radar sensing, occurring in either the uplink training segment or the data payload segment of the communication frame. A subspace signal model and its corresponding generalized likelihood ratio test is developed in order to evaluate the detection performance. We present expressions for the probably of detection and false alarm, and demonstrate the system numerically. Our main message is that coordinating the network to transmit in the uplink together with the UEs serves as an interesting approach in enabling radar sensing in CF massive MIMO systems.

Non-terrestrial networks (NTN) can provide connectivity in unreachable or remote areas. The massive multiple-input multiple-output (MIMO) is a promising architecture for future NTN networks through different platforms, such as earth orbit satellites or airborne vehicles. The long transmission distance and large coverage area challenge the physical layer design in a massive MIMO system. In particular, there is a clear trade-off between power amplifier (PA) efficiency and linearity: PAs are most efficient close to saturation, generating the most nonlinearities and degrading the achievable rate. In this paper, we study the power allocation and array selection in a distributed LoS massive MIMO system with maximum ratio transmission (MRT), by taking PA nonlinearity characteristics into account. With the objective to maximize the sum spectral efficiency (SE) with total power constraints, we first formulate the power allocation problem as nonlinear programming. Then, we propose an iterative power allocation algorithm based on the multiplier punitive method. Simulation results corroborate that the proposed power allocation can maximize spectral efficiency with awareness of the PA nonlinearity and significantly prevents it from degrading performance.

Multipath is a challenging problem for radar-based localization systems, especially in indoor scenarios. Multipath is caused by the bounces from static objects like walls and furniture in the room creating false alarms (“ghosts”) in target detections. Although solutions for the multipath effect have been proposed for a range of radar sensing problems, the specific case of multipath recognition and mitigation for a colocated multiple-input–multiple-output (MIMO) radar remains unsolved. For MIMO radar, the different direction-of-arrival (DoA) and direction-of-departure (DoD) angles inhibit the use of beamforming with a virtual array for localizing the first-order ghosts. Additionally, the prior knowledge of the multipath geometry model (room layout and boundary) is not always accessible. Classical ray tracing methods to resolve multipath are hence, not practical. In this work, we exploit a linear relationship between the target and multipath ghosts in the range-Doppler map to propose a Hough-transform-based multipath recognition solution. The algorithm does not require prior multipath geometry information and applies to the various indoor environments for an MIMO radar. Simulation and measurement results demonstrate the effectiveness of the proposed algorithm.

This paper considers the problem of radar sensing by using a large number of antennas. We use the orthogonal frequency division multiplexing (OFDM) waveform, and show that the large arrays used in massive multiple-input multiple-output (MIMO) communications enable accurate localization in the array near-field, even at the narrow bandwidths typically encountered at low carrier frequencies. We validate our findings experimentally with a massive MIMO testbed operating at 3.5 GHz carrier frequency and 18 MHz OFDM bandwidth in an indoor environment. We consider a single moving cylinder, and demonstrate a median accuracy of (3.4, 5.6) cm in ($x$,$y$) in the near-field. We show that the accuracy is maintained with only a single subcarrier, and that the resolution increases with an order of magnitude when combining all antennas, effectively surpassing the 16.67 m bistatic range resolution set by the OFDM waveform. We use a radar symbol duration of$71.88~\mu $s at an effective transmission period of 2.5 ms, which indicates that the radar and communication systems can be implemented in time-division with a capacity loss of only 2.9%. Our results suggest that near-field radar sensing can be integrated into future massive MIMO systems operating at low carrier frequencies and narrow bandwidths.

In this paper, we experimentally analyze the spatial distribution of nonlinear distortion in massive MIMO systems with various array topologies and user locations. With an indoor channel measurement, we reveal the spatial distortion distribution of the in-band (IB) and out-of-band (OOB) power leakage in a real-life scenario. We further investigate the power leakage under different antenna array topologies: including uniform linear array (ULA), uniform rectangular array (URA), distributed linear subarrays (DIS). The impact of user location on the per antenna distortion is also visualized. The results indicate that the DIS array configuration achieves the lowest in-band and out-of-band power leakage, which renders the distributed array a potential to reduce the linearity requirement of PAs when scaling up a practical massive MIMO system.

Massive MIMO is bringing significant performance improvements in the context of outdoor macrocells, such as spatially confined communication and high antenna gains to overcome pathloss. In this paper, we explore how these benefits scale to indoor scattering-rich deployments based on a dense indoor measured Massive MIMO dataset. First, we design and implement three different and relevant topologies to position our 64 antennas in the environment: Massive MIMO, RadioStripes and RadioWeaves topologies. Second, we measure 252004 indoor channels for a 3x3m2area for each topology, using an automated user positioning and measurement system. Using this dense dataset, we provide a unique analysis of system-level properties such as pathloss and spatial focusing. Our measurement-based analyses verify and quantify that distributing the antennas throughout the environment results in improved propagation fairness and higher spatial confinement. The dataset is publicly available and can serve as a reference database for benchmarking of future indoor communication systems and communication models. We outline the observed implementation challenges, and list diverse R&D challenges that can benefit from using this dataset.

We exploit a dense cell-free mmWave network where User Equipments (UEs) are served by multiple highly directional beams provided by multiple Base Stations (BSs) simultaneously. Such multi-beam scenarios can either offer high spectral efficiency when different information is transmitted through each beam or a diversity gain when each beam transmits the same information. However, this increased spectral efficiency or diversity gain costs a more complex network association phase. A UE requires finding multiple nearby serving BSs and determining the optimal beam pair for each one. Thus, an efficient association process is urgently needed. In this work, we propose a UE-initiated association method for dense cell-free mmWave networks. We design an efficient beam training mechanism with multiple BSs using hybrid beamforming. We evaluate the proposed association method under different network configurations. The simulation results show that compared to traditional solutions, our proposed association method can lead to maximally 100% faster beam training and reduce energy consumption by up to 77%. The proposed UE-initiated association method is also scalable to the number of RF chains and antennas at BSs and UEs, making it very suitable for dense cell-free networks.

In visible light communication (VLC), the quality of communication is primarily dominated by line-of-sight links. To ensure an appropriate link quality anywhere, beamsteering has been proposed where transmitters (TXs) dynamically steer their beams to create beamspots on the users. However, these highly dynamic TXs face the beam tracking problem and result in highly variable illumination. In this work, we propose BlendVLC, a cell-free network architecture to improve the mobility robustness of users by blending the beamspots from both steerable and fixed TXs. We solve the beam tracking by designing a centimeter-level visible light positioning algorithm empowered by a neural network. Relying on this location information, we formulate and solve an optimization problem on the beamspot blending, and design a fast and scalable heuristic for large networks. We build a proof-of-concept testbed as well as a simulator to evaluate BlendVLC. We show that it achieves superior performance compared to denser networks with fully fixed TXs. For example, in a large-scale VLC network of 8 m x 4 m, BlendVLC improves the average system throughput by 30%, while only requiring half the number of TXs.

Automatic Modulation Classification (AMC) is significant for the practical support of a plethora of emerging spectrum applications, such as Dynamic Spectrum Access (DSA) in 5G and beyond, resource allocation, jammer identification, intruder detection, and in general, automated interference analysis. Although a well-known problem, most of the existing AMC work has been done under the assumption that the classifier has prior knowledge about the signal and channel parameters. This paper shows that unknown signal and channel parameters significantly degrade the performance of two of the most popular research streams in modulation classification: expert feature-based and data-driven. By understanding why and where those methods fail, in such unknown scenarios, we propose two possible directions to make AMC more robust to signal shape transformations introduced by unknown signal and channel parameters. We show that Spatial Transformer Networks (STN) and Transfer Learning (TL) embedded into a light ResNeXt-based classifier can improve average classification accuracy up to 10-30% for specific unseen scenarios with only 5% labeled data for a large dataset of 20 complex higher-order modulations.

With the increasing use of wireless communication technologies, it is important to monitor electromagnetic exposure, ideally with high temporal and spatial resolutions. This paper presents our low-cost electro-smog measurement process, covering hardware selection, RF power measurement, and RF power correction. Then, a time series analysis is performed on the electromagnetic exposure data collected in the city of Sala Al Jadida - Morocco for seven days. The results show that the electro-smog exposure has a strong predictable pattern and a preliminary time series model is derived.

In this paper, we present a novel nonlinear digital self-interference canceller algorithm, its implementation details on a software-defined radio (SDR) platform, and performance results of real-time full-duplex experiments on both device and link level. The canceller algorithm is based on an augmented Hammerstein model, with a nonlinear part modeling the transmitter non-idealities followed by a linear filter to model the self-interference (SI) channel. The nonlinear part includes a spline-based model for the nonlinear power amplifier, a polynomial model for baseband nonlinearities, as well as models for I/Q mismatch and LO leakage. The canceller is implemented on an FPGA as a part of an OFDM transceiver testbed for real-time measurements. Extensive real-time measurements show excellent performance: (1) the digital canceller, together with an RF isolator, can suppress the SI to within 1-2 dB's of the receiver noise floor, with total SI suppression of up to 103 dB; (2) digital cancellation of up to 46 dB is evidenced, which is among the highest real-time cancellations in literature; (3) system-level measurements with OFDM signals demonstrate the benefit of utilizing the proposed canceller in a two-way communication scenario, showing up to 90 % increase in sum-rate compared to half-duplex communication.

In this article, we consider a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs coexist with the uplink (UL) of cellular ground users (GUEs). Our analysis focuses on comparing two spectrum sharing mechanisms: i) overlay, where the available time-frequency resources are split into orthogonal portions for U2U and GUE communications, and ii) underlay, where the same resources may be accessed by both link types, resulting in mutual interference. We evaluate the coverage probability and rate of all links and their interplay to identify the best spectrum sharing mechanism. Among other things, our results demonstrate that, in scenarios with a large number of UAV pairs, adopting overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance. We also find that increasing the density of U2U links degrades their rates in the overlay-where UAVs only receive interference from other UAVs-, but not significantly so in the underlay- where the effect of GUE-generated interference is dominant.

In this work, an indoor ultra-dense massive MIMO experiment is analysed to quantity the interference power that affects undesired users or victims when MR precoding is applied. To compare scenarios resulting in different favourable propagation conditions between users, the antennas are deployed as a Uniform Rectangular Array (URA), a Uniform Linear Array (ULA) and a Distributed Uniform Linear Array (D-ULA). We study scenarios where multiple users are served simultaneously. At the same time, the ultra-dense set of possible user locations is sub-sampled on a grid with a variable distance between users ranging from 50mm to 600mm. This work shows, on the one hand, that a URA antenna configuration provides the highest power to victim users and the worst power distribution towards target users. On the other hand, due to improved favourable propagation conditions for many user location pairs, the D-ULA topology reduces the power to victim users. Moreover, it guarantees that the power received by the target user will always be at least 3dB higher than any other victim user even if the users are closer as 100mm, for our analysis based on an indoor data-set and up to three target users.

Channel estimation is essential in massive MIMO systems. Pilot Contamination (PC) however, causes a major bottleneck in the acquisition of this information. The exploitation of the Angle of Arrival (AoA) provides multiple techniques for channel estimation under PC. However, many AoA estimation techniques require information on the signal statistics which is not available in dynamic scenarios. In this paper we propose and analyse the Matrix Pencil Method (MPM) to decorrelate contaminated channels based on their estimated AoA. We evaluate this method both through simulations and experiments in a real-life testbed. Our assessment focuses on a system with a Uniform Linear Array (ULA). The performance of the MPM is validated through simulations1with varying number of antennas, SNR and AoA difference. The results show that our approach effectively decorrelates the channels starting from 20 antennas and an SNR of 15 dB, which outperforms the theoretical expectation. This allows us to enhance the channel estimation quality under PC to the level of no PC. Real-life measurements confirm the simulated results. Our MPM implementation can achieve a target AoA estimation accuracy both with and without PC. We anticipate that the method can be extended for a Uniform Rectangular Array (URA).1We would like to thank NVIDIA for providing the GPU that was used to greatly accelerate our simulations.

We propose a concept of coverage-bounding and `visual' wireless communication-HODOR1-to secure the Internet of Things (IoT). Coverage-bounding means the communication coverage is controlled accurately in 3-dimensions. `Visual' implies that the communication coverage and process are visible to user, representing an important and user-friendly side-channel for se-curing IoT. HODOR can provide secure wireless communication both psychologically (visible to users) and technically (nodes only communicate with each other within their delimited coverage). It can benefit IoT applications for secure wireless communications, especially those that demand secure interactions in proximity.

Given the availability of lightweight radio and processing technology, it becomes feasible to imagine spectrum sensing systems using weather balloons. Such balloons navigate the airspace up to 40 km, and can provide a bird's eye and clear view of terrestrial, as well as aerial spectrum use. In this paper, we present SkySense, which is an extension of the Electrosense sensing framework with mobile GPS-located sensors and local data logging. In addition, we present 6 different sensing campaigns, targeting multiple terrestrial or aerial technologies such as ADS-B, AIS or LTE. For instance, for ADS-B, we can clearly conclude that the number of airplanes that are detected is the same for each balloon altitude, but the message reception rate decreases strongly with altitude because of collisions. For each sensing campaign, the dataset is described, and some example spectrum analysis results are presented. In addition, we analyse and quantify important trends visible when sensing from the sky, such as temperature and hardware variations, increased ambient interference levels, as well as hardware limitations of the lightweight system. A key challenge is the automatic gain control and dynamic range of the system, as a radio navigating over 30km, sees a very wide range of possible signal levels. All data is publicly available through the Electrosense framework, to encourage the spectrum sensing community to further analyse the data or motivate further measurement campaigns using weather balloons.

This paper studies the performance of a user positioning system using Channel State Information (CSI) of a Massive MIMO (MaMIMO) system. To infer the position of the user from the CSI, a Convolutional Neural Network is designed and evaluated through a novel dataset. This dataset contains indoor MaMIMO CSI measurements using three different antenna topologies, covering a 2.5 m by 2.5 m indoor area. We show that we can train a Convolutional Neural Network (CNN) model to estimate the position of a user inside this area with a mean error of less than half a wavelength. Moreover, once the model is trained on a given scenario and antenna topology, Transfer Learning is used to repurpose the acquired knowledge towards another scenario with significantly different antenna topology and configuration. Our results show that it is possible to further train the CNN using only a small amount of extra labelled samples for the new topology. This transfer learning approach is able to reach accurate results, paving the road to a practical CSI-based positioning system powered by CNNs.

Millimeter-wave is one of the technologies powering the new generation of wireless communication systems. To compensate the high path-loss, millimeter-wave devices need to use highly directional antennas. Consequently, beam misalignment causes strong performance degradation reducing the link throughput or even provoking a complete outage. Conventional solutions, e.g. IEEE 802.11ad, propose the usage of additional training sequences to track beam misalignment. These methods however introduce significant overhead especially in dynamic scenarios. In this paper we propose a beamforming scheme that can reduce this overhead. First, we propose an algorithm to design a codebook suitable for mobile scenarios. Secondly, we propose a blind beam tracking algorithm based on particle filter, which describes the angular position of the devices with a posterior density function constructed by particles. The proposed scheme reduces by more than 80% the overhead caused by additional training sequences.

In this paper, we present a fixed mmWave Multi-User Multiple-Input Multiple-Output (MIMO) system for fixed wireless access with a unique architecture. A digital MIMO system is combined with an analog multi-beam antenna array which uses a high-dimension 16×16 Butler matrix to obtain 16 orthogonal beams. A system model of this architecture is presented and used to simulate its performance comparing to the performance of common-used patch antennas. Several MIMO precoding techniques are considered and compared with basic analog beamforming. To verify these results, a prototype is built and a dedicated measurement campaign is performed. The results show that the system model is a good approximation and that the use of the multi-beam antenna array is a good alternative to patch antennas for a large number of users.

Collision detection (CD) is a key capability of carrier sense multiple access (CSMA) based medium access control (MAC) protocol. Applying CD, the transmitter can abort transmission immediately so that the power can be saved. This technique does not need the peer receiver to give feedback on whether there is a packet collision, and hence, the overall overhead is significantly low. The challenge, however, is to operate in transmit time and instantly detect the week colliding signal in the presence of strong self-interference (SI). This paper investigates two CD methods. The first technique trains a convolutional neural network (CNN) model which operates on raw baseband samples, without the need for pre-decoding. The second method treats the SI as a normal signal and estimates the signal to noise ratio (SNR): low SNR implies there is a collision because the pure SI is expected to have high SNR. Both models are evaluated by IEEE 802.15.4-like measured and simulated signals. The results show that collisions up to 30 dB below the SI signal can be detected precisely within 20 μs, while the proposed models can deliver acceptably low false alarm rate <; 1.5 %.

Localization is crucial for nowadays' communication systems, especially for beamforming techniques in massive MIMO systems. Large-scale MIMO systems have exhibited their advantages in communications. In the meantime, they also have the potential to provide accurate localization with their high angular resolution. In this paper, we study indoor localization performance of a Massive MIMO system with a 64-antenna Uniform Linear Array (ULA). Based on the sparse reconstruction method, we propose a Mixed field Sparse Bayesian Learning (MSBL) algorithm to localize devices for both near-field and far-field scenarios. Using the measurement results from our massive MIMO testbed, we show that our proposed MSBL algorithm can improve the localization accuracy by 49% with only a few snapshots. The performance of our algorithm is also robust to low Signal-to-Noise Ratio (SNR) conditions.

The Internet of Things (IoT) paradigm combines the interconnection of massive amounts of battery-constrained and low-computational-power devices with low-latency and high-reliability network requirements. Additionally, diverse end-to-end services and applications with different Quality of Service (QoS) requirements are expected to coexist in the same network infrastructure. Software-defined Networking (SDN) is a paradigm designed to solve these problems, but its implementation in wireless networks and especially in the resource-constrained IoT systems is extremely challenging and has seen very limited adoption, since it requires isolation of data and control plane information flows and a reliable and scalable control plane. In this work, Bluetooth Low Energy (BLE) mesh is introduced as an adequate technology for an all-wireless SDN-BLE implementation, which is a technology that has become the de-facto standard for IoT. The proposed SDN-BLE framework uses a routing network slice for the data plane information flow and a flooding network slice for the control plane information flow, ensuring their isolation while still being transmitted over the wireless medium. The design and implementation of all the classical SDN layers on a hybrid BLE mesh testbed is given, where the data plane is formed by the BLE nodes and the control plane can be centralized on a server or distributed over several WiFi gateways. Several controllers are described and implemented, allowing the framework to obtain end-to-end network knowledge to manage individual nodes over the air and configure their behavior to meet application requirements. An experimental characterization of the SDN-BLE framework is given, where the impact of the different parameters of the system on the network reliability, overhead, and energy consumption is studied. Additionally, the distributed versus centralized control plane operation modes are experimentally characterized, and it is shown that the distributed approach can provide the same performance as the centralized one when careful system design is performed. Finally, a proof of concept for the SDN-BLE framework is presented, where a network congestion is automatically detected and the nodes responsible of such congestion are identified and reconfigured over the air, bypassing the congested links, to resume regular network performance.

LED luminaries are now deployed densely in indoor areas to provide uniform illumination. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure. In this paper, we propose DenseVLC, a cell-free massive MIMO networking system enabled by densely distributed LEDs, that forms different beamspots to simultaneously serve multiple receivers. This is a cell-free system, as there is no notion of autonomous cells and transmitters cooperate to jointly serve the users. Given a power budget for communication, DenseVLC assigns the power budget among the distributed LEDs to optimize the system throughput and user fairness. We formulate an optimization problem to derive the optimal policy for the power allocation. Our insights from the optimal policies allow us to simplify DenseVLC's system design and propose a heuristic algorithm that can reduce the complexity by 99.96%. Besides, we propose a novel synchronization method using non-line-of-sight VLC to synchronize all the transmitters that will form a beamspot to serve the same receiver. We implement DenseVLC with off-the-shelf devices, solve practical challenges in the system design, and evaluate it with extensive and realistic experiments in a system of 36 transmitters and 4 receivers in an area of 3 m × 3 m. Our results show that DenseVLC can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, while maintaining the requirement for uniform illumination. Finally, we demonstrate that DenseVLC is robust against blockage.

We consider a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs share the same spectrum with the uplink (UL) of cellular ground users (GUEs). For this setup, we focus on analyzing and comparing the performance of two spectrum sharing mechanisms: (i) underlay, where the same time-frequency resources may be accessed by both UAVs and GUEs, resulting in mutual interference, and (ii) overlay, where the available resources are divided into orthogonal portions for U2U and GUE communications. We evaluate the coverage probability and rate of both link types and their interplay to identify the best spectrum sharing strategy. We do so through an analytical framework that embraces realistic height-dependent channel models, antenna patterns, and practical power control mechanisms. For the underlay, we find that although the presence of U2U direct communications may worsen the uplink performance of GUEs, such effect is limited as base stations receive the power-constrained UAV signals through their antenna sidelobes. In spite of this, our results lead us to conclude that in urban scenarios with a large number of UAV pairs, adopting an overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance.

A massive MIMO network can serve ten's of users simultaneously. However, in dense scenarios the users are potentially closely-spaced, potentially resulting in substantial inter-user interference. Scheduling can overcome this by selecting the users that lead to the highest combined spectral efficiency. As scheduling comes with a significant pilot overhead, an alternative strategy could minimize user correlation by distributing the antenna elements in space. In this paper, we propose a comprehensive system study including antenna topology and distribution, user scheduling and pilot overhead reduction. Our user scheduling and pilot reduction algorithms are evaluated using system level simulations relying on indoor line-of-sight channel measurements from a 64 antenna base station at 2.61GHz. To have a thorough evaluation of the proposed algorithm, we consider four different antenna topologies, including co-located and distributed placement of the base station arrays. Our evaluation shows that in a conference room with 64 densely deployed users, our proposed low complexity algorithm can improve the spectral efficiency by at least 14% compared to random user selection with the best antenna distribution strategy. Finally, our results show that by relying on channel hardening, we reduce the pilot overhead by 3.2$\times$ .

A practical partial multi-cell MMSE (PM-MMSE) combining vector for multi-cell massive MIMO systems is proposed in this work. This new scheme uses only inter-cell channels causing strong interference as partial inter-cell information in the multi-cell MMSE approach. The performance of PM-MMSE is evaluated using channels measured in a twocell outdoor experiment. In addition, a SIR threshold Γ is introduced as a tradeoff parameter between spectral efficiency and computational cost. For multi-cell scenarios, simulation results show that in a system with 16 cells and 320 users, PM-MMSE is capable of achieving 96% of the M-MMSE spectral efficiency using on average a tenth of inter-cell channel information. For this scenario, PM-MMSE requires only 60% of the total number of multiplications used by M-MMSE.

Wireless connectivity for the Internet of Things (IoT) requires combining low latency and high reliability with power and cost constraints. On top of that, diverse applications and end-to-end services with very different performance requirements should ideally share the same network infrastructure. Software defined networks (SDN) were defined to allow this coexistence, but they have seen very limited adoption in IoT systems due to the inherent resource constrained nature of the wireless devices. Additionally, the low-power nature of IoT communication standards does not usually allow for physically separated control and data channels, which is a basic requirement for real-time network slicing. In this work, we propose a SDN implementation for Bluetooth Low Energy (BLE), which has become the de-facto technology for IoT applications. The proposed BLE mesh structure is able to use different protocols for different services, ensuring a clear separation between control and data channels while still being sent over the wireless medium. A proof-of-concept for the proposed SDN implementation is given in a real BLE mesh testbed, where measurements show how the system is able to automatically detect and recover from network congestion by identifying the nodes responsible of such situation and reconfiguring their parameters over the air.

The use of in-band full duplex (IBFD) is a promising improvement over classical TDD or FDD communication schemes. To enable IBFD radios, the electrical balance duplexer (EBD) has been proposed to suppress the direct self-interference (SI) at the RF stage. The remaining SI is typically assumed to be canceled further in the digital domain. In this paper, we show that the non-zero Doppler frequencies can be extracted from the residual SI, giving information about the speed of objects in the environment. As a result, an IBFD radio can be seen as a monostatic Doppler radar which is affected by the communication signal. This paper presents a detailed performance analysis of the different sources of interference affecting the Doppler radar. The performance is also evaluated using a radar-enhanced IBFD prototype consisting of a SDR module and an EBD designed to achieve up to 55 dB Tx-Rx isolation in the 1.74 GHz RF band. A measurable Doppler component is created by moving a 15 dBsm cone at known velocities at 0. 5-1.3 m from the prototype. For an EBD SI rejection of 45 dB, speeds in the range of 200 to 800 mm/s are detected with high accuracy.

Reliable and direct communication between unmanned aerial vehicles (UAVs) could facilitate autonomous flight, collision avoidance, and cooperation in UAV swarms. In this paper, we consider UAV-to-UAV (U2U) communications underlaying a cellular network, where UAV transmit-receive pairs share the same spectrum with the uplink (UL) of cellular ground users (GUEs). We evaluate the performance of this setup through an analytical framework that embraces realistic height-dependent channel models, antenna patterns, and practical power control mechanisms. Our results demonstrate that, although the presence of U2U communications may worsen the performance of the GUEs, such effect is limited as base stations receive UAV interference through their antenna sidelobes. Moreover, we illustrate that the quality of all links degrades as the UAV height increases- due to a larger number of line-of-sight interferers-, and how the performance of the U2U links can be traded off against that of the GUEs by varying the UAV power control policy.

Multi-user MIMO at millimeter wave frequencies combines the huge bandwidth availability at millimeter wave with the spatial multiplexing gain of MIMO processing. Hybrid architectures have been proposed to reach this purpose while keeping implementation costs reasonably low. Active power splitter devices are the key components of a hybrid architecture enabling a lower implementation complexity compared to a full-digital solution. They distribute the same signal to several antenna elements shifting part of the precoding operations to the analog domain. Active power splitter devices however suffer from frequency distortion and their characterization is thus essential for new system designs. In this paper we propose a splitter model based on real hardware measurements showing that the splitter design critically affects the system performance. The frequency distortion influencing the digital channel estimation significantly distorts the precoded signal leading to an EVM floor in the high SNR region. However, when dynamically adapting the splitter operating point to control the trade-off between gain and bandwidth adaptively, the system EVM can be drastically reduced.

Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this article, we introduce a novel received signal strength indicator (RSSI)-based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPS-enabled sensors nodes (GSNs) in the network to split the wide coverage into classes, enabling RSSI-based fingerprinting of other sensors nodes (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.

The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network-level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased load-dependent co-channel interference received from neighboring ground base stations (BSs) is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 m can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network-based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase the coverage from 23% to 89% and throughput from 3.5 to 5.8 b/s/Hz, outperforming ground UEs. Furthermore, our findings reveal that depending on the UAV altitude and its antenna configuration, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of microcells limit the UAV performance. Although UAV usage has the potential to increase the area spectral efficiency (ASE) of cellular networks with a moderate number of cells, they might hamper the development of future ultradense networks.

status: Published

LED luminaries are now deployed densely in indoor areas to provide uniform illumination. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure. In this paper, we propose DenseVLC, a cell-free massive MIMO system enabled by densely distributed LEDs, that forms different beamspots to serve multiple receivers simultaneously. Given a power budget for communication, DenseVLC can optimize the system throughput by properly assigning the power budget among the distributed LEDs. We formulate an optimization problem to derive the optimal policy for the power allocation. Our insights from the optimal policies allow us to simplify DenseVLC's system design and propose a heuristic algorithm that can reduce the complexity by 99.96%. Besides, we propose a novel synchronization method using non-line-of-sight VLC to synchronize all the transmitters that will form a beamspot to serve the same receiver. We implement DenseVLC with off-the-shelf devices, solve practical challenges in the system design, and evaluate it with extensive and realistic experiments in a system of 36 transmitters and 4 receivers in an area of 3 m x 3 m. Our results show that DenseVLC can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, while maintaining the requirement for uniform illumination.

The use of aerial anchors for localizing terrestrial nodes has recently been recognized as a cost-effective, swift and flexible solution for better localization accuracy, providing localization services when the GPS is jammed or satellite reception is not possible. In this paper, the localization of terrestrial nodes when using mobile unmanned aerial vehicles (UAVs) as aerial anchors is presented. We propose a novel framework to derive localization error in urban areas. In contrast to the existing works, our framework includes height-dependent UAV to ground channel characteristics and a highly detailed UAV energy consumption model. This enables us to explore different trade-offs and optimize UAV trajectory for minimum localization error. In particular, we investigate the impact of UAV altitude, hovering time, number of waypoints and path length through formulating an energy-constrained optimization problem. Our results show that increasing the hovering time decreases the localization error considerably at the cost of a higher energy consumption. To keep the localization error below 100 m, shorter hovering is only possible when the path altitude and radius are optimized. For a constant hovering time of 5 seconds, tuning both parameters to their optimal values brings the localization error from 150m down to 65m with a power saving around 25% based on our simulation results.

This paper studies the feasibility of supporting drone operations using existent cellular infrastructure. We propose an analytical framework that includes the effects of base station (BS) height and antenna radiation pattern, drone antenna directivity and various propagation environments. With this framework, we derive an exact expression for the coverage probability of ground and drone users through a practical cell association strategy. Our results show that a carefully designed network can control the radiated interference that is received by the drones, and therefore guarantees a satisfactory quality of service. Moreover, as the network density grows the increasing level of interference can be partially managed by lowering the drone flying altitude. However, even at optimal conditions the drone coverage performance converges to zero considerably fast, suggesting that ultra-dense networks might be poor candidates for serving aerial users.

LED luminaries are densely deployed indoors to provide uniform illumination for better user comfort. To achieve energy efficient uniform illumination, the 'cells' of neighboring LED luminaries are strongly overlapping to increase the cell edge light intensity. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure by exploiting Distributed Multiple-Input-Single-Output (D- MISO). However, overlapping LED cells cause strong interference when multiple transmitters are active. Therefore, in this work, we propose an Adaptive and Distributed MISO (AD-MISO) method to improve the system throughput of a dense-LED-transmitter VLC network that communicates with multiple users. We formulate an optimization problem for deriving the D-MISO transmission strategy adapting the inter- cell interference dynamically, with the goal to increase the system throughput of multiple users. Given the measured link qualities between the distributed transmitters and users, AD-MISO exploits both spatial and time-division multiplexing by dynamically allocating the transmitters to multiple users such that the system throughput is maximized. We also design a heuristic algorithm to reduce the complexity of AD-MISO, and propose a technique to support heterogeneous service requirements. We evaluate the performance of AD-MISO through simulations under various scenarios. Our results demonstrate that AD-MISO outperforms the pure time-division based D-MISO greatly by increasing the average system throughput up to 35.6%, and our heuristic can achieve similar gains while reducing the complexity by 94%.

In this poster, we envision the promising cooperation between autonomous drones and driverless cars. We discuss potential applications and opportunities enabled by this cooperation.

This paper presents the concept of an UAV-mounted passive radar. Since the radar has no active transmitter and uses signals transmitted by illuminators of opportunity (IOO), it is a low cost, lightweight, low-power consuming solution perfectly fitting for mobile applications, especially for mounting on a UAV. Moreover, it does not require supplemental frequency allocation and creates no additional interference to existing wireless networks. Longterm evolution (LTE) is a good candidate for illuminators of opportunity (IOO) due to the fact that orthogonal frequency division multiplexing (OFDM) signals are used. Moreover, LTE base stations are widely deployed. In this paper, the detection performance of a drone-mounted passive radar is presented, with various settings in terms of targets, wireless propagation, realistic antenna patterns and signal processing.

Given the high throughput requirement for 5G, merging millimeter wave technologies and multi- user MIMO seems a very promising strategy. As hardware limitations impede to realize a full digital architecture, hybrid MIMO architectures using both analog and digital precoding are considered a feasible solution to implement multi- user MIMO at millimeter wave. Real channel propagation and hardware non-idealities degrade the performance of such systems thus experimenting the new architecture is crucial to support system design. Nevertheless, hybrid MIMO systems are not yet understood as the effects of the wide channel bandwidths at millimeter wave, the non-ideal RF front end as well as the imperfections of the analog beamforming using phased antenna arrays are often neglected. In this paper, we present a 60 GHz multi-user MIMO testbed using phased antenna arrays at both transmitter and receivers. The base station equipped with a 32 phased antenna array allocates simultaneously two users. We show that frequency selective hybrid precoding can efficiently suppress inter-user interference enabling spatial multiplexing in interference limited scenario doubling the throughput compared to a SISO scenario and compensating the frequency fluctuation of the channel. In addition, we report an EVM constellation improvement of 6 dB when comparing the hybrid MIMO architecture with a fully analog architecture.

Aerial base stations are a promising technology to increase the capabilities of existing communication networks. However, existing analytical frameworks do not sufficiently characterize the impact of ground interferers on aerial base stations. In order to address this issue, we model the effect of interference coming from coexisting ground networks on the aerial link, which could be the uplink of an aerial cell served by a drone base station. By considering a Poisson field of ground interferers, we characterize aggregate interference experienced by the drone. This result includes the effect of drone antenna pattern, the height-dependent shadowing, and various types of environment. We show that benefits a drone obtains from a better line-of-sight (LoS) at high altitudes is counteracted by a high vulnerability to the interference coming from ground. However, by deriving link coverage probability and transmission rate we show that a drone base station is still a promising technology if the overall system is properly dimensioned according to given density and transmission power of interferers. Particularly, our results illustrate how benefits of such network is maximized by defining the optimal drone altitude and signal-to-interference (SIR) requirement.

Providing low power and long range (LoRa) connectivity is the goal of most Internet of Things networks, e.g., LoRa, but keeping communication reliable is challenging. LoRa networks are vulnerable to the capture effect. Cell-edge nodes have a high chance of losing packets due to collisions, especially when high spreading factors (SFs) are used that increase time on air. Moreover, LoRa networks face the problem of scalability when they connect thousands of nodes that access the shared channels randomly. In this paper, we propose a new MAC layer-RS-LoRa-to improve reliability and scalability of LoRa wide-area networks (LoRaWANs). The key innovation is a two-step lightweight scheduling: 1) a gateway schedules nodes in a coarse-grained manner through dynamically specifying the allowed transmission powers and SFs on each channel and 2) based on the coarse-grained scheduling information, a node determines its own transmission power, SF, and when and on which channel to transmit. Through the proposed lightweight scheduling, nodes are divided into different groups, and within each group, nodes use similar transmission power to alleviate the capture effect. The nodes are also guided to select different SFs to increase the network reliability and scalability. We have implemented RS-LoRa in NS-3 and evaluated its performance through extensive simulations. Our results demonstrate the benefit of RS-LoRa over the legacy LoRaWAN, in terms of packet error ratio, throughput, and fairness. For instance, in a single-cell scenario with 1000 nodes, RS-LoRa can reduce the packet error ratio of the legacy LoRaWAN by nearly 20%.

The use of unmanned aerial vehicles (UAVs) serving as aerial base stations is expected to become predominant in the next decade. However, in order, for this technology, to unfold its full potential, it is necessary to develop a fundamental understanding of the distinctive features of air-to-ground (A2G) links. As a contribution in this direction, this paper proposes a generic framework for the analysis and optimization of the A2G systems. In contrast to the existing literature, this framework incorporates both height-dependent path loss exponent and small-scale fading, and unifies a widely used ground-to-ground channel model with that of A2G for the analysis of large-scale wireless networks. We derive analytical expressions for the optimal UAV height that minimizes the outage probability of an arbitrary A2G link. Moreover, our framework allows us to derive a height-dependent closed-form expression for the outage probability of an A2G cooperative communication network. Our results suggest that the optimal location of the UAVs with respect to the ground nodes does not change by the inclusion of ground relays. This enables interesting insights about the deployment of future A2G networks, as the system reliability could be adjusted dynamically by adding relaying nodes without requiring changes in the position of the corresponding UAVs. Finally, to optimize the network for multiple destinations, we derive an optimum altitude of the UAV for maximum coverage region by guaranteeing a minimum outage performance over the region.

Distributed massive multiple-input multiple-output (DM-MIMO) gives a higher spectral efficiency and enhanced coverage area, compared to collocated massive MIMO (CM-MIMO). In general, for massive MIMO, time division duplex is preferable as it enables downlink (DL) precoding based on uplink (UL) channel estimation. A time division duplex (TDD) reciprocity calibration is then essential to compensate the gap between the UL-DL channels, which can be done completely in the base station relying on sounding reference signals (SRS). For a collocated array, relying on mutual coupling between antenna elements, each SRS is received with sufficient power in the array, enabling a reliable estimate of the calibration coefficients. Nevertheless, for DM-MIMO, much less power is collected in distant inter-cluster antennas which degrades the accuracy of the estimated calibration. In this paper, we propose a novel inter-cluster combining method (ICCM) which improves the signal-to-quantization-noise ratio (SQNR) of the SRS, and hence achieves a more robust calibration accuracy for practical DM-MIMO systems. Our experimental results of two 32-antenna arrays distributed in an indoor environment show that ICCM outperforms the existing state-of-the-art algorithms in the sense of lower DL error vector magnitude (EVM) by exploiting diversity and array gain efficiently.

The growing demand for wireless connectivity has turned bandwidth into a scarce resource that has to be carefully managed and fairly distributed to users. However, the variability of the wireless channel can severely degrade the service received by each user. The Double Relay Communication Protocol (DRCP) [1] is a transmission scheme that addresses these problems by exploiting spatial diversity to enhance the fairness of the system without requiring any additional infrastructure (i.e relay nodes or a backhaul connection). Although DRCP has originally been proposed to work without channel state information at the transmitter (CSIT), in this paper we study how the performance of DRCP can be further improved through power control when CSIT is available. Our approach provides the highest fairness and the largest minimum spectral efficiency for most conditions compared to other studied baseline approaches.

Low power wide area networks are gaining interest to connect thousands of nodes to the internet of things. However, because the link budget in these networks is huge, nodes suffer from a near-far effect. Nodes far from the base station cannot send to the base station succesfully when closer nodes are transmitting, causing destructive collisions. LoRa, the considered technology in this paper, is a spread spectrum technology. It is known that spread spectrum is also sensitive to this effect. This paper presents a scheme to efficiently optimize the packet error rate fairness inside a LoRaWAN cell. This is achieved by optimizing the power and spreading factor for each node while avoiding near-far problems by allocating distant users to different channels. Simulations show that the packet error rate can be decreased up to 50% for edge nodes in a moderate contention scenario where 1 node per 1000m2transmits every 10 minutes.

Internet of things wireless networking with long-range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI has been used for fingerprinting localization where RSSI measurements of GPS anchor nodes have been used as landmarks to classify other nodes into one of the GPS nodes classes. Through measurements we show that a location classification accuracy of 100% is achieved when the classes of nodes are isolated. When classes are approaching each other, our measurements show that we can still achieve an accuracy of 85%. Furthermore, when the density of the GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus improve the possibility of localizing nodes with an error less than 20m from 20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes is localized with an error of less than 50m in our experiment with non-optimized anchor node locations.

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.

The use of drone base stations to provide wireless connectivity for ground terminals is becoming a promising part of future technologies. The design of such aerial networks is however different compared to cellular 2D networks, as antennas from the drones are looking down, and the channel model becomes height-dependent. In this paper, we study the effect of antenna patterns and height-dependent shadowing. We consider a random network topology to capture the effect of dynamic changes of the flying base stations. First we characterize the aggregate interference imposed by the co-channel neighboring drones. Then we derive the link coverage probability between a ground user and its associated drone base station. The result is used to obtain the optimum system parameters in terms of drones antenna beamwidth, density and altitude. We also derive the average LoS probability of the associated drone and show that it is a good approximation and simplification of the coverage probability in low altitudes up to 500 m according to the required signal-to-interference-plus-noise ratio (SINR).

Bluetooth Low Energy (BLE) represents the low-power, low-cost extension of the Bluetooth communication technology envisioned for the Internet of Things. Mesh protocols on top of BLE are currently emerging and the standard is currently being released. This paper first proposes a detailed measurement based comparison of two mesh approaches that fit within BLE operation: flooding and connection oriented networking. Using metrics such as packet delivery ratio (PDR), end-to-end delay and power consumption we conclude that the optimal mesh approach depends on the application. It is shown that for a comparable performance in terms of PDR and overhead, flooding can trade a lower end-to-end delay for a higher power consumption when compared to the connected mesh. We then propose an architecture, called Bluetooth Now, that is able to automatically switch the network between the two based on message priority. Our measurement results confirm the reliable delivery of important and urgent data sent using the Bluetooth Now paradigm, while saving battery life when transmitting non-time critical messages.

The densification of wireless networks that contend for a shared medium, demands improved MAC solutions that can reduce the energy cost of packet collisions. In this paper we analyze a novel in-band full duplex collision and interference detection scheme for dense networks, studying the energy savings that it can bring with respect to the performance of half duplex communications. Under a high external interference scenario, results show that the proposed full duplex scheme is more energy-efficient than half duplex transmissions for any network density. When the interference is low, the full duplex scheme provides energy gains when the number of contending devices is above a critical value. Expressions for calculating this critical number of devices are provided, showing that it is smaller when the likelihood of collisions increases. In the studied cases, results show the energy savings grow exponentially with the density of the network.

Aerial communication platforms have been recently recognized as an effective solution to provide wireless access to terrestrial users, which promise to increase reliability and throughput thanks to their superior coverage capabilities. In this paper, we explore the impact of the height of an Unmanned Aerial Vehicle (UAV) on the area over which it can provide wireless service. We investigate the problem by characterizing the coverage area for a target outage probability, showing that for the case of Rician fading there exist a unique optimum height that maximizes the coverage area. The optimum UAV height guarantees a beneficial trade-off between path loss and fading, which vary as function of distance and the elevation angle with respect to the ground terminals. Moreover, a closed-form approximated solution is provided, which is valid for any functional dependency between the elevation angle and the Rician factor.

Retransmissions due to decoding errors have a big impact on the energy budget of low-power wireless communication devices, which can be reduced by using hybrid automatic repeat request (HARQ) techniques. Nevertheless, this reduction comes at the cost of extra energy consumption introduced by the added computational load. No complete analysis of the tradeoff between retransmissions reduction and baseband consumption of low-power communications over fading channels has been reported so far. In this paper, we study the energy efficiency achievable by HARQ schemes when the code rate of the error-correcting code is optimized. For this purpose, we develop an energy consumption model that focuses on simple HARQ (S-HARQ) and Chase combining (HARQ-CC) transmissions, which are studied under fast-fading and block-fading scenarios with Nakagami-m fading. The retransmission statistics are analyzed, and expressions for the expected number of transmission trials are derived. Using this framework, it is shown that transmission schemes with high diversity gain are the most efficient choice for long range transmissions, which in our case correspond to HARQ-CC and codes with low code rate. On the other hand, schemes with good multiplexing capabilities are optimal for short link distances, which in our analysis correspond to S-HARQ and high code rates. It is also shown that HARQ-CC can effectively extend the transmission range of a low-power communication device.

This paper presents an energy-efficient digital signal processor (DSP) for rectangular-to-polar conversion in 802.11ad polar transmitter working on 60 GHz band. Firstly, system simulations with a complete transmission chain are conducted with regard to error vector magnitude and output spectrum, which allows to systematically optimize the design requirements on the DSP block. Secondly, algorithm and architecture co-optimization on the DSP block is explored to minimize the power consumption. Finally, the proposed DSP is synthesized using 28 nm CMOS technology, which provides a throughput of 7.04 Giga samples per second with a power consumption of 28 mW, and area of 0.01 mm2.

More and more wireless networks are deployed with overlapping coverage. Especially in the unlicensed bands, we see an increasing density of heterogeneous solutions, with very diverse technologies and application requirements. As a consequence, interference from heterogeneous sources-also called cross-technology interference-is a major problem causing an increase of packet error rate (PER) and decrease of quality of service (QoS), possibly leading to application failure. This issue is apparent, for example, when an IEEE 802.15.4 wireless sensor network coexists with an IEEE 802.11 wireless LAN, which is the focus of this work. One way to alleviate cross-technology interference is to avoid it in the frequency domain by selecting different channels. Different multichannel protocols suitable for frequency-domain interference avoidance have already been proposed in the literature. However, most of these protocols have only been investigated from the perspective of intratechnology interference. Within this work, we create an objective comparison of different candidate channel selection mechanisms based on a new multichannel protocol taxonomy using measurements in a real-life testbed. We assess different metrics for the most suitable mechanism using the same set of measurements as in the comparison study. Finally, we verify the operation of the best channel selection metric in a proof-of-concept implementation running on the testbed.

Full duplex wireless communication is a promising new technique that enables the simultaneous transmission and reception of a packet on the same frequency. Until now most research focused on proving the feasibility of full duplex Wi-Fi systems, focusing mainly on PHY layer analysis of the BER or PER. In this paper, the use of full duplex in wireless sensor networks is analysed, and it is shown that this can enable significant energy and delay gains, even when considering a realistic MAC protocol. This paper presents a novel full duplex energy model and MAC protocol for wireless sensor networks, compatible with realistic 802.15.4 chips and the standard MAC protocol. We show the potential of full duplex sensor networks, both for networks with low and high loads. Especially for high loads, full duplex enables a promising collision detection, avoiding wasting scarce resources in long packet collisions. Full duplex nodes outperform half duplex nodes both in terms of energy as in terms of delay, even in case of asymmetric traffic conditions. In addition, several advantages exist in terms of fairness of downlink traffic towards uplink traffic.

Lattice Reduction aided softoutput MIMO detectors have been demonstrated to offer a promising gain. However, computing Log-Likelihood ratios (LLR) for near-optimal MIMO detection, still poses a significant challenge for practical implementations. In this work, we present counter-ML bit-flipping algorithm for LLR generation. The proposed LLR generation algorithm has been designed to take advantage of the previously reported list generation algorithm, Multi-Tree Selective Spanning (MTSS), by maximizing the reuse of computations. Afterwards, a C-programmable MIMO detector architecture providing both data level parallelism (DLP) and instruction level parallelism (ILP), is designed for implementation. The proposed solution supports multiple MIMO detection modes, with both hard and softoutput. Performance of the proposed solution can be tuned ranging from SIC to near-ML to near-MAP, by adjusting a single parameter. In case of 4 × 4 QAM-64, it achieves peak-throughputs of 2.43Gbps and 629Mbps in case of hard and softoutput MIMO detection, with only 66.37mW and 76.14mW respective power consumption.

This paper presents an efficient mitigation approach for duty cycle mismatch of in-phase and quadrature upconversion signals in digital transmitters. This approach is supported by a mathematical analysis of the baseband equivalent impact of duty cycle mismatch. An efficient digital pre-distortion method is proposed to eliminate the distortion impact. Simulation results show that, for both 64-QAM and 256-QAM modulation schemes, the error-vector-magnitude can be improved from -25.1dB to less than -55dB, which leaves substantial design margin for other non-idealities distorting the transmitted signal.

The LTE cellular network is designed for meeting the requirements of a broad range of applications in very dynamic conditions. This explains its great flexibility in resource allocation. In order to provide the mobile stations with the exact required services, the base station relies on feedback information reported by each mobile station. In this paper, several feedback reduction schemes are analysed and compared, for a broad range of LTE resource allocation schemes and deployment scenarios, by means of simulation and a quantitative cost model. It is concluded that feedback reduction should be adapted to the scenario, as function of users, resource allocation strategy or channel properties, and up to 139% gain in overall throughput can be obtained by doing so.

Efficient resource allocation is necessary to provide the users with the quality of service promised in modern cellular networks, such as LTE. Traditional allocation methods make use of the smallest granularity available, the physical resource block (PRB), to assign resources to each user. The selected resources assigned to a user form a transport block (TB). However, the standard constrains the use of only one modulation and coding rate per TB. This forces the channel quality of the resources to be averaged across the TB, in order to determine the best modulation and coding scheme. In state-of-the-art systems, a non-linear heuristic is used in order to perform this averaging. Unfortunately, when bad PRBs are present next to good ones, this strategy is not optimal. We show that dropping the worst PRBs can improve the performance while remaining standard-compliant. We propose a simple algorithm that can be overlaid to any existing solution and we analyse its effect on multiple state-of-the-art schedulers. A gain is obtained both in throughput (up to 8% increase) and in power consumption (up to 23% reduction).

Harmonic rejection mixing is indispensable in software defined radio receivers employing switched mixers. Current analog multi-path mixing solution suffers from phase and gain mismatches along the paths and as a consequence cannot provide sufficient harmonic rejection. In this paper, we present a low complexity flexible digital intensive harmonic rejection architecture and show how it can be used to enhance the rejection of any single harmonic interference by adaptively combining the different mixing paths. Simulation results show that the proposed method can reject any single interferer adaptively by over 80 dB, which is sufficient for practical applications.

Hybrid ARQ (HARQ) combines Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) to exploit information from erroneous packets after retransmissions. Due to its superior reliability, HARQ became a crucial component of several important 3G and 4G systems such as Long Term Evolution (LTE) and LTE-Advanced. Although the performance advantage is very attractive, implementing HARQ is challenging with emerging high throughput communication systems. Knowing that LTE and LTE-A systems would provide Gbps or hundreds of Mbps, a straightforward implementation would require around 10 Mbit memory for a HARQ buffer. With cost and power-limited wireless terminals, the available memory size to support HARQ requires an efficient implementation. In this paper we propose a novel method to exploit the time coherence of the wireless channel in order to reduce the memory storage for HARQ. This method has been evaluated with a full LTE MIMO simulation chain. Compared to state-of-the-art solutions, we show a promising memory compression factor close to 4, whereas performance degradation is marginal.

In recent years, the algorithmic optimizations and implementations of near-optimal Multiple-Input Multiple-Output (MIMO) detectors have been an area of active research. Lattice Reduction (LR) has shown to be a promising technique to improve the performance of linear MIMO detectors. However, LR-aided linear hard-output MIMO detection is still far from optimal. Practical systems use soft-output information to exploit gains from coded systems in order to yield near-optimal performance. In this paper, the LR-aided Selective Spanning Sphere Detection algorithm is proposed as a reduced-complexity candidate list generation method for soft-output MIMO detection, specifically optimized for practical MIMO-OFDM systems. This algorithm uses efficient and scalable heuristics based on simple processor-friendly operations that significantly contribute to lowering the computational complexity of the MIMO detection problem. Results from Monte Carlo simulations reveal that LR-aided SSSD is a promising algorithm that is capable of providing near-optimal performance whilst being especially computationally efficient, in comparison to other algorithms.

Due to the advances in wireless technology and spectrum scarcity, unlicensed band heterogeneous networks are growing rapidly. Increasing users of these networks should compete for the shared spectrum. Therefore, interoperability and coexistence of such networks are becoming key issues that require novel media access protocols equipped with dynamic channel selection to avoid harmful interference. In this paper we focus on dynamic channel selection for coexistence of IEEE 802.11 Wireless LAN and IEEE 802.15.4 sensor networks. Dynamic channel selection algorithm can either be implemented on top of an existing wireless sensor network or assisted with an auxiliary spectrum sensing device. In this research couple of dynamic channel selection algorithms have been developed and implemented to evaluate the added value of the auxiliary sensing device. As such, we propose a novel energy-aware metric to detect and quantify the harmfulness of dynamic interference. We also investigated the impact of interference dynamism on algorithms performance and validated the efficiency of the implemented mechanisms by three sets of experiments. Experiments results primarily validate the efficiency of both interference mitigation techniques. Besides, these measurements suggest that the auxiliary sensing device is most beneficial for highly complex interference profiles.

Hybrid ARQ (HARQ) combines Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) to exploit information from erroneous packets after retransmissions. Due to its superior reliability, HARQ became a crucial component of several important 3G and 4G systems. Although the performance advantage is very attractive, implementing HARQ is challenging with emerging high throughput communication systems such as LTE and LTE-A. Specifically, a large amount of data needs to be stored whenever there is a retransmission. Knowing that LTE/LTE-A systems would provide Gbps or hundreds of Mbps, a straightforward implementation would require around 14 Mbit memory as HARQ buffer. With cost and power limited wireless terminals, the available memory size to support HARQ is a strict and challenging constraint. Hence, it is essential to find efficient techniques to minimize the memory footprint for storing erroneous packets with marginal or, preferably, no degradation. In the context of LTE systems, we propose a novel method to reduce the memory footprint for HARQ systems. This method has been evaluated with a fully fledged practical LTE simulation chain with MIMO transmissions. Compared to state-of-the-art solutions, we show a promising memory compression factor that is close to 5, whereas communication performance degradation is marginal.

We address the problem of detecting and locating narrowband tones in an undersampled signal. It is known that uniformly undersampled signals exhibit frequency aliasing, whereby the frequency location is impossible. To alleviate aliasing, non-uniform sampling can be used. This, however, generates a high level of frequency leakage that prevents detection of weaker signals. We introduce a novel iterative frequency excision technique that allows to detect tones below the original noise floor due to leakage. Up to 20dB of leakage reduction has been achieved with this method.

Lattice Reduction (LR) is a promising technique to improve the performance of linear MIMO detectors. In this paper the Scalable Block-based Parallel LR algorithm (SBP-LR) is proposed and optimized for parallel programmable baseband architectures offering ILP and DLP features. In our algorithm, architecture-friendliness is explicitly introduced from the very beginning of the algorithm/architecture co-design flow. In this context, abundant vector-parallelism is enabled with highly-regular and deterministic data-flow. Hence, SBP-LR can be easily parallelized and efficiently mapped on Software Defined Radio (SDR) baseband architectures. The proposed algorithm has been implemented on ADRES and is evaluated in the context of 3GPP LTE. Most of the previously reported algorithms are implemented for ASIC or FPGA. However, to the best of author's knowledge, this is the first reported LR algorithm explicitly optimized for a Coarse Grain Reconfigurable Array (CGRA) processor like ADRES.

In opportunistic spectrum access, radios sense their environment and adapt communication to utilize unused licensed spectrum without interfering with licensed users. A true cognitive radio, however, should do more than only access spectrum opportunistically. It should autonomously acquire and learn helpful new wireless information and use patterns. In this paper, we construct an algorithm, based on the use patterns in the licensed bands. It finds the optimal trade-off between buffering packets and switching channels, while guaranteeing delay demands. This algorithm is shown to outperform the benchmark solution by up to 50%.

The proposed method classifies traffic patterns of primary channels in cognitive radio systems and applies different prediction rules to different types of traffic. This allows a more accurate prediction of the idle times of primary channels. An intelligent channel selection scheme then uses the prediction results to find the channels with the longest idle times for secondary use. We tested the method with Pareto and exponentially distributed stochastic traffic and with deterministic traffic. The predictive method using past information improves the throughput of the system compared to a system based on instantaneous idle time information. The classification-based predictive method improves the performance compared to pure prediction when the channels of interest include both stochastic and deterministic traffic. The amount of collisions with a primary user can drop 60% within a given interval compared to a predictive system operating without classification.

We address the problem of co-channel interference (CCI) in wireless mesh networks (WMNs). In such networks, multiple nodes communicate concurrently using the same time/frequency resources. This is recognized as the interference channel (IFC) because the CCI is a major impairment in such a scenario. To mitigate the CCI, non-cooperative beamforming techniques can be employed. Non-cooperative beamformers are simpler to implement than their cooperative counter-parts because they require neither synchronization nor the sharing of information data between the transmit nodes. In this paper, we then propose a non-cooperative beamforming scheme to improve the performance of WMNs with frequency-selective channels. We present an iterative algorithm that maximizes the signal-to-interference-and-noise ratio criterion over all subcarriers of the orthogonal frequency division multiplexing system. The performance of this algorithm is evaluated through simulations.

Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and cross-layer optimization techniques designed to alleviate many causes of packet loss. A primary contributing factor is the difficulty in distinguishing between various causes of packet loss, including collisions caused by high network use, co-channel interference from neighboring networks, and errors due to poor channel conditions. In this paper, we propose a novel method for estimating various collision type probabilities locally at a given node of an 802.11 network. Our approach is based on combining locally observable quantities with information observed and broadcast by the access point (AP) in order to obtain partial spatial information about the network traffic. We provide a systematic assessment and definition of the different types of collision, and show how to approximate each of them using only local and AP information. Additionally, we show how to approximate the sensitivity of these probabilities to key related configuration parameters including carrier sense threshold and packet length. We verify our methods through NS-2 simulations, and characterize estimation accuracy of each of the considered collision types.

Achieving low energy consumption is one of the main challenges for wireless video transmission on battery-limited devices. Moreover, the bandwidth is scarce and must be shared efficiently among users. The focus in this paper is on the timely delivery of multiple delay-sensitive video flows over a distributed access wireless LAN with minimal energy cost. This is done taking into consideration the Enhanced Distributed Channel Access (EDCA) mode and the Scalable Video Codec (SVC). In this context, a method is presented for energy-efficient resource allocation across the physical layer and medium access layer, by properly leveraging transmission modes and the available prioritization mechanisms. Global energy savings around 60% are achieved with respect to state-of-the-art EDCA under a wide range of network loads.

Due to their ease of deployment, 802.11 networks are widely used. To cope with increasing throughput requirements, and to take advantage of improvements in hardware performance, 802.11n has been introduced. One of the extra features is the 20/40 functionality, which allows 802.1 In devices to operate using one or two 802.11 channels. In this paper, we study the contention rules for such 20/40 operation on top of the 802.11 Distributed Coordination Function (DCF). We then study the performance of double-channel users in presence of single-channel 802.11 users. Similar to the 802.11 model introduced by Bianchi, we introduce a Markov model for both the legacy 802.11 and double-channel 802.11n users. To couple the Markov models of both types of users, we have to understand how to relate virtual time slots of both types. Next, we introduce collision rules that capture traditional in-channel 802.11 collisions as well as collisions between the different types of users. This allows us to complete the analytical throughput model for 802.11 and double-channel 802.11n users when they coexist. The conclusions are important for understanding 802.11 networks in particular, but also for contention of heterogeneous devices in general.

We present a new scheme for maximizing the signal-to-noise ratio (SNR) in wideband multi-antenna receivers that employ time-interleaved analog-to-digital converters (ADCs). Current wideband receivers interleave a fixed number of slower ADCs into one fast ADC and assign this latter to one antenna according to an antenna selection algorithm. However, this does not always guarantee an optimal trade-off between thermal noise and quantization noise. This results in an overall SNR that is lower than what could be obtained with the same number of ADCs, assigned in a more optimal way. Therefore, we propose to adjust the number of slower ADCs assigned to a certain fast, interleaved ADC dynamically, according to the SNR of every individual antenna. Our proposed algorithm can be implemented at the expense of a very limited hardware complexity increase. The SNR gain of our new scheme can exceed 7 dB, depending on channel conditions and ADC specifications.

The success of dynamic spectrum access through simple listen-before-talk etiquettes has paved the way for opening up the spectrum. However, many problems still remain in these networks. Due to the complex nature of IEEE 802.11 networks, for instance, optimizing these networks regarding power, rate and carrier sense threshold remains a very tough challenge. In this paper, we introduce spatial learning. This new optimization algorithm for IEEE 802.11 networks employs learning to find an optimal combination of power, rate and carrier sense threshold. It is assumed that nodes behave selfishly and are only interested in optimizing their own throughput. Extensive network simulations show that spatial learning performs better than the state-of-the- art solution, spatial backoff, on all axes of interest: network-wide throughput, fairness and power consumption.

We adopt a machine learning approach towards the problem of identifying wireless systems present in a dynamic radio environment with heterogeneous usage. To classify the wireless systems, we utilize two features that typify spectrum use-center frequency and the frequency spread-and cluster the measurement data in this space. Since the systems are unknown prior to clustering, we use an unsupervised clustering method that uses the Chinese restaurant process implemented using Gibbs sampling. The system identification is divided into two parts: training and online classification. In the training phase, we assign wireless systems present in the surrounding to the clusters while the online classification uses this trained data to perform classification. By means of an extensive measurement campaign, we show that the proposed machine learning process achieves up to 90% correctness in classifying the wireless systems considered here.

This work comprehensively compares four possible multichannel medium access control (MAC) approaches for opportunistic spectrum access (OSA). One of important conclusions to be drawn from the analysis is that multichannel OSA MACs that spread both control and data between all available channels, e.g., hopping MACs, perform best among possible OSA MAC implementations when the PU traffic has long ON/OFF periods compared to the time-scale of the SU channel access.

The success of dynamic spectrum access through simple listen-before-talk etiquettes has made way for opening up the spectrum. However, many problems still remain in this kind of networks. Stations might not be able to sense as much transmissions and hence defer channel access less often than their neighbors. This can lead to unfairness or (worst-case) starvation of certain terminals. In this paper we model the throughput of a large-scale 802.11 network. Although the fairness issues in these networks are known, network modeling is still focusing on small-scale rigid networks. We want to open up this research toward large-scale randomly distributed topologies. A new model is developed to predict the long-term throughput of flows inside such a large-scale 802.11 network. Our model is validated through ns-2 simulations.

In this paper, we present a novel algorithm for distributed transmit beamforming enabling multiple single-antenna nodes to simultaneously transmit a common message such that they constructively interfere at the receiver. In order to constructively interfere, the nodes iteratively estimate and adjust their carrier phases with the help of receiver feedback. The receiver's feedback is based on composite channel estimates calculated from two simultaneous transmissions initiated by the nodes at each iteration. We prove that starting from arbitrary carrier phases, the algorithm converges to the maximum gain almost surely. The algorithm is compared to the 1-Bit Feedback algorithm presented in [3] through simulation, and is shown to outperform it by margins of 4:1 and 3:1 on metrics reflecting power consumption and convergence rate, respectively. We simulate the impact of phase estimation errors on the algorithm's steady-state gain and show it to be robust to moderately large errors of plusmn 20deg. We analyze the causes of asynchrony in simultaneous message transmissions and simulate its contribution to phase estimation errors in the context of IEEE 802.15.4 packets. We show that the contribution falls within the acceptable plusmn 20deg range.

The number of wireless networks that coexist in space is increasing steeply. To allow coexistence while avoiding interference, it becomes important to properly characterize the propagation contour where the received power of wireless transmitters reaches a certain threshold. Detailed channel modeling taking into account the specificities of typical urban scenarios is however a very complex task. Thanks to the widespread use of wireless access technology, it becomes feasible to use network nodes to estimate and communicate these propagation contours. In this paper, we propose a lightweight practical scheme for local contour estimation of a given transmitter. The local estimate is efficiently propagated to all other secondary transmitters that can then meet interference constraints optimally, i.e., without having to consider large safety margins that limit spatial reuse gains. This optimality is obtained through iterative power control based on true propagation contour distances and local pathloss estimates. The overhead of the estimation and communication phases is simulated to be close to linear in the number of nodes, so that the solution scales well. The scheme can be used for optimal power control in practical wireless networks, or for the deployment of secondary networks in areas with primary transmitters that should be protected.

Advances in low-power and low-cost sensor networks have led to solutions mature enough for use in a broad range of applications, requiring various degrees of reliability. To facilitate this, a broad range of options are possible to tune reliability, throughput or energy cost in the IEEE 802.15.4 standard defining the medium access control (MAC) and physical layer for sensor networks. Knowing how to tune those knobs however requires detailed models of the protocol behavior under different conditions. In our earlier work, we have proposed a very accurate model for the slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access scheme of the IEEE 802.15.4 standard for the unacknowledged transmission mode. Because of the design of the 802.15.4 carrier sensing mechanism, modeling the performance of the network in case of acknowledged transmissions is not a trivial extension. In this paper, we hence derive such model and illustrate through simulations that it is extremely accurate. Next, using the model, guidelines are derived to optimize the energy or throughput performance of sensor networks using the IEEE 802.15.4 standard.

Advances in low-power and low-cost sensor networks have led to solutions mature enough for use in a broad range of applications varying from health monitoring to building surveillance. The development of those applications has been stimulated by the finalization of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer for sensor networks. One of the MAC schemes proposed is slotted carrier sense multiple access with collision avoidance (CSMA/CA), and this paper analyzes whether this scheme meets the design constraints of those low-power and low-cost sensor networks. The paper provides a detailed analytical evaluation of its performance in a star topology network, for uplink and acknowledged uplink traffic. Both saturated and unsaturated periodic traffic scenarios are considered. The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF only in the use of a per user Markov model to capture the state of each user at each moment in time. The key assumptions to enable this important simplification and the coupling of the per user Markov models are however different, as a result of the very different designs of the 802.15.4 and 802.11 carrier sensing mechanisms. The performance predicted by the analytical model is very close to that obtained by simulation. Throughput and energy consumption analysis is then performed by using the model for a range of scenarios. Some design guidelines are derived to set the 802.15.4 parameters as function of the network requirements.

In many portable devices, wireless network interfaces consume upwards of 30% of scarce system energy. Reducing the transceiver's power consumption to extend the system lifetime has therefore become a design goal. Our work is targeted at this goal and is based on the following two observations. First, conventional energy management approaches have focused independently on minimizing the fixed energy cost (by shutdown) and on scalable energy costs (by leveraging, for example, the modulation, code-rate and transmission power). These two energy management approaches present a tradeoff. For example, lower modulation rates and transmission power minimize the variable energy component, but this shortens the sleep duration thereby increasing fixed energy consumption. Second, in order to meet the quality of service (QoS) timeliness requirements for multiple users, we need to determine to what extent each system in the network may sleep and scale. Therefore, we propose a two-phase methodology that resolves the sleep-scaling tradeoff across the physical, communications and link layers at design time and schedules nodes at runtime with near optimal energy-efficient configurations in the solution space. As a result, we are able to achieve very low run-time overheads. Our methodology is applied to a case study on delivering a guaranteed QoS for multiple users with MPEG-4 video over a slow-fading channel. By exploiting runtime controllable parameters of actual RF components and a modified 802.11 medium access controller, system lifetime is increased by a factor of 3-to-10 in comparison with conventional techniques.

Achieving low energy consumption is one of the main challenges for wireless video transmission on battery limited devices. Moreover, the bandwidth is scarce and needs to be properly shared amongst different users. Congestion in the network can result in packet losses, with a significant impact on video quality. In this paper we propose the use of a channel-adaptive rate control mechanism in a multi-user WLAN up-link scenario. The benefit is twofold: the communication energy is reduced and congestion is strongly alleviated allowing an increase of the video quality or a network capacity increase for a similar quality.

We consider the problem of packet scheduling for the transmission of multiple video streams over a wireless local area network (WLAN). A cross-layer optimization framework is proposed to minimize the wireless transceiver energy consumption while reaching the user required visual quality. The framework relies on the IEEE 802.11 standard and on a wavelet-based scalable video coding scheme. It extends our previous work on energy-efficient scheduling by introducing an application-level video quality metric as QoS constraint (instead of a quality metric at the level of the communication layers) and by reformulating the energy minimization problem subject to the QoS constraint in order to also consider the fairness among users. Simulation results demonstrate significant additional energy gains by means of these extensions.

In many portable devices, wireless network interfaces consume upwards of 30% of scarce system energy. Reducing the transceiver's power consumption to extend the system lifetime has therefore become a design goal. Our work is targeted at this goal and is based on the following two observations. First, conventional energy management approaches have focused independently on minimizing the fixed energy cost (by shutdown) and on scalable energy costs (by leveraging, for example, the modulation, code-rate and transmission power). These two energy management approaches present a tradeoff. For example, lower modulation rates and transmission power minimize the variable energy component, but this shortens the sleep duration thereby increasing fixed energy consumption. Second, in order to meet the quality of service (QoS) timeliness requirements for multiple users, we need to determine to what extent each system in the network may sleep and scale. Therefore, we propose a two-phase methodology that resolves the sleep-scaling tradeoff across the physical, communications and link layers at design time and schedules nodes at runtime with near optimal energy-efficient configurations in the solution space. As a result, we are able to achieve very low run-time overheads. Our methodology is applied to a case study on delivering a guaranteed QoS for multiple users with MPEG-4 video over a slow-fading channel. By exploiting runtime controllable parameters of actual RF components and a modified 802.11 medium access controller, system lifetime is increased by a factor of 3-to-10 in comparison with conventional techniques

The IEEE 802.15.4 standard defines the medium access control (MAC) and physical layer for sensor networks. One of the MAC schemes proposed is slotted carrier sense multiple access with collision avoidance (CSMA/CA), and this paper analyzes whether this scheme meets the design constraints of low-power and low-cost sensor networks. The paper provides a detailed analytical evaluation of its performance in a star topology network for both saturated and unsaturated periodic traffic.The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF only in the use of a per user Markov model to capture the state of each user at each moment in time. The key assumptions to enable this important simplification and the coupling of the per user Markov models are however different, as a result of the very different designs of the 802.15.4 and 802.11 carrier sensing mechanisms. The performance predicted by the analytical model is very close to that obtained by simulation. Throughput and energy consumption analysis is then performed and design guidelines are derived.

During the last decade, wireless communication has seen a trend towards application diversification leading to a significant growth in users. With the availability of - however energy-limited - nomadic devices and real-time multimedia applications, user demand is shifting from simply asking for higher data rates to more complex requirements in terms of Quality of Service (QoS) and energy-efficiency. In this new context energy management is becoming a key success factor. Optimized energy-efficiency requires an energy management that continuously trades off QoS and energy adapting to varying user expectations and environment dynamics. But, QoS can only be evaluated on top of the whole protocol stack while energy consumption largely appears at the lower layers. To minimize overhead during the transitions between layers, we need to address the problem from a cross-layer perspective. We present a methodology that, based on systematic exploration, effective problem partitioning and minimal cross-layer interface, allows energy management in a cross-layer way, while maintaining efficient layered semantics. Different case studies in the context of wireless LAN (WLAN) for multimedia and data traffic transport are discussed, to show how cross-layer energy management can easily be included in systems running state-of-the-art protocols.

Delay optimization is an important issue for ad hoc wireless networks supporting multimedia applications. Current medium access schemes do not take the application packet size into account for their operation. Long packets win the channel contention equally likely as the smaller packets; however, they occupy the channel for a longer time. This unfairness leads to increased delay and jitter for smaller packets. This paper attempts to resolve the above issue by proposing a fully distributed algorithm that adapts the contention process to the packet size as well. More specifically, we emulate the shortest job first scheduling policy by proposing a new resetting backoff regime. The proposed scheme is then compared to the distributed coordination function of the IEEE 802.11 standard. Analytical and simulation results show that the delay of the small packets is significantly improved.

In many devices, wireless network interfaces consume upwards of 30% of scarce portable system energy. Extending the system lifetime by minimizing communication power consumption has therefore become a priority. Conventional energy management techniques focus independently on minimizing the fixed energy consumption of the transceiver circuit or on scalable transmission control. Fixed energy consumption is reduced by maximizing the transceiver shutdown interval. In contrast, variable transmission rate, coding and power can be leveraged to minimize energy costs. These two energy management approaches present a tradeoff in minimizing the overall system energy. For example, variable energy costs are minimized by transmitting at a lower modulation rate and transmission power, but this also shortens the sleep duration thereby increasing fixed energy consumption. We present a methodology for energy-efficient resource allocation across the physical layer, communications layer and link layer. Our methodology is aimed at providing QoS for multiple users with bursty MPEG-4 video over a time-varying channel. We evaluate our scheme by exploiting control knobs of actual RF components over a modified IEEE 802.11 MAC. Our results indicate that the system lifetime is increased by a factor of 2 to 5 compared to the gains of conventional techniques.