This work presents a high-resolution, ray-tracing-based channel modeling for Low Earth Orbit (LEO) satellite-to-ground links in a suburban environment at X-band. Using simulations conducted in Wireless InSite, we develop a parametric channel model that characterizes both large- and small-scale fading effects across different satellite elevation angles. Large-scale fading incorporates attenuation due to terrain-induced shadowing and dynamic environmental factors such as weather conditions, and is compared with 3GPP NTN channel model. Additionally, we quantify link degradation resulting from ground station (GS) antenna misalignment, considering both fixed single-element and electronically steerable phased-array antennas. Small-scale fading is modeled by fitting a shadowed and non-shadowed Rician distribution to the fading statistics at various satellite elevations. To the best of our knowledge, this is the first study to propose a comprehensive elevation-aware channel model for satellite-to-ground propagation at X-band, integrating ray-traced environmental dynamics, elevation-dependent fading, and phased-array beam misalignment effects.

Effective spectrum sharing requires a nuanced understanding of how radio activity varies not only across frequency and geography but also with altitude, an increasingly important dimension for drone networks and non-terrestrial systems. This paper presents a comprehensive survey of existing spectrum measurement studies in the sub-6 GHz range, identifying major gaps in coverage, such as limited measurements involving altitude variation, a lack of longitudinal datasets, and the absence of publicly available geotagged aerial data. To address these limitations, we conduct a multi-year spectrum monitoring campaign using an aerial platform equipped with software-defined radios (SDRs), operating in both urban and rural areas of Raleigh, NC, USA. The aerial platform enables high-resolution measurements of spectrum activity from the ground up to 300 m. We analyze occupancy patterns across licensed and unlicensed bands, including Long Term Evolution (LTE), 5G (New Radio) (NR), and Industrial, Scientific, and Medical (ISM) bands, and document year-over-year changes that reflect evolving commercial deployments and regulatory trends. We further perform an altitude-dependent analysis of spectrum occupancy and received power across the 89 MHz–6 GHz range, highlighting how three-dimensional measurement perspectives reveal usage patterns that are not observable from ground-level sensing alone. Our results demonstrate that altitude is a critical dimension for spectrum characterization and should be considered in future spectrum policy, sensing strategies, and system design. This study provides both a literature-based and measurement-based foundation for altitude-aware spectrum analytics, supported by a publicly available geotagged dataset to enable reproducible research for emerging aerial networks and non-terrestrial communication systems.

This article investigates the feasibility of 5G ultra-reliable low-latency communication (URLLC) for wide-area protection in distribution systems with distributed energy resources (DER). Simulations conducted using ns-3 demonstrate that URLLC can meet the stringent latency requirements of wide-area protection, even under mixed traffic conditions. Based on these findings, experiments are conducted on the platform for open wireless data-driven experimental research (POWDER) platform, using commercial off-the-shelf (COTS) hardware and open-source software stacks. Long-term wireless experiments on POWDER evaluate the impact of network slicing and 5G quality of service identifiers on the network performance. The behavior of best-effort users when sharing the same infrastructure as URLLC UEs is studied. Based on these experiments, significant discrepancies between simulation and hardware implementation performance are identified and analyzed. Finally, critical measures are discussed to align 5G network performance with the requirements of wide-area protection.

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

The growing demand for unmanned aerial vehicle (UAV) based aerial connectivity across urban and rural environments highlights the need for a deeper understanding of spectrum occupancy in aerial corridors. In particular, understanding the altitude-dependent behavior of spectrum occupancy in cellular networks is critical, as such understanding could be used to enable beyond visual line of sight (BVLOS) UAV connectivity in the future. While there are existing models for characterizing altitude-dependent interference in the literature, they are not validated by data and need to be compared with real-world measurements. To address these gaps, in this paper, we conduct cellular spectrum occupancy measurements at various sub-6 GHz bands for altitudes up to 300 meters, in both urban and rural environments. To characterize the real-world spectrum occupancy behavior, we introduce two different approaches: a new theoretical model utilizing stochastic geometry with altitude-dependent factors (SOSGAD), and a ray-tracing model tailored to site-specific line of sight (LOS) and non-LOS scenarios. We also derive the asymptotic behavior of the SOSGAD model as the UAV altitude increases. Through comparative analysis, we evaluate how well the SOSGAD and ray-tracing models characterize altitude-dependent spectrum occupancy in both uplink and downlink scenarios.

We focus on handover management in networks integrating traditional terrestrial ground base stations (GBSs) and non-terrestrial unmanned aerial vehicles (UAVs) serving users. In such scenario, we propose a joint management of handover of users equipment (UEs) between UAVs and GBSs as well as handover of UAVs between GBSs. Our goal is to maximize the sum capacity for UEs while avoiding redundant handovers. As an impact of handover on the future network performance is not explicit, we adopt deep deterministic policy gradient (DDPG). Furthermore, since the UAVs are usually energy constrained, we consider an energy efficient transparent relaying mode for the UAVs. However, in the transparent relaying mode, the access channel quality between the UAV relay and the UE is unknown even if such information is essential for handover. Thus, we further employ deep neural network (DNN) to predict the access channel quality. An incorporation of DDPG for handover management together with DNN for channel quality prediction can impair the sum capacity of the UEs due to an accumulation of inherent small prediction errors of DNN and DDPG. Hence, we also introduce a coordination between DNN and DDPG to suppress the accumulation of the prediction errors. Simulations demonstrate that the proposed handover management and the coordination of DDPG and DNN increase the sum capacity by up to 63% while notably reducing the number of handovers and handover failure ratio compared to the state-of-the-art works.

This work presents the results of measurements conducted on small drones and a bionic bird using a 60 GHz millimeter wave radar, analyzing their micro-Doppler characteristics in both time and frequency domains. In particular, we focus on their distinct nature of movement, i.e., rotating propellers and flapping wings, rather than relying on their materials. The time-series measurements show comparable differences in the phase of the samples as a result of micro-Doppler effects. Utilizing the collected measurement data, we develop neural network models to accurately differentiate between bionic birds and drones, having a significant potential for application in airports where precise object identification is essential. We adopt a convolutional neural network for detecting changes in the amplitude values and a convolutional long- and short-term memory for identifying the phase difference between the drone and bird signatures. The results reveal that distinguishing between small drones and birds can be done based on the phase difference of the scattered radar signals, even with a high noise variance.

Millimeter-wave (mmWave) networks, integral to 5G communication, offer a vast spectrum that addresses the issue of spectrum scarcity and enhances peak rate and capacity. However, their dense deployment, necessary to counteract propagation losses, leads to high power consumption. An effective strategy to reduce this energy consumption in mobile networks is the sleep mode optimization (SMO) of base stations (BSs). In this paper, we propose a novel SMO approach for mmWave BSs in a 3D urban environment. This approach, which incorporates a neural network (NN) based contextual multi-armed bandit (C-MAB) with an epsilon decay algorithm, accommodates the dynamic and diverse traffic of user equipment (UE) by clustering the UEs in their respective tracking areas (TAs). Our strategy includes beamforming, which helps reduce energy consumption from the UE side, while SMO minimizes energy use from the BS perspective. We extended our investigation to include Random, Epsilon Greedy, Upper Confidence Bound (UCB), and Load Based sleep mode (SM) strategies. We compared the performance of our proposed C-MAB based SM algorithm with those of All On and other alternative approaches. Simulation results show that our proposed method outperforms all other SM strategies in terms of the 10thpercentile of user rate and average throughput while demonstrating comparable average throughput to the All On approach. Importantly, it outperforms all approaches in terms of energy efficiency (EE).

The rapid evolution of 5G and beyond has advanced space-air-terrestrial networks, with unmanned aerial vehicles (UAVs) offering enhanced coverage, flexible configurations, and cost efficiency. However, deploying UAV-based systems presents challenges including varying propagation conditions and hardware limitations. While simulators and theoretical models have been developed, real-world experimentation is critically important to validate the research. Digital twins, virtual replicas of physical systems, enable emulation that bridge theory and practice. This paper presents our experimental results from AERPAW’s digital twin, showcasing its ability to simulate UAV communication scenarios and providing insights into system performance and reliability.

In 5G and beyond wireless technologies, Ultra Reliable Low Latency Communication (URLLC) is expected to accommodate several mission-critical use cases, such as smart grid protection. Due to smart grid traffic’s high cost and nondeterministic nature, they cannot be operated on dedicated channels. Instead, carrying out these critical communications in a shared network with other cellular users would be more desirable for maximum resource efficiency. This paper investigates the performance of mission-critical users and other cellular users when operated on shared infrastructure. The paper assesses if 5G URLLC can meet the latency requirements of smart grid protection applications, especially if the resource spectrum is shared among other cellular users in heterogeneous traffic. An open-source tool, ns-3, is used to perform realistic stochastic simulations of a 5G-based communication system. This paper evaluates a wide-area protection application for a distribution system integrated with large Distributed Energy Resources (DERs). The test results demonstrate that mission-critical applications such as smart grid protection can co-exist with other cellular users, provided careful design considerations are taken to ensure the performance requirements of all users in the network are met satisfactorily.

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV’s goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize interference to the ground user equipment (UEs) connected to neighboring cellular base stations (BSs), considering both the shortest path and limitations on flight resources. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach . Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations.

Advancements in unmanned aerial vehicle (UAV) technology have led to their increased utilization in various commercial and military applications. One such application is signal source search and localization (SSSL) using UAVs, which offers significant benefits over traditional ground-based methods due to improved RF signal reception at higher altitudes and inherent autonomous 3D navigation capabilities. Nevertheless, practical considerations such as propagation models and antenna patterns are frequently neglected in simulation-based studies in the literature. In this work, we address these limitations by using a two-ray channel model and a dipole antenna pattern to develop a simulator that more closely represents real-world radio signal strength (RSS) observations at a UAV. We then examine and compare the performance of previously proposed linear least square (LLS) based localization techniques using UAVs for SSSL. Localization of radio frequency (RF) signal sources is assessed based on two main criteria: 1) achieving the highest possible accuracy and 2) localizing the target as quickly as possible with reasonable accuracy. Various mission types, such as those requiring precise localization like identifying hostile troops, and those demanding rapid localization like search and rescue operations during disasters, have been previously investigated. In this paper, the efficacy of the proposed localization approaches is examined based on these two main localization requirements through computer simulations.

The ever-growing demand for wireless connectivity, coupled with limited spectrum resources, has resulted in spectrum congestion and interference. This research investigates the probability of occupancy in common sub-6 GHz cellular network bands based on measurements conducted in urban and rural environments. Specifically, we analyze the spectrum occupancy of various long-term evolution (LTE), 5thgeneration (5G) and Citizens Broadband Radio Service (CBRS) bands used in the United States, considering both uplink and downlink transmissions at altitudes up to 180 meters. Additionally, we explore the influence of altitude on the probability of spectrum occupancy in these bands. Our findings reveal that the probability of occupancy is generally higher in the downlink compared to the uplink. Moreover, we observe that line-of-sight (LoS) signals play a critical role in higher altitudes. These results provide insights spectrum utilization in various cellular bands across different altitudes, with implications on interference and spectrum coexistence between terrestrial networks and unmanned aerial systems (UASs) in the future.

The emerging 5G and future 6G technologies are envisioned to provide higher bandwidths and coverage using millimeter wave (mmWave) and sub-Terahertz (THz) frequency bands. The growing demand for higher data rates using these bands can be addressed by overcoming high path loss, especially for non-line-of-sight (NLOS) scenarios. In this work, we investigate the use of passive transparent reflectors to improve signal coverage in an NLOS indoor scenario. Measurements are conducted to characterize the maximum reflectivity property of the transparent reflector using channel sounder equipment from NI. Flat and curved reflectors, each with a size of 16 inches by 16 inches, are used to study coverage improvements with different reflector shapes and orientations. The measurement results using passive metallic reflectors are also compared with the ray-tracing-based simulations, to further corroborate our inferences. The analysis reveals that the transparent reflector outperforms the metal reflector and increases the radio propagation coverage in all three frequencies of interest: 28 GHz, 39 GHz, and 120 GHz. Using transparent reflectors, there is an increase in peak received power that is greater than 5 dB for certain scenarios compared to metallic reflectors when used in flat mode, and greater than 3 dB when used in curved (convex) mode.

Localization of a radio frequency (RF) signal source has various use cases, ranging from search and rescue, identification and deactivation of jammers, and tracking hostile activity near borders or on the battlefield. The use of unmanned aerial vehicles (UAVs) for signal source search and localization (SSSL) can have significant advantages when compared to terrestrial-based approaches, due to the ease of capturing RF signals at higher altitudes and the autonomous 3D navigation capabilities of UAVs. However, the limited flight duration of UAVs due to battery constraints, as well as limited computational resources on board of lightweight UAVs introduce challenges for SSSL. In this paper, we study various SSSL techniques using a UAV with predefined waypoints.A linear least square (LLS) based localization scheme is considered with enhanced reference selection due to its relatively lower computational complexity. Five different LLS localization algorithms are proposed and studied for selecting anchor positions to be used for localization as the UAV navigates through an area. The performance of each algorithm is measured in two ways: 1) real-time positioning accuracy during the ongoing UAV flight, and 2) long-term accuracy measured at the end of the UAV flight. We compare and analyze the performance of the proposed approaches using computer simulations in terms of accuracy, UAV flight distance, and reliability.

Channel rank and condition number of multi-input multi-output (MIMO) channels can be effective indicators of achievable rates with spatial multiplexing in mobile networks. In this paper, we use extensive ray tracing simulations to investigate channel rank, condition number, and signal coverage distribution for air-to-ground MIMO channels. We consider UAV-based user equipment (UE) at altitudes of 3 m, 30 m, 70 m, and 110 m from the ground. Moreover, we also consider their communication link with a cellular base station in urban and rural areas. In particular, Centennial Campus and Lake Wheeler Road Field Labs of NC State University are considered, and their geographical information extracted from the open street map (OSM) database is incorporated into ray tracing simulations. Our results characterize how the channel rank tends to reduce as a function of UAV altitude, while also providing insights into the effects of geography, building distribution, and threshold parameters on channel rank and condition number.

In this paper, we consider a radar network for sensing ground targets where radars mounted on unmanned aerial vehicles (UAVs) that fly at a certain altitude are randomly distributed according to a two-dimensional homogeneous Poisson point process (HPPP). For such a sensing network, we derive the distribution of the radar interference using a stochastic geometry based analysis. In particular, when Swerling I model is considered for radar cross-section area (RCS) for the target, we derive the Laplace transform of the radar interference. Here, to avoid a strong interference between neighboring radars, a guard zone is introduced within which the UAV radar transmission around the permitted active radar is inhibited. As the radar performance metric, we derive the successful ranging probability (SRP) of a given radar by exploiting the Laplace transform of radar interference. Using our analytical results, which are verified by computer simulations, we argue that the radar network design parameters such as the radius of the guard zone and the density of the active radars can be optimized.

Due to the scarcity of spectrum resources, the emergence of new technologies and ever-increasing number of wireless devices operating in the radio frequency spectrum lead to data congestion and interference. In this work, we study the effect of altitude on sub-6 GHz spectrum measurement results obtained at a Helikite flying over two distinct scenarios; i.e., urban and rural environments. Specifically, we aim at investigating the spectrum occupancy of various long-term evolution (LTE), 5thgeneration (5G) and citizens broadband radio service (CBRS) bands utilized in the United States for both uplink and downlink at altitudes up to 180 meters. Our results reveal that generally the mean value of the measured power increases as the altitude increases where the line-of-sight links with nearby base stations is more available. SigMF-compliant spectrum measurement datasets used in this paper covering all the bands between 100 MHz to 6 GHz are also provided.

Unmanned aerial vehicles (UAVs) are expected to be used extensively in the future for various applications, either as user equipment (UEs) connected to a cellular wireless network, or as an infrastructure extension of an existing wireless network to serve other UEs. Next generation wireless networks will consider the use of UAVs for joint communication and radar and/or as dedicated radars for various sensing applications. Increasing number of UAVs will naturally result in larger number of communication and/or radar links that may cause interference to nearby networks, exacerbated further by the higher likelihood of line-of-sight signal propagation from UAVs even to distant receivers. With all these, it is critical to study network coexistence of UAV-mounted base stations (BSs) and radar transceivers. In this paper, using stochastic geometry, we derive closed-form expressions to characterize the performance of coexisting UAV radar and communication networks for spectrum overlay multiple access (SOMA) and time-division multiple access (TDMA). We evaluate successful ranging probability (SRP) and the transmission capacity (TC) and compare the performance of TDMA and SOMA. Our results show that SOMA can outperform TDMA on both SRP and TC when the node density of active UAV-radars is larger than the node density of UAV-comms.

Radio dynamic zones (RDZs) are geographically bounded areas where novel advanced wireless technologies can be developed, tested, and improved, without the concern of interfering to other incumbent radio technologies nearby the RDZ. In order to operate an RDZ, use of a real-time spectrum monitoring system carries critical importance. Such a monitoring system should detect out-of-zone (OoZ) signal leakage outside of the RDZ, and if the interference to nearby receivers is intolerable, the monitoring system should be capable of mitigating such interference. This can e.g. be achieved by stopping operations inside the RDZ or switching to other bands for RDZ operation. In this paper, we introduce a spectrum monitoring concept for OoZ signal leakage detection at RDZs, where sensor nodes (SNs) are installed at the boundary of an RDZ and monitor the power leakage from multiple transmitters within the RDZ. We propose a prediction algorithm that estimates the received interference at OoZ geographical locations outside of the RDZ, using the measurements obtained at sparsely located SNs at the RDZ boundary. Using computer simulations, we evaluate the performance of the proposed algorithm and study its sensitivity to SN deployment density.

The deployment at scale of Unmanned Aerial Systems have become increasingly imminent in the last few years, even as concerns regarding the dependability and predictability of their command and control channels remain fully to be addressed. The intersection of ground-to-air wireless communications, aerial networking, and trajectory control has become a research area of sharp interest. The validation of such research, beyond the theoretical/simulation stage, requires a facility that is both realistic, and admits of potentially risky or unsafe operation, while in the end guaranteeing personnel and equipment safety. The AERPAW project is an ambitious project, funded by the PAWR program of the US NSF, to create a remote accessible research platform for a research facility to enable such validation. To enable remote usage of such a testbed, yet provide the researcher with complete experimental freedom, the AERPAW facility includes a combination of architectural mechanisms that balance freedom of experimentation with regulatory compliance and safety. In this paper, we articulate the challenges and considerations of designing such mechanisms, and present the architectural features of AERPAW that attempt to realize these lofty goals.

The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi networks are critical to provide internet access in today’s world. The increasing demand for high data rate in Wi-Fi networks has led to several advancements in the 802.11 standard. Supporting MIMO transmissions with higher number of transmit antennas operating on wider bandwidths is one of the key capabilities for reaching higher throughput. However, the increase in sounding feedback overhead due to higher number of transmit antennas may significantly curb the throughput gain. In this paper, we develop an unsupervised learning-based method to reduce the sounding duration in a Wi-Fi MIMO link. Simulation results show that our method uses approximately only 8% of the number of bits required by the existing feedback mechanism and it can boost the system throughput by up to 52%.

International mobile subscriber identity (IMSI) sharing-based aggregated communication aims to connect multiple Internet of Things (IoT) devices to the mobile operator’s core network over the same subscriber line. IoT devices with low data rates and long data sending intervals are first grouped together and assigned the same subscriber identity. They then take turns to perform their data exchanges using the same cellular connection, yielding huge savings in resource (e.g., number of active bearers) usage. Current solutions however do not consider different device traffic characteristics, the flexibility in traffic patterns, and dynamic network environments where new IoT devices join and existing ones leave the network. In this article, we study the problem of the grouping of IoT devices that will share the same subscriber identity based on their traffic patterns which can also be slightly shifted. We also study the efficient regrouping of these devices as the set of devices in the network changes. We first solve the optimal grouping and traffic aggregation problem for the initial and updated network states using integer linear programming (ILP). Then, to avoid the high complexity of ILP solutions, we develop heuristic-based solutions. Through extensive simulations, we show that heuristic-based algorithms can provide close to optimal ILP-based results while running much faster. The results also show that shifting-based grouping provides more resource saving compared to no-shifting-based aggregation and the proposed solution for dynamic environments can maintain the resource saving with a much lower complexity.

We investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

For reliable and efficient communications of aerial platforms, such as unmanned aerial vehicles (UAVs), the cellular network is envisioned to provide connectivity for the aerial and ground user equipment (GUE) simultaneously, which brings challenges to the existing pattern of the base station (BS) tailored for ground-level services. Thus, we focus on the coverage probability analysis to investigate the coexistence of aerial and terrestrial users, by employing realistic antenna and channel models reported in the 3rd Generation Partnership Project (3GPP). The homogeneous Poisson point process (PPP) is used to describe the BS distribution, and the BS antenna is adjustable in the down-tilted angle and the number of the antenna array. Meantime, omnidirectional antennas are used for cellular users. We first derive the approximation of coverage probability and then conduct numerous simulations to evaluate the impacts of antenna numbers, down-tilted angles, carrier frequencies, and user heights. One of the essential findings indicates that the coverage probabilities of high-altitude users become less sensitive to the down-tilted angle. Moreover, we found that the aerial user equipment (AUE) in a certain range of heights can achieve the same or better coverage probability than that of GUE, which provides an insight into the effective deployment of cellular-connected aerial communications.

Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks. In this paper, we consider the authentication technique that uses fingerprint embedding to defend 5G cellular networks with unmanned aerial vehicle (UAV) systems from eavesdroppers and intruders. Since the millimeter wave (mmWave) cellular networks use narrow and directional beams, PLS can take further advantage of the 3D spatial dimension for improving the authentication of UAV users. Considering a multi-user mmWave cellular network, we propose a power allocation technique that jointly takes into account splitting of the transmit power between the precoder and the authentication tag, which manages both the secrecy as well as the achievable rate. Our results show that we can obtain optimal achievable rate with expected secrecy.

In the upcoming 5G communication, the millimeter-wave (mmWave) technology will play an important role due to its large bandwidth and high data rate. However, mmWave frequencies have higher free-space path loss (FSPL) in line-of-sight (LOS) propagation compared to the currently used sub-6 GHz frequencies. What is more, in non-line-of-sight (NLOS) propagation, the attenuation of mmWave is larger compared to the lower frequencies, which can seriously degrade the performance. It is therefore necessary to investigate mmWave propagation characteristics for different deployment scenarios of interest, to understand coverage and rate performance in such scenarios. In this paper, we focus on 28 GHz wideband mmWave signal propagation characteristics at Johnston Regional Airport (JNX), a local airport near Raleigh, NC. To collect data, we use an NI PXI-based channel sounder at 28 GHz for indoor, outdoor, and indoor-to-outdoor scenarios. Results on LOS propagation, reflection, penetration, signal coverage, and multipath components (MPCs) show a lower indoor FSPL, a richer scattering, and a better coverage compared to outdoor. We also observe high indoor-to-outdoor propagation losses.

The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we propose a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7% and 86% at a signal-to-noise ratio of 30 dB and 18 dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment.

Cellular-enabled unmanned aerial vehicles (UAVs) require almost continuous cellular network connectivity to fulfill their missions successfully. However, the area (e.g., rural) they fly over may have partial coverage, making the path planning of such UAV missions a challenging task. Recently a tolerable outage duration is taken into account for such UAVs, and the trajectory optimization under this outage duration is studied. However, these existing studies consider only a single UAV and focus on optimization of each UAV's own path separately even in multi-UAV scenarios. In this paper, we study the trajectory optimization problem for cellular-enabled UAVs by taking into account the collaboration among UAVs. That is, for a given set of UAVs, each with a mission to fly from a starting point to an ending point, we aim to optimize the total mission completion time for all UAVs such that none of them has a connection outage more than a threshold. We let UAVs collaborate and provide connectivity as relays to each other to solve their outage problem and shorten their trajectories. We first model and solve this problem using nonlinear programming after discretization of the problem. Since it takes longer to solve the problem with such an approach, we then provide a graph-based approximate solution that runs fast. Numerical results show that the proposed approximate solution provides close to optimal results and performs better than state-of-the-art solutions that consider each UAV separately without collaboration among UAVs.

Reliable and long-range wireless communications carry critical importance for remote identification (ID) of unmanned aerial systems (UAS). Federal Aviation Authority (FAA) in the United States has recently released rules that describe how drones can broadcast their ID and location information, without mandating a specific radio access technology. In this paper, we study LoRa as a candidate technology for drone remote ID. We first provide a brief overview of the recent FAA rules on drone remote ID released in December 2020. Subsequently, we provide preliminary results based on ray tracing and MATLAB simulations that evaluate the reliability and range of LoRa for UAS remote ID scenarios. Two different LoRa multi-user interference scenarios are considered: in one scenario, the interferer uses the same spreading factor (SF) as the desired user, while in another scenario, desired and interfering user SFs are different. Our results quantify the performance gains that may be obtained under different interference conditions, using various coding rates and SFs.

As the use of unmanned aerial vehicles (UAVs) in various commercial, civil, and military applications increases, it becomes important to study the design of aerial drone corridors that can support multiple simultaneous UAV missions. In this work, we study the placement of base stations (BSs) to serve aerial drone corridors while satisfying specific UAV mission requirements, such as the geometrical waypoints for the UAV to fly through and the minimum data rate to be supported along the mission trajectory. We develop a mathematical model of the drone corridor and propose a brute force algorithm that leverages A* search to meet the quality of service (QoS) requirements of the corridor by choosing the minimal set of BS locations from a pre-determined initial set. Using raytracing simulations, BS placement results are presented for various antenna array sizes in a dense urban region in East Manhattan. It was found that, for the scenario under consideration, a single BS equipped with an 8x8 antenna array is sufficient to satisfy the given QoS requirements of the corridor, while two BSs are required when using 4x4 antenna arrays.

Visible light communication (VLC) is an emerging technology that enables broadband data rates using the visible spectrum. In this paper, considering slow beam steering where VLC beam directions are assumed to be fixed during a transmission frame, we find the steering angles that simultaneously serve multiple users within the frame duration and maximize the data rates. This is achieved by solving a non-convex optimization problem using a grid-based search and majorization-minimization (MM) procedure. Subsequently, we consider multiple steerable beams with a larger number of users in the network and propose an algorithm to cluster users and serve each cluster with a separate beam. We optimize the transmit power of each beam to maximize the data rates. Finally, we propose a non-orthogonal multiple access (NOMA) scheme for the beam steering and user clustering scenario, to further increase the data rates of the users. The simulation results show that the proposed beam steering method can efficiently serve a high number of users, and with power optimization, a sum rate gain up to thirteen times is possible. The simulation results for NOMA suggests an additional 10 Mbps sum rate gain for each NOMA user pair.

To address the demands for more capacity and higher data rates, the fifth generation network (5G) technology is being developed and gradually rolled out by major cellular carriers. Millimeter wave (mmWave) systems and unmanned aerial vehicles (UAVs) are two critical enablers of 5G. To ease the integration of 5G into existing networks, it is essential to study how base stations (BSs) can be used to serve both ground and aerial users simultaneously in a mmWave network. In this work, we consider BSs equipped with two antennas- one tilted down to serve ground users and another tilted up to serve aerial users. Using ray tracing simulations, we investigate the ideal tilt of these two antennas. Our results indicate that reusing the spectrum effectively in urban environments requires an understanding of the interplay between the effects of shadowing due to buildings, ground reflections, beam orientation, BS separation, interference between the two beams, and UAV heights. Specifically, to simultaneously serve UAVs at a height of 200 m and ground users, it is desirable to use a BS equipped with one antenna tilted up at 30 degrees and another tilted down at of 10 degrees. This achieves the best compromise between the above effects for the simulation configuration considered in this paper. Further, it was observed that the aerial and ground users are, in some scenarios, actually better served by the antenna not meant for them.

Drones will have extensive use cases across various commercial, government, and military sectors, ranging from delivery of consumer goods to search and rescue operations. To maintain safety and security of people and infrastructure, it becomes critically important to quickly and accurately detect non-cooperating drones. In this paper we formulate a received signal strength (RSS) based detector, leveraging the existing wireless infrastructures that might already be serving other devices. Thus the detector should be able to detect the presence of a drone signal buried in radio frequency (RF) interference and thermal noise, in a mixed line-of-sight (LOS) and non-LOS (NLOS) environment. We develop analytical expressions for the probability of false alarm and the probability of detection of a drone, which quantify the impact of aggregate interference and air-to-ground (A2G) propagation characteristics on the detection performance of individual sensors. We also provide analytical expressions for the average network probability of detection, which captures the impact of sensor density on a network's detection coverage. Finally, we find the critical sensor density that maximizes the average network probability of detection for a given requirement of probability of false alarm.

In this paper, we consider simultaneous information and energy transfer (SWIPT) in unmanned aerial vehicle (UAV)-assisted cellular Internet of Things (IoT) networks, in which the user equipment (UE) locations are modeled as Thomas cluster processes. A realistic air-to-ground communication model is incorporated into the analysis. In particular, different line of sight (LOS) and non-LOS (NLOS) path loss models are considered for the links from the UAVs to UEs and ground base stations (GBSs) to UEs. Three dimensional (3D) antenna patterns are adopted, e.g., a doughnut-shaped antenna radiation pattern is considered for UAVs and a combination of horizontal and vertical antenna pattern is utilized for GBSs. In addition, we employ the power splitting technique in the SWIPT scenario, which allows the UEs to harvest energy and decode information simultaneously using the same received signal. Association probability and energy coverage probability of the UAVs and GBSs are determined. Moreover, an analysis of the successful transmission probability which jointly addresses the energy and signal-to-interference-plus-noise ratio (SINR) coverages is provided. Finally, performance is further investigated via numerical results.

The integration of unmanned aerial vehicles (UAVs) into the terrestrial communications networks with a variety of tasks is viewed as a key technology for 5G and beyond. In this work, we consider the uplink millimeter-wave (mmWave) transmission between a set of UAVs and a base station (BS), where the UAVs deploy uplink non-orthogonal multiple access (NOMA) in multiple clusters. Furthermore, the BS also serves its own desired ground user equipment (UE) in the presence of many other ground UEs associated with other cells, which share the same frequency band. Considering the limited energy budget of UAVs, we formulate an energy efficiency (EE) problem, and propose a solution aided by the Dinkelbach's algorithm and successive convex approximation (SCA). Using realistic air-to-ground (A2G) and terrestrial channel models, we assess the performance of the proposed algorithm under various circumstances (maximum transmit power for UAVs, quality-of-service (QoS) constraint for the desired UE, etc.), and identify the best use cases.

Aggregated Internet of Things (IoT) communication aims to use core network resources efficiently by providing cellular access to a group of IoT devices over the same subscriber identity. Leveraging the low data rates and long data sending intervals of IoT devices, several of the IoT devices in the same serving area of the core network are grouped together and take turns to send their data to their servers without causing overlaps in their communication. In this paper, we take this approach further and benefiting from the flexibility in data sending schedules, we aim to increase savings in cellular resources by shifting (delaying or performing earlier) the regular traffic patterns of IoT devices slightly. To this end, we consider two different traffic shifting models, namely, consistent and inconsistent shifting. We first solve the optimal aggregation of IoT devices under each model by using Integer Linear Programming (ILP). In order to avoid the high complexity of ILP solution, we then develop a heuristic based solution that runs in polynomial time. Through simulations, we show that heuristic based solution provides close to optimal results in various scenarios and shifting based aggregated communication offers more resource optimization (i.e., smaller number of bearers needed to connect all IoT devices) than the aggregated communication with no shifting.

Unmanned aerial vehicles (UAVs) enabled networks can enhance wireless connectivity and support emerging services. However, this would require system‐level understanding to modify and extend the existing terrestrial network infrastructure. In this study, the authors integrated UAVs as user equipment and base stations into an existing long term evolution (LTE)‐Advanced heterogeneous network (HetNet) and provide system‐level insights of this three‐tier LTE‐Advanced air‐ground HetNet (AG‐HetNet). The performance of AG‐HetNet was evaluated through brute‐force technique and heuristics algorithms in terms of the fifth percentile spectral efficiency (5pSE) and coverage probability. In particular, system‐wide 5pSE and coverage probability were compared, when unmanned aerial base stations (UABSs) are deployed on a fixed hexagonal grid and when their locations are optimised using a genetic algorithm (GA) and elitist harmony search algorithm based on the genetic algorithm (eHSGA); while jointly optimising the inter‐cell interference coordination (ICIC) and cell range expansion (CRE) network parameters for different ICIC techniques. The simulation results show that the heuristic algorithms (GA and eHSGA) outperform the brute‐force technique and achieve better peak values of coverage probability and 5pSE. Simulation results also show that a trade‐off exists between peak values and computation time when using heuristic algorithms. Furthermore, the three‐tier hierarchical structuring of reduced power subframes further‐enhanced ICIC (FeICIC) defined in 3GPP Rel‐11 provides considerably better 5pSE and coverage probability than the 3GPP Rel‐10 with almost blank subframes eICIC. They also investigated the network performance for different practical deployment heights of UABS and they found low‐altitude UABSs to perform sparsely better than medium‐altitude UABSs.

The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave) frequency band a key component of the next-generation wireless networks. While mmWave spectrum offers extremely large transmission bandwidths to accommodate ever-increasing data rates, unique characteristics of this new spectrum need special consideration to achieve the promised network throughput. In this work, we consider the off-grid targets (basis mismatch) problem for mmWave communications. The off-grid effect naturally appears in compressed sensing (CS) techniques adopting a discretization approach for representing the angular domain. This approach yields a finite set of discrete angle points, which are an approximation to the continuous angular space, and hence degrade the accuracy of related parameter estimation. In order to cope with the off-grid effect, we present a novel parameter-perturbation framework to efficiently estimate the channel and the covariance for mmWave networks. The proposed algorithms employ a smart perturbation mechanism in conjunction with a low-complexity greedy framework of simultaneous orthogonal matching pursuit (SOMP), and jointly solve for the off-grid parameters and weights. Numerical results show a significant performance improvement through our novel framework as a result of handling the off-grid effects, which is totally ignored in the conventional sparse mmWave channel or covariance estimation algorithms.

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

The spectrum-efficient millimeter-wave (mmWave) communications has recently attracted much attention as a viable solution to spectrum crunch problem. In this work, we propose a novel non-orthogonal multiple access (NOMA) framework, which makes use of the directional propagation characteristics of mmWave communications so as to improve the spectral efficiency through non-orthogonal signaling. In particular, we consider one-bit quantized angle information as a limited yet effective feedback scheme describing the channel quality of user equipment (UE) in mmWave bands. The UE pairs for NOMA transmission are then established using not only the one-bit distance feedback as a classical approach, but also the one-bit angle feedback. The proposed strategy is therefore referred to as two-bit NOMA. We also propose a novel hybrid strategy, called combined NOMA, for the circumstances with no UE pair through two-bit NOMA. Whenever no UE pair is available through any NOMA strategy, we resort to single user transmission (SUT) with proper UE selection schemes. The hybrid outage sum-rate performance is also analyzed thoroughly with the respective outage and rate expressions. The numerical results verify that the proposed strategy outperforms one-bit NOMA schemes with either angle- or distance-only feedback, and has a very close outage sum-rate performance to that for the optimal full-resolution feedback.

In this paper, we tackle channel estimation in millimeter-wave hybrid multiple-input multiple- output systems by considering off-grid effects. In particular, we assume that spatial parameters can take any value in the angular domain, and need not fall on predefined discretized angles. Instead of increasing the number of discretized points to combat off-grid effects, we use implicit Dirichlet kernel structure in the Fourier domain, which conventional compressed sensing methods do not use. We propose greedy low-complexity algorithms based on orthogonal matching pursuit (OMP); our core idea is to traverse the Dirichlet kernel peak using estimates of the discrete Fourier transform. We demonstrate the efficacy of our proposed algorithms compared to standard OMP reconstruction. Numerical results show that our proposed algorithms obtain smaller reconstruction errors when off-grid effects are accounted for.

The deployment of the unmanned aerial vehicle (UAV) has been foreseen as a promising technology for the next generation communication networks. The distance limitation imposed by the line of sight RF connection can be removed by using RF coverage from existing commercial cellular service. In this work, we consider a transmission mechanism that aims to improve the data rate between a terrestrial base station (BS) and user equipment (UE) through deploying multiple UAVs relaying the desired data flow. Considering the coexistence of this network with other established communication networks, we take into account the effect of interference, which is incurred by the existing nodes. Our primary goal is to optimize the three-dimensional (3D) trajectories and power allocation for the relaying UAVs to maximize the data flow while keeping the interference to existing nodes below a predefined threshold. An alternating-maximization strategy is proposed to solve the joint 3D trajectory design and power allocation for the relaying UAVs. To this end, we handle the information exchange within the network by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. Simulation results show that our approach can considerably improve the information flow while the interference threshold constraint is met.

We consider a downlink multiuser visible light communications (VLC) network where users randomly change their location and vertical orientation. The non-orthogonal multiple access (NOMA) strategy is adopted to serve multiple users simultaneously, and, hence, to improve spectral efficiency. We propose two novel user scheduling schemes for NOMA, which are referred to as individual and group-based. In order to further reduce the computational complexity and link overhead, novel limited-feedback schemes (on channel quality) are also proposed, which basically involve mean vertical angle (instead of its instantaneous value). Moreover, a two-bit feedback scheme is proposed for group-based user scheduling, which relies on not only distance but also vertical angle (in contrast to conventional one-bit feedback with distance only). The outage probability and sum-rate expressions are derived analytically, which show a very good match with the simulation data. Numerical results verify that the practical feedback scheme with the mean vertical angle achieves a near-optimal sum-rate performance, and the two-bit feedback significantly outperforms the one-bit feedback.

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

Using radio frequency (RF) coverage from the existing cellular networks is an attractive option to maintain beyond visual line-of-sight (BVLOS) connectivity with drones. These cellular drones can connect with a ground control station (GCS) for control and data delivery wherever cellular service is available. However, RF coverage for these cellular networks has been optimized for the ground users and while they do leak upwards, reliable RF coverage exists to only about 400 feet high. As the drones are becoming more commonly used for activities ranging from emergency responses to aerial deliveries, the base stations covering users both on the ground and in the air with RF transmission cannot scale to the capacity needed to support increasing number of drones. Use of directional beams that are necessary for millimeter wave (mmWave) transmission in the 5G cellular system can reduce interference among users, and hence increase capacity compared to sector based transmissions used for 4G systems without massive MIMO (mMIMO). In this paper, considering the multiplicative capacity gains needed to support large number of drones, we propose a separate mmWave cellular network with optimized coverage for a ''drone corridor'' in the air. We present our current findings in the following areas critical to validating the effectiveness of this proposed network: 1) RF propagation characteristics towards drones in the air compared with propagation towards users on the ground; 2) use of multiple access (MA) technology for increased spectral efficiency for a swarm of drones; 3) optimal design of protected zones and beamforming to secure drone specific wireless communications.

Internet of things (IoT) is one of main paradigms for 5G wireless systems. Due to high connection density, interference from other sources is a key problem in IoT networks. Especially, it is more difficult to find a solution to manage interference in uncoordinated networks than coordinated system. In this work, we consider 3D topology of uncoordinated IoT network and propose interference mitigation scheme with respect to 3D antenna radiation pattern. In 2D topology network, the radiation pattern of dipole antenna can be assumed as onmi-directional. We show the variance of antenna gain on dipole antenna in 3D topology, consider the simultaneous use of three orthogonal dipole antennas, and compare the system performance depending on different antenna configurations. Our simulation results show that proper altitude of IoT devices can extensively improve the system performance.

New use cases for advanced wireless technologies are emerging in the unmanned aerial systems (UAS) spaces. These put pressure on technology and regulation. The way to overcome this is to gain experience and collect data while operating UAS in production environments. To this end, we introduce AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless, and present an architecture for designing a large-scale community testbed in a production-like environment to enable controllable experiments with latest wireless technologies and systems. Using advanced networking and virtualization technology to manage the platform resources, users will be able to configure the testbed for running a variety of at-scale experiments for UAS localization and tracking, networking, trajectory optimization, spectrum management, and aerial-terrestrial cellular network design and optimization based on 5G and software radio technology, among others.

In this paper, an analytical framework is provided to analyze the energy coverage performance of unmanned aerial vehicle (UAV) energy harvesting networks with clustered user equipments (UEs). Locations of UAVs are modeled as a Poison Point Process (PPP), while locations of UEs are modeled as a Poisson Cluster Process (PCP). Two different models are considered for the line-of-sight (LOS) probability function to compare their effect on the network performance. Moreover, ultra-wideband (UWB) antennas with doughnut-shaped radiation patterns are employed in both UAVs and UEs, and the impact of practical 3D antenna radiation patterns on the network performance is also investigated. Initially, the complementary cumulative distribution function (CCDF) and probability density function (PDF) of path losses for each tier are derived. Subsequently, association probabilities with each tier are obtained. Energy coverage probability is derived for the entire network using tools from stochastic geometry. Via numerical results, we show that UAV height and antenna orientation play crucial roles on the energy coverage performance.

Device-to-device (D2D) communication enables machine-type devices (MTD) in Internet-of-Things (IoT) network communicate directly with each other and offload the cellular network. However, it may introduce interference as they share the same spectrum with the other devices that are directly connected to the base station. In this study, we look at the problem of assigning D2D communicating IoT pairs to the IoT devices that are directly connected to the base station such that the overall system throughput is not only maximized but also a stable matching is obtained. Different than previous work, we study many-to-one matching and propose a distributed privacy preserving stable matching process for efficient resource allocation without releasing location information.

In the last few years, unmanned aerial vehicles (UAVs) have developed rapidly and the applications of UAVs have been expanded in wide areas, including photography, cargo delivery, inspection, and communications. Conventionally, UAVs are controlled and operated by a ground station using specific radio transmission modules, where the line-of-sight signal transmission is preferred and the operation range is limited, especially in urban areas. Using the Internet of Things (IoT) technologies, a UAV can be regarded as a terminal device connected in the ubiquitous network, where many other UAVs are communicated, navigated, controlled, and surveilled in real time and beyond line-of-sight.

Visible light communications (VLC) has been studied thoroughly in recent years as an alternative or complementary technology to radio frequency communications. The reliability of VLC channels highly depends on the availability and alignment of line of sight links. In this paper, we study the effect of random receiver orientation for mobile users over VLC downlink channels, which affects the existence and quality of line of sight links. Based on the statistics of the receiver location and relative orientation with respect to the transmitter LED, we develop an analytical framework to characterize the statistical distribution of VLC downlink channels, which is then utilized to obtain the outage probability and the bit error rate. Our analysis is generalized for arbitrary distributions of relative orientation and location for a single transmitter, and extended to multiple transmitter case for some certain scenarios. Extensive Monte Carlo simulations show a perfect match between the analytical and the simulation data in terms of both the statistical channel distribution and the resulting bit error rate. Our results also characterize the channel attenuation due to random receiver orientation and location for various scenarios of interest.

Unmanned aerial vehicle (UAV) base stations (BSs) can be a promising solution to provide connectivity and quality-of-service guarantees during temporary events and after disasters. In this paper, we consider a scenario where UAV-BSs are serving a large number of mobile users in a hot spot area (e.g., in a stadium). We introduce non-orthogonal multiple-access (NOMA) transmission at UAV-BSs to serve more users simultaneously considering user distances as the available feedback for a user ordering during NOMA formulation. With millimeter-wave transmission and multi-antenna techniques, we assume that UAV-BS generates directional beams and multiple users are served simultaneously within the same beam. However, due to the limitations of physical vertical beamwidth of the UAV-BS beam, it may not be possible to cover the entire user region at UAV altitudes of practical relevance. During such situations, a beam scanning approach is proposed to maximize the achievable sum rates. We develop a comprehensive framework over which outage probabilities and respective sum rates are derived rigorously and we investigate the optimal operational altitude of UAV-BS to maximize the sum rates using our analytical framework. Our analysis shows that NOMA with distance feedback can provide better outage sum rates than orthogonal multiple access.

Visible light communications (VLC) is an emerging technology with a promise of viable solution to spectrum crunch problem in conventional radio-frequency (RF) bands. In this paper, we consider a downlink multiuser VLC network where users randomly change their location and vertical orientation. In order to increase the spectral efficiency, we consider the non-orthogonal multiple access (NOMA) transmission to serve multiple users simultaneously. In particular, we propose individual and group-based user ordering techniques for NOMA with various user feedback schemes. In order to reduce the computational complexity and link overhead, feedback on the channel quality is proposed to be computed using a mean value of the vertical angle (instead of the exact instantaneous value), as well as the distance information. In addition, a two-bit feedback scheme is proposed for the group-based user scheduling, which relies on both the distance and the vertical angle, and differs from the conventional one-bit feedback of the distance only. The outage probability and sum-rate expressions are derived analytically, which show a very good match with the simulation data. Numerical results verify that the practical feedback scheme with the mean vertical angle achieves a near-optimal sum-rate performance, and the two-bit feedback significantly outperforms the one-bit feedback.

The overheads associated with feedback-based channel acquisition can greatly compromise the achievable rates of FDD based massive MIMO systems. Indeed, downlink (DL) training and uplink (UL) feedback overheads scale linearly with the number of base station (BS) antennas, in sharp contrast to TDD-based massive MIMO, where a single UL pilot trains the whole BS array. In this work, we propose a graph-theoretic approach to reducing DL training and UL feedback overheads in FDD massive MIMO systems. In particular, we consider a single-cell scenario involving a single BS with a massive antenna array serving to single-antenna mobile stations (MSs) in the DL. We assume the BS employs two-stage beamforming in the DL, comprising DFT pre-beamforming followed by MU-MIMO precoding. The proposed graph-theoretic approach exploits knowledge of the angular spectra of the BS-MS channels to construct DL training protocols with reduced overheads. Simulation results reveal that the proposed training-resources allocation method can provide approximately 35% sum-rate performance gain compared to conventional orthogonal training. Our analysis also sheds light into the impact of overhead reduction on channel estimation quality, and, in turn, achievable rates.

5G millimeter wave (mmWave) technology is envisioned to be an integral part of next-generation vehicle-to-everything (V2X) networks and autonomous vehicles due to its broad bandwidth, wide field of view sensing, and precise localization capabilities. The reliability of mmWave links may be compromised due to difficulties in beam alignment for mobile channels and due to blocking effects between a mmWave transmitter and a receiver. In this paper, we study the channel characteristics for mmWave and sub-6 GHz V2X communications using ray-tracing simulations. We present results for time-varying path-loss, delay spread, and angular spreads in the presence of a moving vehicle for line-of-sight (LOS) and non-LOS (NLOS) trajectory, respectively. Additionally, we study the effect of delay spread and angular spread when the base station (BS) and user equipment (UE) are capable of adjusting the beam directions and beamwidths dynamically. Finally, we explore the impact of blockage effects on the quality of receive beams at 28 GHz for the considered vehicle trajectories.

In this paper, we address the problem of path planning for multiple unmanned aerial vehicles (UAVs), to gather data from a number of roadside units (RSUs). The problem involves finding time-optimal paths for multiple UAVs so that they collectively visit all the RSUs, while also exchanging information at their own point when they fly from a starting point to the final location. We solve the problem by applying modified evolutionary methods based on genetic algorithm (GA) and harmony search (HS). The modified search methods seek to determine the overall shortest path utilizing various evolutionary operators regarding each UAV which has identical properties at the start location. Numerical results are introduced under different scenarios and the performances of the proposed algorithms are evaluated.

Three dimensional (3D) radiation pattern of an antenna mounted at a drone can significantly influence the air-to-ground (A2G) link quality. Even when a drone transmitter is very close to a ground receiver, if the antenna orientations are not aligned properly, a significant degradation can be observed in the received signal power at the receiver. To characterize such effects for a doughnut-shaped antenna radiation pattern, using an ultra-wideband (UWB) transmitter at the drone and a UWB receiver at the ground, we carry out A2G channel measurements to capture the link quality at the ground receiver for various link distances, drone heights, and antenna orientations. We develop a simple analytical model to approximate the influence of 3D antenna patterns on the received signal strength (RSS), which show reasonable agreement with measurements despite the simplicity of the model and the complicated 3D radiation from the UWB antennas. We also explore how the signal strength can be improved when multiple antennas with different orientations are utilized at transmitter/receiver.

We introduce the concept of using unmanned aerial vehicles (UAVs) as drone base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for 5G networks. We first present a system model for forward link transmissions in an IB-IAB multi- tier drone cellular network. We then investigate the key challenges of this scenario and propose a framework that utilizes the flying capabilities of the UAVs as the main degree of freedom to find the optimal precoder design for the backhaul links, user-base station association, UAV 3D hovering locations, and power allocations. We discuss how the proposed algorithm can be utilized to optimize the network performance in both large and small scales. Finally, we use an exhaustive search-based solution to demonstrate the performance gains that can be achieved from the presented algorithm in terms of the received signal to interference plus noise ratio (SINR) and overall network sum-rate.

Millimeter wave (mmWave) communications can provide high speed data rate for 5G wireless networks, taking advantage of the large bandwidth available at mmWave frequencies. It is also well known that mmWave links may suffer from outages due to non-line-of-sight (NLOS) propagation problems. In this paper, we study the characteristics of IEEE~802.11ad based mmWave systems at 60~GHz, and investigate the effect of passive reflectors for improving coverage. The experiments are performed using Tensorcom single chip TC60G-USB3-EVB and TC60G70100UP picocell platforms in different indoor and outdoor environments, both supporting beamforming based on IEEE~802.11ad specifications. Our results show that the passive metallic reflectors may signifciantly improve the mmWave signal coverage in regions where the line-of-sight (LOS) connectivity between the transmitter and the receiver is limited.

Unmanned aerial vehicles (UAVs) can supplement the existing ground-based heterogeneous cellular networks (Het-Nets), by replacing/supporting damaged infrastructure, providing real-time video support at the site of an emergency, offloading traffic in congested areas, extending coverage, and filling coverage gaps. In this paper, we introduce distributed algorithms that leverage UAV mobility, enhanced inter-cell interference coordination (ICIC), and cell range expansion (CRE) techniques defined in 3GPP Release-10 and 3GPP Release-11. Through Monte-Carlo simulations, we compare the system-wide 5th percentile spectral efficiency (5pSE) while optimizing the performance using a brute force algorithm, a heuristic-based sequential algorithm, and a deep Q-learning algorithm. The autonomous UAVs jointly optimize their location, ICIC parameters, and CRE to maximize 5pSE gains and minimize the outage probability. Our results show that the ICIC technique relying on a simple heuristic outperforms the ICIC technique based on deep Q-learning. Taking advantage of the multiple optimization parameters for interference coordination, the heuristic based ICIC technique can achieve 5pSE values that are reasonably close to those achieved with exhaustive brute force search techniques, at a significantly lower computational complexity.

In this paper, we consider the outage performance of the M-BLAST symbol detection algorithm over uncoded quasistatic MIMO channels with spatial multiplexing. The M-BLAST algorithm is known to achieve a superior error performance over its predecessor V-BLAST with a signal-to-noise ratio (SNR) improvement of as large as 2 dB. We evaluate this performance improvement analytically, which has not been studied before, by deriving the outage probability expression over generalized complex binary alphabets with two transmitting and unlimited number of receiving antennas. Although the underlying successive interference cancellation (SIC) receiver introduces a high non-linearity which complicates the analysis, our analytical and simulation results are shown to have a very good match.

Zone-level occupancy counting is a critical technology for smart buildings and can be used for applications, such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building and an established wireless network. In this paper, in order to achieve occupancy counting, we consider the use of Wi-Fi probe requests that are continuously transmitted from Wi-Fi enabled smart devices for discovering nearby access points. To this end, Wi-Fi Pineapple equipment are used for passively capturing ambient probe requests from Wi-Fi devices, such as smart phones and tablets, where no connectivity to a Wi-Fi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multimodel (IMM) Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using Wi-Fi data collected at a university building show that the use of Wi-Fi probe requests in conjunction with IMM-based Kalman filters can be a viable solution for zone-level occupancy monitoring in smart buildings.

Visible light communications (VLCs) are a promising technology to address the spectrum crunch problem in radio frequency networks. A major advantage of VLC networks is that they can use the existing lighting infrastructure in indoor environments, which may have large number of LEDs for illumination. While LEDs used for lighting typically have limited bandwidth, presence of many LEDs can be exploited for indoor VLC networks, to serve each user by multiple LEDs for improving link quality and throughput. In this paper, LEDs are grouped and assigned to the users based on received signal strength from each LED, for which different solutions are proposed to achieve maximum throughput, proportional fairness, and quality of service. Additionally, power optimization of LEDs for a given assignment is investigated, and the Jacobian and Hessian matrices of the corresponding optimization problem are derived. Moreover, for multi-element receivers with LED grouping at the transmitter, an improved optimal combining method is proposed. This method suppresses interference caused by simultaneous data transfer of LEDs and improves the overall signal-to-interference-plus-noise-ratio by 2-5 dB. Lastly, an efficient calculation of channel response is presented to simulate multipath VLC channel with low computational complexity.

Efficient symbol detection algorithms carry critical importance for achieving the spatial multiplexing gains promised by multi-input multi-output (MIMO) systems. In this paper, we consider a maximum a posteriori probability (MAP)-based symbol detection algorithm, called M-BLAST, over uncoded quasi-static MIMO channels. Relying on the successive interference cancellation (SIC) receiver, the M-BLAST algorithm offers a superior error performance over its predecessor V-BLAST with a signal-to-noise ratio (SNR) gain of as large as 2 dB under various settings of recent interest. Performance analysis of the M-BLAST algorithm is very complicated since the proposed detection order depends on the decision errors dynamically, which makes an already complex analysis of the conventional ordered SIC receivers even more difficult. To this end, a rigorous analytical framework is proposed to analyze the outage behavior of the M-BLAST algorithm over binary complex alphabets and two transmitting antennas, which has a potential to be generalized to multiple transmitting antennas and multidimensional constellation sets. The numerical results show a very good match between the analytical and simulation data under various SNR values and modulation alphabets.

Due to increase in the number of aging people worldwide, ambient assisted living (AAL) has been recently gaining extensive attention for autonomous monitoring of people in indoor environments. One possible way to fulfill this task is by using ultrawideband (UWB) radars. The UWB signals are ideal for detection, localization and mapping applications due to fine resolution of their multipath components, and can serve well for AAL scenarios. In this paper, using multiple UWB radar transceivers we introduce a fall detection, localization and tracking technique for people in need of assistance. The proposed method allows precise monitoring of people with special needs without any tags or wearables. To enhance ranging precision, we detect and discard outliers in the scanned UWB radar data collected by Time Domain P410 radios. A novel method of fall detection is introduced based on the residual covariance from extended Kalman filter (EKF). Computer simulations demonstrate the effectiveness of the proposed technique for AAL applications.

Establishing a reliable communication infrastructure at an emergency site is a crucial task for mission-critical and real-time public safety communications (PSC). To this end, use of unmanned aerial vehicles (UAVs) has recently received extensive interest for PSC to establish reliable connectivity in a heterogeneous network (HetNet) environment. These UAVs can be deployed as unmanned aerial base stations (UABSs) as part of the HetNet infrastructure. In this article, we explore the role of agile UABSs in LTE-Advanced HetNets by applying 3GPP Release-11 further-enhanced inter-cell interference coordination (Fe-ICIC) and cell range expansion (CRE) techniques. Through simulations, we compare the system-wide 5th percentile spectral efficiency (SE) when UABSs are deployed in a hexagonal grid and when their locations are optimized using a genetic algorithm, while also jointly optimizing the CRE and the FeICIC parameters. Our simulation results show that at optimized UABS locations, the 3GPP Release-11 FeICIC with reduced power subframes can provide considerably better 5th percentile SE than the 3GPP Release-10 with almost blank subframes.

Millimeter-wave (mmWave) communications, along with massive multiple-input multiple-output (MIMO) systems, are recently being considered extensively as enabling technologies for emerging 5G cellular networks. The existing literature shows that the capacity in massive MIMO systems is limited by the pilot contamination effect, caused by the reuse of pilot sequences during the uplink channel estimation stage. It has also been proved that the pilot contamination effect increases with the angular spread (AS) of the multipath clusters. In this work, we study the relation between the AS and the capacity of a cellular network, where we show that there exists an optimal AS that maximizes the capacity. Furthermore, we observe that the optimal AS depends on the specific configuration of the network, such as the number of antennas installed at the base station, and the number of cells and users per cell. Since the AS depends on the operating frequency, we argue that the system capacity can be maximized by proper selection of the carrier frequency.

Maintaining communications during major hurricanes is critically important for public safety operations by first responders. This requires accurate knowledge of the propagation channel during hurricane conditions. In this work, we have carried out ultra-wideband (UWB) channel measurements during hurricane conditions ranging from Category-1 to Category- 4, generated at the Wall of Wind (WoW) facility of Florida International University (FIU). Time Domain P410 radios are used for channel measurements. From the empirical data analysis in time domain, we developed a UWB statistical broadband channel model for hurricanes. In particular, we characterize the effects of rain and wind speed on large scale and small scale UWB propagation parameters.

Communication at mmWave bands carries critical importance for 5G wireless networks. In this paper, we study the characterization of mmWave air-to-ground (AG) channels for unmanned aerial vehicle (UAV) communications. In particular, we use ray tracing simulations using Remcom Wireless InSite software to study the behavior of AG mmWave bands at two different frequencies: 28 GHz and 60 GHz. Received signal strength (RSS) and root mean square delay spread (RMS-DS) of multipath components (MPCs) are analyzed for different UAV heights considering four different environments: urban, suburban, rural, and over sea. It is observed that the RSS mostly follows the two ray propagation model along the UAV flight path for higher altitudes. This two ray propagation model is affected by the presence of high rise scatterers in urban scenario. Moreover, we present details of a universal serial radio peripheral (USRP) based channel sounder that can be used for AG channel measurements for mmWave (60 GHz) UAV communications.

Localization of mobile wireless devices carries critical importance for applications such as search and rescue, public safety, surveillance, and occupancy monitoring. In this paper, we study the problem of localizing WiFi-enabled mobile devices such as smartphones and tablets using the measurements captured by an unmanned aerial vehicle (UAV). We make use of the continuously broadcasted WiFi probe requests from mobile devices, capture them at different locations at a WiFi sniffer carried by a UAV, and subsequently estimate the user's location using random-forest based machine learning technique. More specifically, the geographical area of interest is partitioned into multiple zones, and based on the measured probe requests, we are interested to identify the zone where the WiFi device is located. Our experimental results show that the WiFi device can be detected in correct occupancy zone with a 81.8% accuracy.

Passive radio-frequency identification (RFID) systems carry critical importance for Internet of Things (IoT) applications due to their energy harvesting capabilities. RFIDbased position estimation, in particular, is expected to facilitate a wide array of location-based services for IoT applications with low-power requirements. In this paper, considering monostatic and bistatic configurations and 3-D antenna radiation pattern, we investigate the accuracy of received signal strength-based wireless localization using passive ultra high frequency RFID systems. The Cramer-Rao lower bound (CRLB) for the localization accuracy is derived, and is compared with the accuracy of maximum likelihood estimators for various RFID antenna configurations. Numerical results show that due to RFID tag/antenna sensitivity, and the directional antenna pattern, the localization accuracy can degrade at blind locations that remain outside of the RFID reader antennas' main beam patterns. In such cases optimizing elevation angle of antennas are shown to improve localization coverage, while using bistatic configuration improves localization accuracy significantly.

Broadband wireless communication is of critical importance during public safety scenarios as it facilitates situational awareness capabilities for first responders and victims. In this paper, the use of LTE-Unlicensed (LTE-U) technology for unmanned aerial base stations (UABSs) is investigated as an effective approach to enhance the achievable broadband throughput during emergency situations by utilizing the unlicensed spectrum. In particular, we develop a game theoretic framework for load balancing between LTE-U UABSs and WiFi access points (APs), based on the users' link qualities as well as the loads at the UABSs and the ground APs. To solve this game, we propose a regret-based learning (RBL) dynamic duty cycle selection (DDCS) method for configuring the transmission gaps in LTE-U UABSs, to ensure a satisfactory throughput for all users. Simulation results show that the proposed RBL-DDCS yields an improvement of 32% over fixed duty cycle LTE-U transmission, and an improvement of 10% over Q-learning based DDCS.

Unmanned aerial vehicles (UAVs) are expected to be used extensively in the near future in applications such as aerial surveillance, transportation, and disaster assistance. The conditions under which UAVs operate are different from those of conventional piloted aircrafts. This necessitates development of new air-to-ground (AG) propagation channel models for UAVs. To our best knowledge, there are limited studies in the literature on sounding and modeling of ultrawideband (UWB) AG propagation channels. In this work, comprehensive UWB measurements are conducted for various UAV communication scenarios using Time Domain P410 UWB kits. Both time and frequency domain analysis of the measured data are carried out. Based on the measured data, stochastic path loss and multipath channel models are developed to characterize AG UWB propagation channels.

Unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) to deliver broadband wireless connectivity during temporary events, at hotspot areas, or after disasters that may destroy existing communication infrastructure. Since UAV- BSs are low power nodes, their efficient placement is important to reap the maximum capacity and coverage benefits from their deployments. By making use of \emph{UAV mobility} and multi- antenna arrays, it is possible to achieve angular domain user separation with lower feedback requirement. In this paper, we propose an approach to identify optimum hovering locations for UAV-BSs equipped with multi-antenna arrays. We formulate the problem as a signal-to-noise ratio (SNR) maximization at ground nodes subject to a constraint that interference leakage is lower than a threshold. We consider a scenario with two UAVs each with a single user attached and develop our proposed scheme to achieve: 1) angular domain user separation, and 2) SNR maximization at each user. The performance evaluation of the proposed scheme is carried out through simulations, which show that under interference limited conditions the proposed scheme provides capacity performance better than linear zero-force-beamforming (LZFBF), without the requirement of full channel state information of all the users.

It is expected that drones will take a major role in the connected smart cities of the future. They will be delivering goods and merchandise, serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security of smart cities. However, pervasive use of drones for future smart cities also brings together several technical and societal concerns and challenges that needs to be addressed, including in the areas of cybersecurity, privacy, and public safety. Drones, while can be used for the betterment of the society, can also be used by malicious entities to conduct physical and cyber attacks, and threaten the society. The goal of this survey paper is to review various aspects of drones in future smart cities, relating to cybersecurity, privacy, and public safety. We will also provide representative results on cyber attacks using drones.

In order to cope with the phenomenal growth of mobile data traffic, unlicensed spectrum can be utilized by the Long Term Evolution (LTE) cellular systems. However, ensuring fair coexistence with WiFi is a mandatory requirement. In one approach, periodically configurable transmission gaps can be used to facilitate a coexistence between WiFi and LTE. In this paper, a Multi-Armed Bandit (MAB) based dynamic duty cycle selection method is proposed for configuration of transmission gaps ensuring a better coexistence for both technologies. Then the concept is further strengthened with downlink power control mechanism using the same algorithm leading to a high energy efficiency and interference reduction. Performance results are given for different user equipment and WiFi station densities in which it is shown that significant improvements in overall throughput and energy efficiency can be achieved.

While the newly envisioned smart(er) grid (SG) will result in a more efficient and reliable power grid, its collection and use of fine-grained meter data has widely raised concerns on consumer privacy. While a number of approaches are available for preserving consumer privacy, these approaches are mostly not very practical to be used due to two reasons. 1) Since the data is hidden, this reduces the ability of the utility company to use the data for distribution state estimation. 2) The approaches were not tested under realistic wireless infrastructures that are currently in use. In this paper, we propose to implement a meter data obfuscation approach to preserve consumer privacy that has the ability to perform distribution state estimation. We then assess its performance on a large-scale advanced metering infrastructure (AMI) network built upon the new IEEE 802.11s wireless mesh standard. For the data obfuscation approach, we propose two secure obfuscation value distribution mechanisms on this 802.11s-based wireless mesh network (WMN). Using obfuscation values provided via this approach, the meter readings are obfuscated to protect consumer privacy from eavesdroppers and the utility companies while preserving the utility companies' ability to use the data for state estimation. We assessed the impact of this approach on data goodput, delay, and packet delivery ratio (PDR) under a variety of conditions. Simulation results have shown that the proposed approach can provide very similar performance to that of nonprivacy approach with negligible overheads on the meters and network.

In wireless cellular networks with densely deployed base stations, knowing the velocities of mobile devices is key to avoiding call drops and improving the quality of service to the user equipments (UEs). A simple and efficient way to estimate a UE's velocity is by counting the number of handovers made by the UE during a predefined time window. Indeed, handover-count based mobility state detection has been standardized since long term evolution (LTE) Release-8 specifications. The increasing density of small cells in wireless networks can help in accurate estimation of velocity and mobility state of a UE. In this paper, we model densely deployed small cells using stochastic geometry, and then analyze the statistics of the number of handovers as a function of UE velocity, small-cell density, and handover count measurement time window. Using these statistics, we derive approximations to the Cramer-Rao lower bound (CRLB) for the velocity estimate of a UE. Also, we determine a minimum variance unbiased (MVU) velocity estimator whose variance tightly matches with the CRLB. Using this velocity estimator, we formulate the problem of detecting the mobility state of a UE as low, medium, or high-mobility, as in LTE specifications. Subsequently, we derive the probability of correctly detecting the mobility state of a UE. Finally, we evaluate the accuracy of the velocity estimator under more realistic scenarios such as clustered deployment of small cells, random way point (RWP) mobility model for UEs, and variable UE velocity. Our analysis shows that the accuracy of velocity estimation and mobility state detection increases with increasing small cell density and with increasing handover count measurement time window.

Indoor localization has been widely studied due to the inability of GPS to function indoors. Numerous approaches have been proposed in the past and a number of these approaches are currently being used commercially. However, little attention was paid to the privacy of the users especially in the commercial products. Malicious individuals can determine a client's daily habits and activities by simply analyzing their WiFi signals and tracking information. In this paper, we implemented a privacy-preserving indoor localization scheme that is based on a fingerprinting approach to analyze the performance issues in terms of accuracy, complexity, scalability and privacy. We developed an Android app and collected a large number of data on the third floor of the FIU Engineering Center. The analysis of data provided excellent opportunities for performance improvement which have been incorporated to the privacy-preserving localization scheme.

In this study, we investigate angle-of-arrival (AOA) and received signal strength (RSS) based localization methods for visible light communication (VLC) systems. We show that while AOA-based localization allows the receiver to locate itself via a least squares estimator by exploiting the directionality of light-emitting diodes (LEDs), RSS-based approach takes Lambertian pattern of LEDs into account and better deals with further improving the localization accuracy via a nonlinear least squares (NLS) estimator. In order to reduce the complexity of the NLS estimator, we develop an analytical learning rule based on the Newton-Raphson method and use the result of AOA-based localization as an initial point for the learning rule. As a benchmark, we also derive generic analytical expressions of the Cramer-Rao lower bound (CRLB) for RSS-based localization.

Radio-Frequency IDentification (RFID) systems carry critical importance for reaping the benefits of a diverse set of applications that will be enabled by the emerging Internet of Things (IoT). In particular, RFID based position estimation and tracking will facilitate a wide array of location based services. Considering a monostatic RFID configuration with a three dimensional antenna radiation pattern, this paper investigates the limits of localization accuracy for received signal strength (RSS) based wireless localization using passive UHF RFID systems. The Cramer-Rao Lower Bound (CRLB) for the localization accuracy are derived, and are compared with the localization accuracy of maximum likelihood estimators for various RFID antenna topologies. Numerical results show that due to the directional antenna pattern used in RFID systems, the localization accuracy can degrade at blind locations that remain outside of the RFID readers' main beam patterns.Cramer-Rao Lower Bound (CRLB) for the localization accuracy are derived, and are compared with the localization accuracy of maximum likelihood estimators for various RFID antenna topologies. Numerical results show that due to the directional antenna

In order to coexist with the WiFi systems in the unlicensed spectrum, Long Term Evolution (LTE) networks can utilize periodically configured transmission gaps. In this paper, considering a time division duplex (TDD)-LTE system, we propose a Q-Learning based dynamic duty cycle selection technique for configuring LTE transmission gaps, so that a satisfactory throughput is maintained both for LTE and WiFi systems. By explicitly taking the impact of IEEE 802.11n beacon transmission mechanism into account, we evaluate the coexistence performance of WiFi and LTE using the proposed technique. Simulation results show that the proposed approach can enhance the overall capacity performance by 19% and WiFi capacity performance by 77%, hence enabling effective coexistence of LTE and WiFi systems in the unlicensed band.

The use of small cell deployments in heterogeneous network (HetNet) environments is expected to be a key feature of 4G networks and beyond, and essential for providing higher user throughput and cell-edge coverage. However, due to different coverage sizes of macro and pico base stations (BSs), such a paradigm shift introduces additional requirements and challenges in dense networks. Among these challenges is the handover performance of user equipment (UEs), which will be impacted especially when high velocity UEs traverse picocells. In this paper, we propose a coordination-based and context-aware mobility management (MM) procedure for small cell networks using tools from reinforcement learning. Here, macro and pico BSs jointly learn their long-term traffic loads and optimal cell range expansion, and schedule their UEs based on their velocities and historical rates (exchanged among tiers). The proposed approach is shown to not only outperform the classical MM in terms of UE throughput, but also to enable better fairness. In average, a gain of up to 80% is achieved for UE throughput, while the handover failure probability is reduced up to a factor of three by the proposed learning based MM approaches.

The articles in this special issue focus on recent advances in the field of heterogeneous cellular networking.

In this paper, a green wireless communication system in which base stations are powered by renewable energy sources is considered. This system consists of a capacity-constrained renewable power supplier (RPS) and a base station (BS) that faces a predictable random connection demand from mobile user equipments (UEs). In this model, the BS, which is powered via a combination of a renewable power source and the conventional electric grid, seeks to specify the renewable power inventory policy, i.e., the power storage level. On the other hand, the RPS must strategically choose the energy amount that is supplied to the BS. An M/M/1 make-to-stock queuing model is proposed to investigate the decentralized decisions when the two parties optimize their individual costs in a noncooperative manner. The problem is formulated as a noncooperative game whose Nash equilibrium (NE) strategies are characterized to identify the causes of inefficiency in the decentralized operation. A set of simple linear contracts are introduced to coordinate the system so as to achieve an optimal system performance. The proposed approach is then extended to a setting with one monopolistic RPS and N BSs that are privately informed of their optimal energy inventory levels. In this scenario, we show that the widely used proportional allocation mechanism is no longer socially optimal. To make the BSs truthfully report their energy demand, an incentive compatible (IC) mechanism is proposed for our model. Simulation results show that using the green energy can present significant traditional energy savings for the BS when the connection demand is not heavy. Moreover, the proposed scheme provides valuable energy cost savings by allowing the BSs to smartly use a combination of renewable and traditional energy, even when the BS has a heavy traffic of connections. Also, the results show that performance of the proposed IC mechanism will be close to the social optimal when the green energy production capacity increases.

One of the most important features of the Smart Grid (SG) is real-time self-assessment which may threat that target power system stability. In order to improve robustness of power systems against such attacks, accurate estimation of the power system operation is required and conventional power flow methods should be upgraded. In this paper, we derive minimum variance unbiased estimators (MVUEs) for active power based on the voltage phase at each node of the power system. The state variables are the voltage phases and the received measurement signals are active power measurements. The proposed method is implemented on a four-bus test system. Three scenarios are defined to investigate the effect of covariance matrix topology on the estimation accuracy. The results shows that lower correlation between the noise vector elements leads to a more accurate estimation of power system operation.

Device-to-device (D2D) communication enables mobile devices to directly communicate with each other without the help of infrastructure and to reuse radio resources within cellular networks. When D2D user equipments (UEs) use the same radio resources for discovery and communication as the cellular network, intercell interference (ICI) may degrade the D2D discovery performance. Therefore, ICI between the cellular network and D2D UEs should be coordinated to avoid performance degradation. In this paper, we evaluate the benefits of time-domain intercell interference coordination (ICIC) approaches in D2D discovery by using almost blank subframes (ABS). Also, we present the benefits of using multi-hop discovery.

The time domain inter-cell interference coordination (eICIC) mechanism specified in LTE Rel. 10 improves the throughput of picocell-edge users by protecting them from macrocell interference. On the other hand, it also degrades the aggregate capacity in macrocell because the macro base station (MBS) does not transmit data during the coordinated subframes (CSFs). The MBS data transmission at a reduced power level during CSFs, which is referred to as FeICIC in LTE Rel. 11, can improve the capacity in macrocell while not causing high interference to the picocell users. Using stochastic geometry, this paper lays a theoretical foundation for analyzing the heterogeneous networks (HetNets) with reduced power subframes and range expansion bias (REB). The analytic expression for average capacity is derived and validated through Monte Carlo simulations. Our analysis shows that with optimum parameter settings FeICIC can provide substantially better performance than eICIC in terms of both aggregate capacity in a cell and fairness among the users.

Handover is one of the most critical functions in a cellular network and it supports the mobility of user equipments (UEs) across multiple cells. It is usually based on signal measurements from candidate base stations and filtering the signal measurements becomes necessary due to channel imperfections. In LTE, handover measurements are filtered using layer 3 (L3) filters in the UE, whereby the UE checks for the handover entry condition at discrete sampling instants of the L3 filter. The selection of sampling period becomes crucial in improving the handover performance of the network. Therefore, it is necessary to understand the trade-offs related to the sampling period of an L3 filter. The goal of this paper is to derive the handover failure (HF) expression as a function of L3 sampling periods in LTE. A geometric handover model is used for derivations and all analytical expressions are verified through simulations.

In an uncoordinated network, the link-level performance of a wireless receiver might degrade significantly due to the interference from other transmitters that share the same spectrum. As a solution, in this study, the concept of partially overlapping tones (POT) is introduced. In POT, interference energy observed at a victim receiver is mitigated by partially overlapping the individual subcarriers via an intentional carrier frequency offset between the links. It is argued that the self-interference arising due to the use of POT can be more easily addressed than the dominant other-user interference, potentially yielding higher spectral efficiencies with POT. Using a spatial Poisson point process based framework, a tractable bit error rate analysis is provided to demonstrate potential benefits emerging from POT in system-level scenarios.

Poisson Point Process (PPP) models provide important insights for the analysis of heterogeneous networks (HetNets). Recently, several theoretical results which investigate the outage and the capacity of HetNets have been developed based on PPPs. The goal of the present paper is to provide exact analytical expressions for the capacity of range expanded and interference coordinated HetNets, and validate these theoretical analysis through well-documented computer simulations. Both analytical and simulated capacity results are obtained for various range expansion and interference coordination scenarios, and are shown to have good agreement.

Interference cancellation is expected to have significant importance for next-generation wireless communication systems due to various co-channel deployment scenarios and dense frequency reuse. In this study, an interference cancellation receiver that exploits the unique characteristics of single-carrier frequency-division multiple access based systems is proposed. The proposed receiver suppresses the co-channel dominant interference by employing a soft window to the frequency-domain samples where the desired and interfering signals overlap. In order to improve the performance, demodulation and regeneration stages are repeated multiple times by initially accommodating a group of reliable symbols before the iterations. The simulation results indicate that proposed methods work particularly well for low overlap ratios compared to interference coordination and no cancellation methods.

In a wireless communication medium, the transmitted signal reaches to the receiver antenna after passing through multipath channel. The difference in path delays and mobility cause the transmitted signal spread both in time and frequency, resulting in inter-symbol (or adjacent block) and inter-carrier (or adjacent channel) interference, respectively. The pulse shaping and matching filters used in the transceivers also impact the level of dispersion in both time and frequency. In this study, the composite effects of wireless medium and the used filters are investigated on the equalization of multicarrier systems. For this purpose, considering a symbol-spaced tap model for the equalization, the number of effective taps in both time and frequency domains is obtained via Akaike information criterion (AIC) for different signal-to-noise ratios (SNRs). Subsequently, a method which characterizes the equalization complexity of the receiver as a function of the transmit pulse shape, communication medium, the receive filter response, and SNR is proposed.

In this paper, we characterize the relation between handover failure and ping-pong rates in a 3GPP heterogeneous network scenario as a function of relevant system parameters such as time-to-trigger, user equipment velocity, range expansion bias, etc. Under the assumptions that the picocell coverage and radio link failure areas are circular regions, and that users follow linear trajectories, handover failure and ping-pong rates are derived in closed-form expressions. Simulation results verify the accuracy of our analytical derivations, which shed new light on key aspects of the handover process in a heterogeneous network.

ABSTRACT In this paper, theoretical lower bounds on performance of linear least‐squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least‐squares (NLS) position estimators are quantified. In addition, two techniques are proposed in order to improve the performance of the LLS approach. First, a reference selection algorithm is proposed to optimally select the measurement that is used for linearizing the other measurements in an LLS estimator. Then, a maximum likelihood approach is proposed, which takes correlations between different measurements into account in order to reduce average position estimation errors. Simulations are performed to evaluate the theoretical limits and to compare performance of various LLS estimators. Copyright © 2010 John Wiley & Sons, Ltd.

Interference cancellation will become more important in future wireless communication systems due to various co-channel deployment scenarios and denser frequency reuse. In this study, an interference cancellation receiver is proposed which can be used for single-carrier frequency-division multiple access based systems. In the proposed receiver, impact of dominant interference (DI) is mitigated by an iterative blanking algorithm. If the DI is detected on the desired band, overlapping DI spectrum is blanked, followed by demodulation and regeneration of the desired signal. Afterward, the blanked overlapping samples are replaced by the corresponding samples of the regenerated signal. In order to improve the performance, demodulation and regeneration stages can be repeated multiple times. Performance of the proposed approach is compared with those of interference coordination and no cancellation through link-level and system-level simulations.

In a typical macrocell network, all macrocell mobile stations (mMSs) are synchronized to the macrocell base station (mBS) during the uplink. The mMSs that are closer to the mBS transmit their signals at a later time instant so that signals of all the all mMSs arrive at the mBS at the same time. However, signals of the mMSs reach at the femtocell base stations (fBSs) with different delays, which may cause interference problems for femtocell users. For orthogonal frequency division multiple access (OFDMA) based networks, interference due to delays larger than the cyclic prefix of the desired signal will appear as inter-carrier interference (ICI) and inter-symbol-interference (ISI). In this paper, statistics of the ICI power received from the mMSs at the fBSs is derived considering different locations of the fBSs in the macrocell network. The concept of zero-ICI region is introduced and its implications for coexisting macrocell/femtocell networks are presented. Theoretical findings are verified via simulation results.

Range expansion (RE), when applied with inter-cell interference coordination (ICIC) may have significant benefits for improving the throughput and fairness of macrocell/picocell networks. In this paper, first, benefits of RE in the uplink and downlink are reviewed, emphasizing its definitive merit for uplink communications. It is shown that while RE also improves the fairness in the downlink, it may seriously degrade the total system capacity if no ICIC is utilized. Then, a novel cell-selection technique that capitalizes on subframe-blanking at the macrocell is introduced for improving the downlink capacity and fairness. System level simulation results show that the proposed cellselection method adaptively achieves a good balance between sum capacity and fairness, and can be used in place of biasbased cell-selection.

In split spectrum macrocell-femtocell networks, certain portion of the available spectrum resources are dedicated to each tier in order to avoid interference problems among the tiers. In such a setting, effective partitioning of the spectrum resources carries critical importance for maximizing the total capacity and satisfying quality of service (QoS) requirements of the users within each tier. This paper proposes spectrum splitting methods for a macrocell-femtocell network considering factors other than the capacity maximization. First, a capacity maximizing spectrum splitting solution is derived based on certain simplifying assumptions. Then, a modified QoS-oriented fairness metric is introduced which captures important characteristics of tiered network structures that are not captured by the Jain's fairness index. Finally, a spectrum splitting strategy that simultaneously considers capacity maximization, fairness constraints, and QoS constraints is proposed. Example numerical results show that while only considering total capacity maximization may yield unfair spectrum splitting, a more balanced spectrum allocation can be achieved by also considering fairness and QoS constraints.

Access configuration of femtocell networks carries critical importance due to the resulting interference scenarios. Especially in closed-access femtocell networks, there might be significant interference between the femtocell and the macrocell users. In this paper, we evaluate the capacity of closed access femtocell networks employing various dynamic spectrum reuse techniques. When there is a macrocell user in the vicinity of a femtocell, the femtocell may dynamically decide not to reuse the spectrum of the macrocell user to avoid interference. We discuss and evaluate the following three decision criteria for this purpose: maximum sum capacity, minimum macrocell loss, and minimum effective interference. Computer simulations in realistic settings are provided to demonstrate possible gains with the proposed methods.

Femtocell networks may significantly improve the capacity of next-generation cellular systems due to more efficient utilization of spectrum resources and better link qualities for mobile users. In this paper, we investigate the performance of open-access femtocell networks for different scenarios, where macrocell users are allowed to join a particular femtocell if it is preferable to do so. Both dedicated-channel and co-channel femtocell deployments are considered. We investigate different cell selection metrics, and argue that a capacity-based cell selection typically results in larger capacities for the users. Impact of wall penetration loss and how it relates to the capacities of indoor and outdoor users are also evaluated. Extensive downlink simulation results are provided to justify various system tradeoffs.

Coexistence of narrowband (NB) and multicarrier technologies will be a major concern in next generation wireless communication systems due to the co-channel interference (CCI) problem. In this paper, an efficient CCI cancellation method is proposed that may be utilized for an improved coexistence. The method treats both co-channel signals as desired signals and enhances them in an iterative manner. Through computer simulations, it is demonstrated that it yields significant gains in the symbol error rate (SER) performance of both the NB and multicarrier systems. Its computational complexity is compared with the complexity of the joint demodulation technique and it is shown to be much lower.

Cognitive radio is an enabling technology for efficient utilization of radio spectrum. In this paper, an orthogonal frequency division multiple access (OFDMA) based cognitive (opportunistic) network is considered, which co-exists with a macrocell network through utilizing its unused subcarriers. We particularly consider the uplink opportunity detection problem by the opportunistic network, where accurate synchronization to the macrocell network is crucial for minimizing the interference received from the macrocell users. After demonstrating the impact of synchronization on the inter-carrier-interference (ICI) observed at the opportunistic network, an improved blind first-user synchronization technique is proposed, and its statistics are analyzed. Through computer simulations it is shown that the proposed technique yields less interference and better opportunities for the opportunistic network.

Interference between neighboring femtocells may have a critical impact on their performances. In this paper, impact of resource spreading is investigated on the capacity of two neighboring femtocell networks. It exploits the fact that at a given time, the number of active femtocell users may be much smaller than the maximum allowed, and the allocated spectrum may typically be under-utilized. The behavior of the capacities of two neighboring femtocell networks (one being under-utilized, and the other being fully-utilized) with respect to the spreading factor are analyzed for different interference conditions. Also, a rate-constrained resource utilization approach in the underutilized network is discussed. Through spreading the resources of the under-utilized femtocell, it is shown to improve the capacity of the fully utilized femtocell network in most scenarios.

Femtocell networks have the potential of significantly improving the capacity of next-generation cellular systems. However, interference to/from the macrocell network is a critical problem affecting the capacity. Co-channel operation with closed-subscriber group (CSG) access offers a large bandwidth to be shared by the users of the femtocell network; however, it also introduces significant interference to/from the macrocell network. On the other hand, femtocell networks using dedicated spectrum assignment and open access mode have insignificant interference concerns, with the trade-off of available spectrum limitations. In this study, we investigate interference vs. bandwidth tradeoffs in different types of femtocell network deployments through the help of channel capacity formulations. Related downlink simulation results are provided to compare the capacities of different deployment modes and the impact of key system parameters.

Separating user signals in the uplink (UL) of an orthogonal frequency division multiple access (OFDMA) system without access to the subcarrier assignment scheme (SAS) is a challenging task, but it can have a number of useful applications. In this paper, a semi-blind user separation algorithm is proposed, which assumes time synchronization and availability of information on the type of SAS used. The proposed algorithm estimates the carrier frequency offsets and time delays of each frequency allocation block by exploiting the cross-correlations over pilot subcarriers. A clustering method to group the blocks is employed, where each group belongs to a different user. Mathematical model of the proposed algorithm is presented, and possible user separation applications in OFDMA-based femtocells are discussed. Feasibility of the algorithm is proved through practical simulations.

Accurate detection of spectrum opportunities within the frequency band of an orthogonal frequency division multiple access (OFDMA) system carries critical importance for OFDMAbased cognitive radios. In this paper, we analyze the opportunity detection performances of energy detection and ESPRIT (estimation of signal parameters by rotational invariance techniques) algorithms in the presence of timing misalignments in uplink (UL) OFDMA. For the energy detector, the statistics of subcarrier power are derived considering timing misalignments, and they are verified through computer simulations. Using these statistics, which take inter-carrier-interference (ICI) effects into account, receiver operating characteristics (ROCs) of the energy detector receiver are obtained. It is shown that energy detection has a considerably better performance than ESPRIT, especially when the subcarrier assignment changes frequently. Moreover, a closed form expression is derived for the UL-OFDMA synchronization point that minimizes the ICI. Finally, it is shown that employing resource allocation blocks with larger sizes in the primary network yields better opportunities for the cognitive radio.

Accurate detection of spectrum opportunities within the frequency band of an orthogonal frequency division multiple access (OFDMA) system is a critical requirement for the realization of a coexisting cognitive radio. In this paper, we analyze the opportunity detection performances of energy detection and ESPRIT algorithms in the presence of timing misalignments in uplink (UL) OFDMA. For the energy detector, the statistics of subcarrier power are derived, and the accuracy of these derivations is verified through simulations. The feasibility of opportunity detection in an asynchronous UL-OFDMA system is proven. Also, it is shown that energy detection has a considerably better performance than the ESPRIT algorithm especially when the subcarrier assignments change frequently.

A common technique for wireless positioning is to estimate time-of-arrivals (TOAs) of signals traveling between a target node and a number of reference nodes, and then to determine the position of the target node based on those TOA parameters. In determining the position of the target node from TOA parameters, linear or nonlinear least-squares (LS) estimation techniques can be employed. Although the linear LS techniques are suboptimal in general, they facilitate low- complexity position estimation. In this paper, performance of various linear LS techniques are compared, and suboptimality of the linear approach is quantified in terms of the Cramer-Rao lower bound (CRLB). Simulations are performed to compare the performance of the linear LS approaches versus the CRLBs for linear and nonlinear techniques.

In this paper, we propose a simple model-based time-of-arrival (TOA) estimation technique for multi-band orthogonal frequency division multiplexing (MB-OFDM) signals based on the ECMA-368 standard [1]. The proposed technique does not need oversampling at the receiver. The key idea is to minimize the energy leakage from the first channel path due to mis-sampling. We use realistic channel models and assume that no channel statistical information is known at the receiver. In our approach, the multi-band signals are coherently combined in order to improve the resolution of the TOA estimation. We compare the performance of the proposed scheme with the well-known maximum-likelihood based algorithm, namely, space-alternating generalized expectation-maximization (SAGE). Simulations based on the ECMA-368 standard parameters in both IEEE 802.15.3a and IEEE 802.15.4a standard channel models show that our approach outperforms the SAGE algorithm by avoiding the local maxima. Furthermore, we have also shown that our approach can achieve a root mean square error of TOA estimation much smaller than the receiver sampling interval and it is robust to the narrowband interference.

Linear least squares (LLS) estimation is a low complexity but sub-optimum method for estimating the location of a mobile terminal (MT) from some distance measurements. It requires selecting one of the fixed terminals (FTs) as a reference FT for obtaining a linear set of expressions. However, selection of the reference FT is commonly performed arbitrarily in the literature. In this paper, a method for selection of the reference FT is proposed, which improves the location accuracy compared to a fixed selection of the reference FT. Moreover, a covariance- matrix based LLS estimator is proposed in line of sight (LOS) and non-LOS (NLOS) environments which further improves accuracy since the correlations between the observations are exploited. Simulation results prove the effectiveness of the proposed techniques.

In this paper, a novel joint time-of-arrival (TOA) estimation and localization technique for ultra-wideband (UWB) sensor network applications is proposed. To our best knowledge, most works reported in the literature set the threshold for a TOA estimator based on the parameters of the received signal only. In this work, we use the residual localization error as a metric to accurately choose the threshold values. A sub-optimal version of the proposed algorithm with lower computational complexity is also discussed and the improvements are demonstrated using Monte-Carlo simulations based on the IEEE 802.15.4a channel models.

In this paper, we analyze the localization accuracy of a linear least-squares technique both theoretically and via simulations. Closed-form expressions for the mean square localization errors are derived both for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios and compared with the Cramer-Rao lower bound (CRLB). Analysis reveals that the localization error tends to increase as the mobile terminal moves away from the reference base-station.

Non-line-of-sight (NLOS) identification and mitigation carries significant importance in wireless positioning systems. In this paper, the authors propose a novel NLOS identification technique based on the multipath channel statistics such as the kurtosis, the mean excess delay spread, and the root mean square delay spread. In particular, the IEEE 802.15.4a ultrawideband channel models are used as examples and the above statistics are found to be well modeled by log-normal random variables. Subsequently, a joint likelihood ratio test is developed for LOS/NLOS identification. Simulation results show that correct identification can be achieved with over 90% of the realizations for most channel models. A weighted least squares localization algorithm is also developed using the NLOS information, and accuracy gains with respect to a conventional least squares algorithm are demonstrated via Monte-Carlo simulations.

Ultrawideband impulse radio systems offer two kinds of processing gains that can be adapted based on the interference level in the system so that quality of service requirements are fulfilled. An adaptive assignment scheme for two types of multiple-access parameters in cluster-based wireless sensor networks is investigated. A mathematical framework is developed for asynchronous communications using a Gaussian approximation method to model the multiple-access interference in two cases: one with fixed frame duration, where the goal is to increase the average throughput, and the other with fixed symbol duration, where the goal is to increase the network lifetime. Extension of the analysis to multipath channels is carried out, and the validity of the Gaussian approximation is investigated using the Kullback-Leibler distance.

Impulse-radio (IR) ultra-wideband (UWB) communications can be achieved with various transceiver architectures. Even though matched filtering is the optimal signal detection technique with Nyquist rate sampling, differentially coherent and non-coherent transceiver architectures may be favorable at lower rate samples. In this paper, we present a unified performance analysis approach for different IR-UWB transceiver types employing various modulation options and using the captured energy statistics at sub-Nyquist sampling rates. Energy capture versus robustness against noise is demonstrated as a trade-off for stored-reference transceivers. Theoretical results are verified via simulations

In this paper, we consider time of arrival estimation of ultra-wideband signals based on low-rate samples that are obtained after a square-law device. Signal conditioning techniques based on a bank of cascaded multi-scale energy collection filters and wavelets are introduced, where correlations across multiple scales are exploited for edge and peak enhancements towards a more accurate detection. The performances of the discussed algorithms are tested on IEEE 802.15.4a residential line-of-sight (LOS) channels.

Ultrawideband (UWB) is an attractive physical layer technology for wireless sensor networks due to its unique characteristics. Flexibility in adjusting the processing gain of UWB systems makes it possible to tune the data rate and transmission range to fulfill the requirements of specific applications. Conventional systems assign identical multiple access parameters to all users regardless of the signal-to-interference plus noise ratio of the received signal. An adaptive assignment scheme for multiple access parameters in cluster based wireless sensor networks is investigated. First, an orthogonal time hopping sequence construction is proposed for synchronous communications (downlink), where the number of pulses per symbol are adjusted to meet the bit error rate requirement of an application. Then, adaptation of multiple access parameters in asynchronous scenarios (uplink) is evaluated using a Gaussian approximation method to model the multiple access interference in two cases: one with fixed frame duration, where the goal is to increase the average throughput, and the other with fixed symbol duration, where the goal is to increase the network lifetime. Finally, a mathematical framework is developed for approximating the interference when the number of pulses per symbol and the frame duration vary.

Time hopping (TH) is commonly used with impulse radio (IR) based ultrawideband (UWB) systems as a multiple access scheme. In such systems, TH codes must he carefully designed to optimize the system performance. In this paper, TH code design for both synchronous and asynchronous UWB-IR systems are studied. For synchronous systems, a novel code assignment algorithm that depends on the number of users and delay spread of the channel is proposed. Then, the proposed codes are extended to quasi-synchronous scenarios. For asynchronous systems, frequency hopping (FH) codes obtained by finite field theory are applied to TH-IR, and their autocorrelation and crosscorrelation properties are investigated. A multiuser performance analysis method is proposed which makes use of code correlation characteristics.