Spectrum Coexistence Research Articles

Toward Spectrum Coexistence: First Demonstration of the Effectiveness of Boresight Avoidance between the NRAO Green Bank Telescope and Starlink Satellites

Abstract NRAO and SpaceX have been engaged in coordinated testing efforts since Fall 2021, including conducting experiments on different interference avoidance schemes for the Karl G. Jansky Very Large Array in New Mexico, and the Green Bank Telescope (GBT) inside the National Radio Quiet Zone in West Virginia. The Starlink system is capable of avoiding direct illumination of telescope sites with their adaptive tasking to place downlink beams far away. Nevertheless, even satellites operating in this mode can potentially present strong signals into the telescope’s receiver system if they pass close to the telescope’s main beam at the boresight. For additional protection, Starlink satellites can either momentarily redirect or completely disable their downlink channels while they pass within some minimum angular separation threshold from the telescope’s boresight, methods that are referred to as “telescope boresight avoidance.” In two separate experiments conducted since Fall 2023, NRAO and SpaceX arranged to have the GBT observe a fixed R.A./decl. position in the sky, chosen to have a large number of close-to-boresight Starlink passages. Preliminary analysis from these two experiments illustrates the feasibility of these avoidance methods to significantly reduce, if not eliminate, the negative impact of close-to-boresight satellite passages. Importantly, these experiments demonstrate the value of continuing cooperative efforts between NRAO and SpaceX, and expanding cooperation between the radio astronomy and satellite communities more generally.

A robust joint frequency spectrum and power allocation strategy in a coexisting radar and communication system

The resource allocation technique is of great significance in achieving frequency spectrum coexistence in Joint Radar-Communication (JRC) systems, by which the problem of radio frequency spectrum congestion can be well alleviated. A Robust Joint Frequency Spectrum and Power Allocation (RJFSPA) strategy is proposed for the Coexisting Radar and Communication (CRC) system. Specifically, we consider the uncertainty of target Radar Cross Section (RCS) and communication channel gain to formulate a bi-objective optimization model. The joint probabilities that the Cramér-Rao Lower Bound (CRLB) of each target satisfying the localization accuracy threshold and the Communication Data Ratio (CDR) of each user satisfying the communication threshold are simultaneously maximized, under the constraint of the total power budget. A Three-Stage Alternating Optimization Method (TSAOM) is proposed to obtain the Best-Known Pareto Subset (BK-PS) of this problem, where the frequency spectrum, radar power, and communicator power are allocated using the greedy search and standard convex optimization methods, respectively. Simulation results confirm the effectiveness of the proposed RJFSPA strategy, compared with the resource allocation methods in a uniform manner and that ignores the uncertainties. The efficiency of the TSAOM is also verified by the comparison with the exhaustive search-based method.

A Physical Testbed and Open Dataset for Passive Sensing and Wireless Communication Spectrum Coexistence

A Physical Testbed and Open Dataset for Passive Sensing and Wireless Communication Spectrum Coexistence

A Tractable Framework for Spectrum Coexistence Between Satellite Receivers and Terrestrial Networks

A Tractable Framework for Spectrum Coexistence Between Satellite Receivers and Terrestrial Networks

Deep Reinforcement Learning for NextG Radio Access Network Slicing With Spectrum Coexistence

Reinforcement learning (RL) is applied for dynamic admission control and resource allocation in NextG radio access network slicing. When sharing the spectrum with an incumbent user (that dynamically occupies frequency-time blocks), communication and computational resources are allocated to slicing requests, each with priority (weight), throughput, latency, and computational requirements. RL maximizes the total weight of granted requests over time beyond myopic, greedy, random, and first come, first served solutions. As the state-action space grows, Deep Q-network effectively admits requests and allocates resources as a low-complexity solution that is robust to sensing errors in detecting the incumbent user activity.

Radio Dynamic Zones: Motivations, Challenges, and Opportunities to Catalyze Spectrum Coexistence

The wireless spectrum is a natural resource upon which we all depend in more ways than we realize. While our personal and professional lives thrive on mobile broadband, a plethora of other applications, such as weather forecasting, climate science, astronomy, space exploration, and civil/military navigation also critically depend on the radio spectrum. Although these technologies are vastly different, they all share a common need for more spectrum and increasingly converge to the same bands. Currently, technological and policy frameworks are insufficient to facilitate the required mutual trust across distinct applications, and ensure their protection and harmonious coexistence. This paper discusses Radio Dynamic Zones (RDZ) as experimental platforms for large-scale research on spectrum coexistence of disparate stakeholders. We consider three representative stakeholder technologies that have experienced exponential growth in spectrum needs and capabilities: consumer broadband, microwave remote sensing, and radio astronomy. We detail emerging coexistence issues across these stakeholders that can inform the design of RDZs. We then conceptualize an RDZ architecture and desired features and discuss grand research challenges towards their realization.

Optimization of LPI-FDA-MIMO Radar and MIMO Communication for Spectrum Coexistence

This paper studies the spectral efficiency of a multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) and a MIMO communication system in the same frequency band. To suppress mutual interference for both systems, and enhance low probability of intercept (LPI) capability of radar system, we propose a joint design of the transmit waveform and receive filter of radar, and the code-book employed by the communication system to form its space-time code matrix. Our problem formulationis to maximize the signal-to-interference-plus-noise ratio (SINR) and simultaneously minimize the probability of target detection of the radar signal under the peak-to-average ratio and communication rate constraints. Since the design problem is non-convex, we first transform it into three sub-optimization problems. An iterative algorithm is then developed to solve these subproblems to obtain the solution of the original problem. Simulation results show that the devised approach can effectively reduce the mutual interference as well as increase the SINR and LPI for radar system.

Improved Spectrum Coexistence in 2.4 GHz ISM Band Using Optimized Chaotic Frequency Hopping for Wi-Fi and Bluetooth Signals.

Efficiently managing coexistence is crucial for achieving high-quality wireless communication in the Industrial, Scientific, and Medical (ISM) band where multiple wireless communication systems operate. Coexistence problems between Wi-Fi and Bluetooth Low Energy (BLE) signals are especially significant due to their shared frequency band, which often leads to interference and a reduced performance for both systems. Therefore, effective coexistence management strategies are essential for ensuring the optimal performance of Wi-Fi and Bluetooth signals in the ISM band. In this paper, the authors conducted a study to investigate coexistence management in the ISM band by evaluating four frequency hopping techniques: random, chaotic, adaptive, and an optimized chaotic technique proposed by the authors. The optimized chaotic technique aimed to minimize interference and ensure zero self-interference among hopping BLE nodes by optimizing the update coefficient. Simulations were conducted in an environment with existing Wi-Fi signal interference and interfering Bluetooth nodes. The authors compared several performance metrics, including the total interference rate, total successful connection rate, and trial execution time for channel selection processing time. The results indicated that the proposed optimized chaotic frequency hopping technique achieved a better balance between reducing interference with Wi-Fi signals, achieving a high success rate for connecting BLE nodes, and requiring minimal trial execution time. This makes it a suitable technique for managing interference in wireless communication systems. While the proposed technique had a higher interference than the adaptive technique for small numbers of BLE nodes, for larger numbers of nodes it had a much lower interference than the adaptive technique. The proposed optimized chaotic frequency hopping technique provides a promising solution for effectively managing coexistence in the ISM band, particularly between Wi-Fi and BLE signals. It has the potential to improve the performance and quality of wireless communication systems.

Beamforming and Waveform Designing for Spectrum Coexistence System Based on Constructive Interference

In this letter, a joint communication beamforming and radar transmit waveform design method is proposed for radar and communication coexistence (RCC) systems. The optimization problem is designed to maximize the radar signal-to-interference-plus-noise ratio (SINR), under the constraints of communication constructive interference (CI), radar waveform similarity and constant modulus. A novel block fading (BF) algorithm is developed for tackling the non-convexity of the problem constructed, where the closed-form sub-optimal solutions are obtained by transforming the original problem into an element-by-element phase angle problem. Finally, the numerical simulation results verify the superiority of the proposed algorithm on the performance of radar detection and multi-user interference (MUI) elimination.

Fourier Domain Millimeter-Wave Imaging Using Noncooperative 5G Communications Signals

The spectrum of millimeter-wave frequencies is becoming increasingly crowded due to the advances in fifth-generation (5G) communications and automotive radar, leading to greater interest in spectrum coexistence for functions like sensing and communications. While most research has focused on minimizing interference between sensing and communications, we take a different approach that leverages dense communications signal environments for passive sensing applications. We present a millimeter-wave imaging system that captures information in the spatial frequency (Fourier) domain, similar to concepts used in radio astronomy. In contrast to traditional millimeter-wave sensing and imaging approaches, Fourier-domain image formation is improved as more incoherent signals are included in the environment. In particular, multiple millimeter-wave communications signals generate spatiotemporal incoherence, which enables Fourier-domain sampling and image formation. We present a method for characterizing the imaging efficacy of communications signals in an environment based on new spatiotemporal coherence metrics. We discuss simple solutions to problems of increasing spatial coherence in cases where transmit resources are limited. Finally, we demonstrate a 38 GHz millimeter-wave imaging system capturing 5G communications signals from a set of independent transmitters, demonstrating the ability to image multiple objects in a scene.

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.

Developing novel low complexity models using received in-phase and quadrature-phase samples for interference detection and classification in Wireless Sensor Network and GPS edge devices

Despite Wireless Sensor Networks (WSNs) significantly developing over the past decade, these networks, like most wireless networks, remain susceptible to malicious interference and spectrum coexistence. Other vulnerabilities arise as WSN applications adopt open standards and typically resource and energy-constrained commercial-off-the-shelf equipment. Deployments include safety-critical applications such as the internet of things, medical, aerospace and space and deep-sea exploration. To manage safety and privacy requirements across such a diverse wireless landscape, security on wireless edge devices needs improvement while maintaining low complexity. This paper improves wireless edge device security by developing a novel intelligent interference diagnostic framework. Received in-phase (I) and quadrature-phase (Q) samples are exclusively utilized to detect modern, subtle and traditional crude jamming attacks. This I/Q sample utilization inherently enables decentralized decision-making, where the low-order features were extracted in a previous study focused on classifying typical 2.4–2.5 GHz wireless signals. The associated optimal intelligent models are leveraged as the foundation for this paper’s work. Initially, Matlab Monte Carlo simulations investigate the ideal case, which incorporates no hardware limitations, identifies the required data type of signal interactions and motivates a hardware investigation. Software-defined radios (SDRs) collect the required live over-the-air I/Q data and transmit matched signal (ZigBee) and continuous-wave interference in developed ZigBee wireless testbeds. Low complexity supervised machine learning models are developed based exclusively on the low-order features and achieve an average accuracy among the developed models above 98%. The designed methodology involves examining ZigBee over-the-air data for artificial jamming and SDR jamming of ZigBee signals transmitted from SDR and commercial (XBee) sources. This approach expands to a legitimate node classification technique and an overall algorithm for wireless edge device interference diagnostic tools. The investigation includes developing Support Vector Machine, XGBoost and Deep Neural Network (DNN) models, where XGBoost is optimal. Adapting the optimized models to global positioning system signals establishes the transferability of the designed methodology. Implementing the designed approaches on a Raspberry Pi embedded device examines a relatively resource-constrained deployment. The primary contribution is the real experimentally validated interference diagnostic framework that enables independent device operation, as no channel assumptions, network-level information or spectral images are required. Developed models exclusively use I/Q data low-order features and achieve high accuracy and generalization to unseen data.

Distributed Adaptive Cooperative Bandwidth Spectrum Sensing for Cognitive MIMO Radios

To allow secondary and primary concurrent transmissions achieving optimal spectrum utilization in cognitive radio networks (CRNs), this paper proposes an innovative distributed cooperative spectrum sensing methodology using adaptive sensing bandwidth entitled distributed adaptive cooperative bandwidth spectrum sensing (DACB-SS) scheme when multiple secondary users (SUs) use part of the bandwidth to collaboratively perform spectrum sensing throughout the whole frame to detect the primary user (PU)'s reappearance in time. The SU’s spectrum efficiency is maximized by jointly optimizing the sensing bandwidth proportion, the number of cooperative SUs, and the detection probability, subject to the constraints on the SUs’ interference and the false alarm probability. A gripping slant of DACB-SS regime is provided to maintain overall maximum spectral efficiency. Another contribution of DACB-SS algorithm is the benefit to make great improvement on the sum capacity to obtain better performance of spectrum coexistence. Simulation results illustrate that the proposed analytical model investigates characteristic changes of the spectrum usage affected by the outage probability of PR and the coexisting secondary transmission bandwidth in terms of overall spectral efficiency as well as the outage capacity which can be characterized by the outage probability of SR and the interference from ST to PR in multiuser multiple-input multiple-output broadcast channel (MU-MIMO-BC). Besides, the sum capacity is impacted by channel gains, primary transmit power and secondary transmit power in Gaussian cognitive inference channel (G-CIFC).

New Location Algorithm with Primary User Based on Scatter Information in Cognitive Radio

In cognitive radio network, if cognitive user conflicts with primary user during the spectrum sensing, it should concede avoiding the effect of communication with primary user. The paper initiates a new sensing methodology using scatter information around mobile users to locate primary user. The multi-group TOA (Time of Arrival) and AOA (Angle of Arrival) provided by two base stations are measured based on the single reflect statistics channel model under NLOS (Non-line-of-sight) environment. An innovative location algorithm, proposed in this paper, which combines neural network with the weighted centroid algorithm to achieve primary user’s location. The proposed algorithm can make great improvement on the location accuracy by effectively mitigating NLOS error in cognitive radio networks. Another contribution of the proposed algorithm is the benefit to the cognitive performance, which can be characterized by the probability of spectrum coexistence. The results under different simulation circumstances illustrate that the current location algorithm improves detection performance and location performance to obtain more exact probability of spectrum coexistence.

Communications Standards for Unmanned Aircraft Systems: The 3GPP Perspective and Research Drivers

An unmanned aircraft system (UAS) consists of an unmanned aerial vehicle (UAV) and its controller, which use radios to communicate. While the remote controller (RC) is traditionally operated by a person who is maintaining visual line of sight with the UAV it controls, the trend is moving toward long-range control and autonomous operation. To enable this, reliable and widely available wireless connectivity is needed because it is the only way to remotely control a UAV or take control of an autonomous UAV flight. This article surveys the ongoing Third Generation Partnership Project standardization activities for enabling networked UASs. In particular, we present the requirements, envisaged architecture, and services to be offered to/by UAVs and RCs, which will communicate with one another, with the UAS traffic management (UTM), and with other users through cellular networks. Critical research directions relate to security and spectrum coexistence, among others. We identify major R&D platforms that will drive the standardization of cellular communications networks and applications.

Multiple Power Allocation Game Schemes for Spectrum Coexistence Model Between Multistatic MIMO Radar Sensors and MU Communication.

The normal operations of radar systems and communication systems under the condition of spectrum coexistence are facing a huge challenge. This paper uses game theory to study power allocation problems between multistatic multiple-input multiple-output (MIMO) radars and downlink communication. In the case of spectrum coexistence, radars, base station (BS) and multi-user (MU) have the working state of receiving and transmitting signals, which can cause unnecessary interferences to different systems. Therefore, when they work together, they should try to suppress mutual interferences. Firstly, the signal from BS is considered as interference when radar detects and tracks targets. A supermodular power allocation game (PAG) model is established and the existence and uniqueness of the Nash equilibrium (NE) in this game are proved. In addition, the power allocation problem from BS to MU is also analyzed, and two Stackelberg PAG models are constructed. It is proved that the NE of each game exists and is unique. Simultaneously, two Stackelberg power allocation iterative algorithms converge to the NEs. Finally, numerical results verify the convergence of the proposed PAG algorithms.

Radio resource management for large constellations in a spectrum sharing environment

SummaryIn the last five years, the satellite community has witnessed growing interest in the delivery of Broadband Services from non‐geostationary orbit (NGSO) systems. However, accessing the entire spectrum available without having to share it among the different operators is key for these systems to have a chance of breaking even, which makes for a challenging business case. This paper focuses on the challenges of spectrum coexistence of multiple satellite constellations for broadband communications and proposes a radio resource management algorithm to enable such coexistence.

Erratum: Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication [IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E103.A (2020) , No. 1 pp.297-302

Erratum: Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication [IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E103.A (2020) , No. 1 pp.297-302

Spectrally Compatible Waveform Design for MIMO Radar With ISL and PAPR Constraints

This paper deals with the waveform design to ensure the spectrum co-existence between multiple-input multiple-output (MIMO) radar and communication systems. The transmit waveform energy of MIMO radar in the prescribed spatial-frequency domain is minimized to reduce the mutual interference induced by the overlaid communication services. Additionally, the integrated sidelobes level (ISL) and peak-to-average power ratio (PAPR) constraints are forced on the probing waveform. To tackle the resulting NP-hard problem, we first introduce several auxiliary variables to convert the fourth-order ISL constraint and the PAPR constraint into several quadratic constraints. Then, the recast problem is formulated into a new alternating direction method of multipliers (ADMM) form, with small-sized tractable subproblems solved sufficiently. Numerical simulations verify that the proposed algorithm outperforms another competitive method in terms of the convergence performance and also demonstrate that the designed waveform can achieve spatial-frequency nulling under the ISL and PAPR constraints.

Interference and Intrusion in Wireless Sensor Networks

Wireless sensor network (WSN) systems for safety-critical, space and Internet of Things applications have recently begun to adopt open standards and commercial-off-the-shelf equipment, and persistently face challenges of malicious intrusion and spectrum coexistence. These threats are explored through Monte-Carlo simulation and benchtop testing, including matched protocol interference and sophisticated, interactive intrusion attacks. The need for expanding intrusion detection via a more holistic approach, whilst simultaneously improving WSN security, is illustrated. Discussions on WSN security, vulnerabilities, and attacks are also provided.