Incumbent Users Research Articles

Unlicensed Spectrum Access and Performance Analysis for NR-U/WiGig Coexistence in UAV Communication Systems

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%.

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.

Intelligent Access to Unlicensed Spectrum: A Mean Field Based Deep Reinforcement Learning Approach

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

Distributed and Secure Uplink Power Control in Dynamic Spectrum Access

In dynamic spectrum access (DSA), secondary users (SU) should only be allowed to access a licensed band belonging to incumbent users (IU) when the quality-of-service (QoS) requirements of both IUs and SUs can be satisfied at the same time. However, IU’s location and its received interference strength are considered sensitive in many DSA systems which should not be revealed, making it very challenging to optimize the network utility subjected to satisfying the operation and security requirements of SUs and IUs. In this paper, we develop a secure and distributed SU transmit power control algorithm to solve this challenge. Our algorithm achieves optimal SU power control to maximize the sum of SU rates. The SINR-guaranteed coexistence between SUs and IUs are enabled to maintain effective communication, while no information is directly required from IUs. Local measurements of IU signals provided by Environmental sensing capability (ESC) also undergo a security masking process to ensure that IU location cannot be derived from its outputs. Convergence and stability properties of our algorithm and its privacy-protection strength are both theoretically analyzed and experimentally evaluated through simulations.

Decentralized Spectrum Access System: Vision, Challenges, and a Blockchain Solution

Spectrum access system (SAS) is widely considered the de facto solution to coordinating dynamic spectrum sharing (DSS) and protecting incumbent users. The current SAS paradigm prescribed by the FCC for the CBRS band and standardized by the WInnForum follows a centralized service model in that a spectrum user subscribes to a SAS server for spectrum allocation service. This model, however, neither tolerates SAS server failures (crash or Byzantine) nor resists dishonest SAS administrators, leading to serious concerns about SAS system reliability and trustworthiness. This is especially concerning for the evolving DSS land-scape where an increasing number of SAS service providers and heterogeneous user requirements are coming up. To address these challenges, we propose a novel blockchain-based decentralized SAS architecture called BD-SAS that provides SAS services securely and efficiently, without relying on the trust of each individual SAS server for the overall system trustworthiness. In BD-SAS, a global blockchain (G-Chain) is used for spectrum regulatory compliance while smart contract-enabled local blockchains (L-Chains) are instantiated in individual spectrum zones for automating spectrum access assignment per user request. We hope our vision of a decentralized SAS, the BD-SAS architecture, and discussion on future challenges can open up a new direction toward reliable spectrum management in a decentralized manner.

Efficient Wideband Spectrum Sensing Using Mems Acoustic Resonators

The ever-increasing demand for wireless applications has resulted in an unprecedented radio frequency (RF) spectrum shortage. Ironically, at the same time, actual utilization of the spectrum is sparse in practice [1]. To exploit previously underutilized frequency bands to accommodate new unlicensed applications and achieve highly efficient usage of the spectrum, the Federal Communications Committee (FCC) has repurposed many frequency bands for dynamic spectrum sharing. This includes the 6 GHz band to be shared between Wi-Fi 6 and the incumbent users [2] as well as the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [3].

Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing.

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we propose the implementation of intelligent dynamic real-time spectrum resource management through the application of data mining and case-based reasoning, which reduces the complexity of existing intelligent dynamic spectrum resource management and enables efficient real-time resource allocation. In this case, data mining and case-based reasoning analyze the activity patterns of incumbent users using vast amounts of sensing data from industrial IoT and enable rapid resource allocation, making use of case DB classified by case. In this study, we confirmed a number of optimization engine operations and spectrum resource management capabilities (spectrum handoff, handoff latency, energy consumption, and link maintenance) to prove the effectiveness of the proposed intelligent dynamic real-time spectrum resource management. These indicators prove that it is possible to minimize the complexity of existing intelligent dynamic spectrum resource management and maintain efficient real-time resource allocation and reliable communication; also, the above findings confirm that our method can achieve a superior performance to that of existing spectrum resource management techniques.

Detecting Byzantine attacks in Cognitive Radio Networks : A two-layered approach using Hidden Markov Model and machine learning

Cognitive Radio Networks (CRN) are gaining popularity due to their ability to harness the TV whitespace. CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU). Such a collaborative sensing mechanism enables an increased detection accuracy of the incumbent’s presence (or absence) as all the SUs send their local sensing reports to a Fusion Centre (FC), where these sensing reports are aggregated to arrive at a final sensing decision. However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.k.a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. The first layer, Processing layer, uses Hidden Markov Model (HMM) to get a probabilistic relationship between the PU’s states and the SUs’ sensing reports. This generates the required dataset for the next layer. The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters. Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users.

Preserving Honest/Dishonest Users’ Operational Privacy with Blind Interference Calculation in Spectrum Sharing System

Spectrum sharing has been gaining its popular adoption as a potential solution to improve spectrum utilization in future wireless systems. Both Federal Communications Commission (FCC) and European Telecommunications Standards Institute (ETSI) support dynamic spectrum access (DSA) as an enabling technology for spectrum sharing. To effectively realize DSA in practice, users (from both defense and commercial sectors) are required to share their (radio) operational information, which risks exposing their security, privacy, and business plan to unintended agents. Protecting users' operating information is hence the key to DSA's success. In this paper, taking the FCC's spectrum access system (SAS) as a study case, we investigate the operational privacy issue of Incumbent Users (IUs) and honest/dishonest Secondary Users (SUs). For the case of IUs and honest SUs, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we introduce an interference calculation scheme that allows users to calculate an interference budget without revealing operational information (e.g., antenna height, transmit power, location...), referred to as the blind interference calculation scheme (BICS). BICS also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. To further save the overhead in calculating the interference map, we introduce a quantization method and optimize the grid sizes of the terrestrial area of interest. Additionally, for the case of IUs and dishonest SUs, we propose a “punishment and forgiveness” (PF) mechanism, which draws support from SUs' reputation scores (RSs) and reputation histories (RHs), to encourage SUs to provide truthful information. Theoretical analysis and extensive simulations show that our proposed PSEO and PF-PSEO schemes can better protect all users' operational privacy under various privacy attacks, yielding higher spectrum utilization with less online overhead, compared with state of the art approaches.

The Value of Sharing Intermittent Spectrum

We examine a model of Cournot competition with congestion motivated by recent policy to allow commercial sharing of wireless spectrum that is assigned to other users such as government agencies. A key feature of such spectrum is that it is intermittently available because of the incumbent user’s activity. In our model, wireless service providers (SPs) compete for a common pool of customers using their own proprietary (exclusively licensed) bands of spectrum along with access to an additional intermittent band. When the intermittent band is unavailable, any traffic carried on that band must be shifted to the proprietary bands. Customers are sensitive to both the price they pay and the average congestion they experience across the bands of spectrum from which they receive service. We compare two different access policies for this intermittent band: one in which it is open to all SPs and one in which it is licensed to a single SP. We also allow the band to be divided into multiple subbands where each subband is either open or licensed. We characterize trade-offs between social welfare and consumer welfare that depend on the choice of different access policies and assignments of subbands to SPs. These can involve subtle issues related to the ability of a larger SP to make more efficient use of intermittent spectrum and the increase in competition by assigning more spectrum to smaller SPs. This paper was accepted by David Simchi-Levi, operations management.

Green Traffic Off-Loading Over Uncertain Shared Spectrums With End-to-End QoS Guarantee

The fast proliferation of Internet of Things (IoT) exacerbates the current spectrum shortage. As a promising solution, licensed spectrum sharing has received great attentions recently. However, due to the unpredictable activities of incumbent users, the availability of the shared spectrum is uncertain, making the quality of service (QoS) provisioning based on it a difficult task, especially under multi-hop scenarios. In this paper, considering the energy-efficiency issue, we propose an end-to-end (E2E) QoS guaranteed green traffic off-loading scheme over the uncertain shared spectrums in multi-hop networks. Specifically, we formulate it into a two-step problem, an off-loaded traffic maximization (OTM) problem and an energy-efficiency improvement (EEI) problem. The former one is to determine the admitted sessions. To achieve the E2E QoS guarantee under the uncertain spectrum supply, we develop a concept of equivalent link capacity, and construct a risk-averse joint chance constraint. Since the formulated problem is intractable, we first develop a data-driven conservative transformation to make the joint chance constraint a set of deterministic individual ones. Then, we design a convex-hull-based and a substitution-based linearization approach to make all the constraints tractable. For the second step, the EEI problem with the same constraints as the OTM problem is designed to refine the resource scheduling for the admitted sessions to optimize the energy-efficiency. To deal with the fractional-form energy-efficiency objective, a Dinkelbach method based iterative algorithm is further developed. Numerical results have shown the effectiveness of the proposed scheme.

Tiered Spectrum Measurement Markets for Joint Licensed and Unlicensed Secondary Access

We consider a three-tier spectrum sharing framework, recently adopted in the United States for the Citizens Broadband Radio Service, which enables commercial users to share this spectrum with the incumbent users. This sharing can be further assisted by environmental sensing capability operators (ESCs) that monitor the spectrum occupancy to determine when the incumbents are absent. Two key aspects of this framework that impact how spectrum access firms (SAs) using this spectrum may compete are the differences in information provided by different ESCs and how different firms bundle their services using the licensed and unlicensed band. We show that if the SAs tend to use the unlicensed band more, they will prefer to obtain information from different ESCs. On the other hand, if one of the SAs uses the licensed band more, both the SAs tend to obtain information from the same ESC. If both the SAs tend to differ more in accessing the unlicensed bandwidth, they tend to obtain information from the same ESC more. Our analysis reveals that regulation is required if there is a single ESC, as the unlicensed spectrum may be unused if the user's valuation is high.

Towards Fair and Efficient Spectrum Sharing Between LTE and WiFi in Unlicensed Bands: Fairness-Constrained Throughput Maximization

The deployment of Long Term Evolution (LTE) networks in unlicensed spectrum is a promising solution to overcome the scarcity of licensed spectrum. Yet it has been widely observed that severe unfairness and performance degradation would occur when LTE coexists with WiFi, the incumbent user of unlicensed bands, without proper adjustment. Fair and efficient coexistence of these two networks thus becomes crucial. It, nevertheless, remains largely unknown how to optimize the total throughput of the LTE and WiFi networks under fairness constraints. To address the above open issue, this paper considers that a WiFi network coexists with an LTE network using the Category 3 or Category 4 Listen-Before-Talk (LBT) mechanism, and aims to characterize the maximum total throughput of the LTE and WiFi networks under two fairness constraints including throughput fairness and 3GPP fairness. The analysis shows that the maximum total throughput is independent of which LBT mechanism the LTE network adopts, and can be improved as the mean successful transmission time of the LTE network increases. Explicit expressions of the optimal initial backoff window sizes to achieve the maximum total throughput under both throughput fairness and 3GPP fairness are also derived, which shed important light on the practical network design. It is found that the initial backoff window size of the LTE network should be enlarged as the mean successful transmission time of the LTE network increases, indicating that for fair and efficient coexistence with a WiFi network, the LTE network needs to access the unlicensed channel infrequently with large packets. To facilitate implementation in practice, distributed schemes are further proposed, with which WiFi and LTE can optimally adjust the backoff window sizes based on their own observation and estimation without the need of coordination between these two networks.

DPavatar: A Real-Time Location Protection Framework for Incumbent Users in Cognitive Radio Networks

Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both US and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time, and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU's location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms.

Feature Extraction in Local Spectrum Sensing for Next Generation Cognitive Radios – A Review

In the context of the currently developed networks for the realization of future communications, the concept of Cognitive Radio (CR) has a significant place. Its implementation in modern ultra-dense networks (UDN) requires the development of novel and improved adaptive solutions for the main functionalities of a CR system. This technology has a great potential for solving the significant spectrum underutilization issue which has been established as characteristic for the traditional communication networks and hence, the continual and substantive research efforts in the recent years. The principal challenge for CRs is the optimization of spectrum utilization without creating unwanted interference for the incumbent (primary) users. Thus, a significant portion of the research is directed towards the vital spectrum sensing functionality. This paper reviews the potential uses of CR in UDN as well as the main approaches for modeling of spectrum sensing with respect to signal detection. The review focuses on local spectrum sensing techniques and presents the strengths and weaknesses of their mathematical definition. Specific attention is given to the channel models which are considered in the literature and to what kinds of features are extracted from the received signal to achieve accurate detection.

Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities and Challenges

The ever-increasing demand for unlicensed spectrum has prompted regulators in the US and Europe to consider opening up the 6 GHz bands for unlicensed access. These bands will open up 1.2 GHz of additional spectrum for unlicensed radio access technologies (RATs), such as Wi-Fi and 5G New Radio Unlicensed (NR-U), in the US and if permitted, 500 MHz of additional spectrum in Europe. The abundance of spectrum in these bands creates new opportunities for the design of mechanisms and features that can support the emerging bandwidth-intensive and latency-sensitive applications. However, coexistence of unlicensed devices both with the bands' incumbent users and across different unlicensed RATs present significant challenges. In this paper, we provide a comprehensive survey of the existing literature on various issues surrounding the operations of unlicensed RATs in the 6 GHz bands. In particular, we discuss how key features in next-generation Wi-Fi are being designed to leverage these additional unlicensed bands. We also shed light on the foreseeable challenges that designers of unlicensed RATs might face in the near future. Our survey encompasses key research papers, contributions submitted to standardization bodies and regulatory agencies, and documents presented at various other venues. Finally, we highlight a few key research problems that are likely to arise due to unlicensed operations in the 6 GHz bands. Tackling these research challenges effectively will be critical in ensuring that the new unlicensed bands are efficiently utilized while guaranteeing the interference-free operation of the bands' incumbent users.

Feature Extraction in Local SpectrumSensing for Next Generation CognitiveRadios � A Review

In the context of the currently developed networks for the realization of future communications, the concept of Cognitive Radio (CR) has a significant place. Its implementation in modern ultra-dense networks (UDN) requires the development of novel and improved adaptive solutions for the main functionalities of a CR system. This technology has a great potential for solving the significant spectrum underutilization issue which has been established as characteristic for the traditional communication networks and hence, the continual and substantive research efforts in the recent years. The principal challenge for CRs is the optimization of spectrum utilization without creating unwanted interference for the incumbent (primary) users. Thus, a significant portion of the research is directed towards the vital spectrum sensing functionality. This paper reviews the potential uses of CR in UDN as well as the main approaches for modeling of spectrum sensing with respect to signal detection. The review focuses on local spectrum sensing techniques and presents the strengths and weaknesses of their mathematical definition. Specific attention is given to the channel models which are considered in the literature and to what kinds of features are extracted from the received signal to achieve accurate detection.

인컴번트 사용자 채널 점유확률 변화에 따른 SVM 기반의 무선 트래픽 이용환경 추론기법

In this paper, we propose a case-based reasoning Cognitive Radio(CR) engine that uses limited resources efficiently in military tactical wireless communication environments. A CR engine should be able to learn and infer, and thus predict the available channel information of a secondary user based on information about traffic usage. To be able to do so, a low probability of channel collision should be associated with the engine. The engine should, thereby, be able to indicate the probability of channel occupancy of an incumbent user who requires interference protection. We used a Support Vector Machine(SVM) to measure the histogram-type wireless traffic usage environment in accordance with the change in the probably of channel occupancy of the incumbent user. SVM is a sorting algorithm of machine learning. Next, we calculated the histogram

A Chaotic Q-learning-Based Licensed Assisted Access Scheme Over the Unlicensed Spectrum

To meet the ever-increasing demand for mobile data traffic, mobile operators are seeking to utilize unlicensed spectrum as a supplement to the licensed spectrum. The harmonious and spectrum-efficient coexistence scheme between LTE and incumbent users on the unlicensed spectrum is thus mandatory. Currently, advanced intelligent technologies are being expected to play the crucial role in the future communication system. We thus introduce the Q-learning (QL) framework into LTE licensed assisted access (LAA) scheme in the paper, thereby forming a QL based LAA scheme. We first redefine the fairness in the sharing of unlicensed spectrum and then divide the state space into six states based on the predefined throughput and fairness thresholds, followed by the definition of the action set and reward function. In the proposed scheme, based on the convergent Q table, where each element is used to evaluate the pros and cons of taking an action, the agent can repeatedly interact with the environment until it reaches the terminal state, i.e. selects the optimal action (i.e. contention window size). Additionally, the chaotic motion with ergodicity, regularity and randomness is first introduced into the action-decision strategy to accelerate the training velocity with the balance consideration of exploration and exploitation. The simulation results prove that the proposed $\epsilon$ -chaotic greedy selection strategy has faster convergence velocity compared with other methods such as $\epsilon$ -greedy, pure greedy, Bolzmann and random selection strategy, and that the proposed chaotic QL LAA scheme outperforms the other LAA schemes such as the 3GPP, linear, fixed LAA and Listen Before Talk (LBT) adaptive schemes in terms of throughput, collision probability, fairness and delay.

Trading Privacy for Utility in Database-Assisted Dynamic Spectrum Access

In dynamic spectrum access, commercially operated database servers are often used to assist opportunistic users (OUs) to query and access spectrum vacancies of incumbent users (IUs). This query and answer process introduces significant privacy concerns due to the potential leakage of sensitive operational details about IUs, especially their locations. Existing privacy-preserving mechanisms, such as spatial obfuscation, k-anonymity and cryptographic approaches, can be used to hide IU locations. They, however, cannot guarantee that the interference level caused by OU activity remains under required limits. Moreover, privacy mechanisms do not account for spectrum efficiency (i.e., the loss of OU transmission opportunities) in their designs. The complex three-way tradeoff between privacy, interference, and utility has not been systematically studied in the literature. In this paper, we tackle this challenge by formalizing the relationship between privacy and spectrum efficiency using the concept of a privacy zone (PZ) contained in an exclusion zone (EZ). Within the PZ, IU location is indistinguishable, whereas the EZ guarantees an interference limit for any IU located within the PZ. By fixing either the privacy or the interference requirement, we formulate and solve corresponding optimization problems that maximize utility under fixed and probabilistic OU location deployments, respectively. Our formulations can accommodate the privacy requirements of multiple IUs. Simulation results using real-world maps/parameters show that the IU’s privacy increases with decreasing OUs’ utility, given a fixed allowable interference for the IUs. Furthermore, we show that this paper outperforms the work of Bahrak et al .