Dynamic Spectrum Access Systems Research Articles

Distribution of Heterogeneous Traffic Resources for 5G Cognitive Radio Networks based on Cooperative Learning and QOE

In addition to offering faster speeds and lower latency, 5G networks provide enhanced availability, significantly higher capacity, improved stability, and superior connectivity. The Mean Opinion Score (MOS), which quantifies the subjective quality of the user experience, has emerged as a widely accepted metric for assessing the quality of various types of network traffic. As we move towards the 5G era, measuring end-user quality has become increasingly important in the evolution of wireless communications. This paper presents a resource allocation approach for 5G cognitive radio networks that leverages cooperative learning and prioritizes Quality of Experience (QoE) for integrated heterogeneous traffic. The solution is based on a distributed underlay Dynamic Spectrum Access (DSA) system that utilizes MOS as a foundational metric for managing resource allocation across real-time video and data traffic, each with distinct characteristics. The proposed technique is designed to meet strict interference limitations for primary users while simultaneously maximizing the overall MOS. This is accomplished by employing reinforcement learning in a system where primary users and secondary users share the same frequency band of interest, ensuring efficient coexistence. Importantly, MOS serves as a standardized measure that allows for training across nodes carrying various types of traffic without compromising performance.

Enhanced Signal Area Estimation in Radio-Communication Spectrograms Based on Morphological Image Processing

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

Federated Learning-Based Spectrum Occupancy Detection.

Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection's effectiveness. Given the recent trend of using federated learning, we present a federated learning algorithm for distributed spectrum occupancy detection. This idea improves overall spectrum-detection effectiveness, simultaneously keeping a low amount of data that needs to be exchanged between sensors. The proposed solution achieves a higher accuracy score than separate and autonomous models used without federated learning. Additionally, the proposed solution shows some sort of resistance to faulty sensors encountered in the system. The results of the work presented in the article are based on actual signal samples collected in the laboratory. The proposed algorithm is effective (in terms of spectrum occupancy detection and amount of exchanged data), especially in the context of a set of sensors in which there are faulty sensors.

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.

Proof of Sense: A Novel Consensus Mechanism for Spectrum Misuse Detection

Optimal use of scarce radio spectrum is essential in the proliferation of beyond 5G networks, and promising blockchain technology offers various benefits for the spectrum management. However, existing blockchain-based solutions are expensive, nonoptimized, and lack spectrum fraud detection. This article proposes a novel consensus mechanism for a blockchain-based dynamic spectrum access (DSA) system. The proposed “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Proof-of-Sense</i> ” consensus mechanism operates based on spectrum sensing procedures rather than cryptographic calculations. It is specially designed to address fraudulent/unauthorized access to the spectrum by analyzing the sensed spectrum data. The core of the consensus mechanism is a cryptographic key sharing mechanism inspired by Shamir’s secret sharing scheme. Moreover, the proposed DSA system can enable different microservices, such as automated spectrum auctions, payment and penalty handling, and spectrum fraud detection. A proof of concept based on experimental approaches coupled with Matlab simulations is presented to analyze the performance of the proposed consensus mechanism.

PORÓWNANIE METOD DETEKCJI ZAJĘTOŚCI WIDMA RADIOWEGO Z WYKORZYSTANIEM UCZENIA FEDERACYJNEGO Z ORAZ BEZ WĘZŁA CENTRALNEGO

Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms supported by machine learning or artificial intelligence are often and successfully used. To protect the privacy of user data and to reduce the amount of control data, an interesting approach is to use federated machine learning. This paper compares two approaches to system design using federated machine learning: with and without a central node.

From Spectrum Bonding to Contiguous-Resource Batching Task Scheduling

We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource; some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings.

Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach.

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.

Optimization of Citizen Broadband Radio Service Frequency Allocation for Dynamic Spectrum Access System

With the increase in mobile broadband utilization, more spectrum release is recommended by the Federal Communications Commission for spectrum sharing under a three-tire system called Citizens Broadband Radio Service. The standardization, functional and operational necessities of this framework are defined by the Wireless Innovation Forum. If an unavoidable shipborne radar appears on the channel, the channel must be vacated by the lower tier users. The timing constraints on CBRS is also stringent. Wireless stations transmit short beacon frames termed as heartbeat signals. These signals consist of the wireless channel encryption data, Service Set Identifier (SSID) and other credential data. These signals also transmit commands to vacate a channel. The heartbeat interval, timing constraint and domain proxy features are analyzed in this paper. CBSD renunciation and spectrum acquisition is performed with the help of domain proxy based communication. The CBRS-SAS channel allocation algorithm is further investigated. The communication interoperability and network robustness can improved with the introduction of secondary SAS and secondary domain proxy respectively.

Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems

In a dynamic spectrum allocation (DSA) system, reliable prediction of spectrum occupancy based on a spectrum consumption model (SCM) is critical for system design, performance analysis, and evaluation. In this article, we focus on a low-level abstracted measured dataset from a massive campaign and investigate the occupancy of representative frequency bands. First, we apply an autoregressive-moving-average (ARMA) model combined with a low-pass filter, given the stationarity of the channel measurement dataset and thanks to the computational simplicity of the model. The average received power and off-state probability are extracted from the measured data. According to the results, the measured and predicted data are in good agreement. Comparing the proposed model-based ARMA with the popular long short-term memory learning algorithm, they have similar error accuracy with pre-processed data, while ARMA has a much lower training complexity. In the second step, we develop an SCM describing the spectrum usage for designing and examining the DSA system. We extract the periodic, aperiodic low-frequency, and burst components of the time series. Also, a binary sequence is extracted from a sparse occupancy channel, and modelled by a non-homogeneous Markov chain. Results show that the model-generated data can maintain the same statistics as the measured data.

A Modified FFT Algorithm for OFDM Based Wireless System

The constrained open range and the wastefulness in the range use in a fixed range task strategy, requests another correspondence model to abuse the present remote range unquestionably. This new structures connection point of view is accumulated as bleeding edge sifts through moreover as Dynamic Spectrum Access (DSA) and shrewd radio systems. The FFT and its turn IFFT are fundamental estimations in signal overseeing, programming portrayed radio, and the most encouraging change framework for example Symmetrical OFDM. From the normal organization of OFDM we can locate to FFT/ IFFT modules expect the essential work for any OFDM based handset. So when zero respected wellsprings of data/yields overpower nonzero inputs/yields, by then wide IFFT/FFT estimation for OFDM is never again supportive in term of implementation moment It is conceivable to decrease the implementation moment with "pruning" the FFT. In this we have finished a novel and significant data zero searched for after FFT pruning (IZTFFTP) estimation subject to DIF radix-2 framework. Separate from different estimations, the yielded outcome of IZFFTP show that it is free of condition of the zero respected data what's everything the all the more keeping up a reasonable exchange off among reality bizarreness of any structure by not just diminishing the extent of complex extension correspondingly a comparative number of sided advancements what's more. The proposed join is executed in essential level PC program for example in C++and this takes after the Cooley-Tukey radix-2 FFT estimation, holding all the key highlights, for example, straightforwardness and consistency, by making some assortment and programming adjustment

SpecGuard: Spectrum Misuse Detection in Dynamic Spectrum Access Systems

Dynamic spectrum access (DSA) is the key to solving worldwide spectrum shortage. The open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate users. Secondary-user authentication is thus critical to ensure the proper operations of DSA systems. This paper presents SpecGuard, the first crowdsourced spectrum misuse detection framework for DSA systems. In SpecGuard, a transmitter is required to embed a spectrum permit into its physical-layer signals, which can be decoded and verified by ubiquitous mobile users. We propose three novel schemes for embedding and detecting a spectrum permit at the physical layer. The first scheme relies on a higher transmission power to embed the spectrum permit. To alleviate the assumptions on the additional transmission power, the second scheme is proposed with a limited negative impact on the normal data transmission. The third scheme takes a different approach by adopting a novel constellation design and exploiting the trust between the transmitter and the receiver. Crowdsourced spectrum misuse detection eliminates the need for the deployment of dedicated sensors and thus greatly reduces the deployment and maintenance cost. Detailed theoretical analyses, MATLAB simulations, and USRP experiments confirm that our schemes can achieve correct, low-intrusive, and fast spectrum misuse detection.

Trading Utility for Privacy in Shared Spectrum Access Systems

In an effort to meet growing demands on the radio frequency spectrum, regulators are exploring methods to enable band sharing among a diverse set of user devices. Proposed spectrum access systems would dynamically assign spectrum resources to users, maintaining databases of spectrum use information. While these systems are anticipated to increase the efficiency of spectrum sharing, incumbent users have raised concerns about exposing details of their operations and have questioned whether their privacy can be protected. In this paper, we explore whether primary users can retain a critical level of privacy in a spectrum access system setting, where they must reveal some information to enable dynamic access to the spectrum by other users. Under a variety of operational scenarios and user models, we examine adversary techniques to exploit the spectrum access system and obfuscation strategies to protect user privacy. We develop analytical methods to quantify the resulting privacy and validate our results through simulation. To the best of our knowledge, this is the first paper that considers inference attacks on primary users in the setting of a highly dynamic spectrum access system. Privacy analysis of this kind will help to enable the adoption of shared spectrum access systems by allowing incumbent users to quantify and mitigate risks to their privacy.

DYWAMIT: Asynchronous Wideband Dynamic Spectrum Sensing and Access System

Dynamic spectrum access (DSA) is a promising solution for enhancing the utilization efficiency of spectrum resources. In DSA, spectrum sensing is crucial and cooperative spectrum sensing has been confirmed as a promising technology that can combat the wireless channel fading problem. Most current works have assumed that the secondary users (SUs) are synchronous with the primary users' (PUs) network. However, on one hand, the SUs may have no information about the PUs' communication protocols. On the other hand, communications among the PUs are not based on synchronous operation in some systems. In order to address such problems, the DYWAMIT, an asynchronous wideband DSA system, is proposed. In this scheme, the SUs need not maintain synchronism with the PUs, enabling the SUs to cognitively and asynchronously access primary channels even without knowing the communication protocols of the PUs. Performance analysis and simulations are conducted to evaluate the proposed DYWAMIT algorithm; in particular, the average throughput of the SUs, accounting for the asynchronism between the SUs and PUs, is derived for four cases of interest. Some important future research challenges are highlighted.

Cognitive channel selection and scheduling for multi-channel dynamic spectrum access networks considering QoS levels

Dynamic spectrum access (DSA) networks are composed of unprivileged users, called secondary users (SUs), that utilize the spectrum opportunities produced by the absence of co-located privileged primary users (PUs) via their cognitive capabilities. Providing a certain level of quality of service (QoS) to these users is a very challenging problem whilst providing protection to licensed PUs and contenting with nearby SUs. Even though centralized solutions may lead to better solutions in terms of network efficiency, the dynamic nature of DSA networks make the distributed solution approaches more attractive. In this paper, we present a fast, distributed, PU temporal-activity-estimation-aided spectrum assignment scheme for a multi-channel DSA system, including several multi-interface capable SUs with traffic demands at differentiated QoS levels. We first developed the proposed cognitive channel selection method, considering a simplified network having single channel and investigated its performance. Second, considering multi-channel environment, along with contending multi-SUs each of which can utilize multiple channels using their multi-interface property, we adopted the designed algorithm and coupled the proposed spectrum selection scheme with a distributed spectrum sharing mechanism that we devised to increase the overall network utility further. We modeled our scheme along with the network model using MATLAB® and evaluated its performance via several simulations. The extensive simulations validate the effectiveness of the proposed channel assignment scheme in terms of the Figure of Merit we defined, composed of the weighted sum of the throughput ratio values for packets of different QoS levels that successfully reached at the destination. The results also show the superior performance of the proposed scheme with respect to a native scheme that utilizes every spectrum opportunity in favor of the most demanding traffic flow.

Distributed Learning-Based Cross-Layer Technique for Energy-Efficient Multicarrier Dynamic Spectrum Access With Adaptive Power Allocation

This paper proposes energy and cross-layer aware resource allocation techniques that allow dynamic spectrum access (DSA) users, by means of learning algorithms, to locate and exploit unused spectrum opportunities effectively. Specifically, we design private objective functions for DSA users with multiple channel access and adaptive power allocation capabilities. We also propose simple two-phase heuristics for allocating spectrum and power resources among users. The proposed heuristics split the spectrum and power allocation problem into two subproblems, and solve each of them separately. The spectrum allocation problem is solved, during the first phase using learning. Two procedures to learn the channel selection are proposed and compared in terms of optimality, scalability, and robustness. The power allocation, on the other hand, is formulated as a real optimization problem and solved, during the second phase, by traditional optimization solvers. Simulation results show that energy and cross-layer awareness and multiple channel access capability improve the performance of the system in terms of the per-user average rewards received from accessing the dynamic spectrum access system. In addition, the two proposed methods for channel selection via learning represent a tradeoff between optimality, scalability, and robustness.

&lt;formula&gt; &lt;tex&gt;$P^{2}$&lt;/tex&gt; &lt;/formula&gt;-SAS: Privacy-Preserving Centralized Dynamic Spectrum Access System

Centralized spectrum management is one of the key dynamic spectrum access (DSA) mechanisms proposed to govern the spectrum sharing between government incumbent users (IUs) and commercial secondary users (SUs). In the current centralized DSA designs, the operation data of both government IUs and commercial SUs need to be shared with a central server. However, the operation data of government IUs are often classified information and the SU operation data may also be commercial secrets. The current system design dissatisfies the privacy requirement of both IUs and SUs, since the central server is not necessarily trustworthy for holding such sensitive operation data. To address the privacy issue, this paper presents a privacy-preserving centralized DSA system ( $P^{2}$ -SAS), which realizes the complex spectrum allocation process of DSA through efficient secure multi-party computation. In $P^{2}$ -SAS, none of the IU or SU operation data would be exposed to any snooping party, including the central server itself. We formally prove the correctness and privacy-preserving property of $P^{2}$ -SAS and evaluate its scalability and practicality using experiments based on real-world data. Experiment results show that $P^{2}$ -SAS can respond an SU’s spectrum request in 6.96 s with communication overhead of less than 4 MB.

On the Near-Optimality of Training-Based GLRT Spectrum Sensing

Spectrum sensing is of utmost importance in cognitive radio and dynamic spectrum access systems for achieving spectrum awareness. To provide reliable spectrum awareness, it is critical to develop (near-) optimal sensing techniques and understand achievable performance limits. In this paper, we study likelihood ratio test (LRT)-based methods for detection of a signal of interest (SOI) assuming multiple receive antennas in the presence of spatially correlated additive noise (colored noise). We show that with on/off status information of the SOI available at receiver, a training-based generalized likelihood ratio test (TB-GLRT) method can be designed which approximates the optimal LRT estimator-correlator (EC) detector. Using the inverse Laplace transform and complex Wishart distribution theory, we derive formulas for detection and false alarm probabilities of both LRT-EC and GLRT schemes. The convergence of the TB-GLRT to the LRT-EC is analyzed and proved based on convergence behaviors of the detectors and their decision statistics. Simulation results verify the analytical convergence properties. In addition, the results illustrate the effects of different system design parameters, and demonstrate that the TB-GLRT scheme can provide substantial performance improvement over several existing GLRT methods.

Subcarrier Ordering for OFDM Packet Radio Systems

Subcarrier ordering is a ranking of the subchannels of a multicarrier communication system according to a quality criterion. We describe and evaluate two techniques for subcarrier ordering in half-duplex packet radio systems, including OFDM dynamic spectrum access systems. The ordering techniques require no estimates of subchannel gains or subcarrier signal strengths. The performance of each technique is evaluated for channels with fading and for channels with interference and fading.

Multicast Rendezvous in Fast-Varying DSA Networks

Establishing communications between devices in a dynamic spectrum access (DSA) system requires the communicating parties to “rendezvous” before transmitting data packets. Frequency hopping (FH) is an effective rendezvous method that does not rely on a predetermined control channel. Previous FH-based rendezvous designs mainly target unicast rendezvous, and do not intrinsically support multicast rendezvous , where a group of nodes need to rendezvous simultaneously. Furthermore, these designs do not account for fast primary user (PU) dynamics, leading to long time-to-rendezvous (TTR). In this paper, we exploit the uniform $k$ -arbiter and Chinese Remainder Theorem quorum systems to develop three FH-based multicast rendezvous algorithms, which provide different tradeoffs between rendezvous efficiency (e.g., low TTR) and security (e.g., robustness to node compromise). Our rendezvous algorithms are tailored for asynchronous and spectrum-heterogeneous DSA systems. To account for fast PU dynamics, we develop an algorithm for adapting the proposed FH designs on the fly. This adaptation is done through efficient mechanisms for channel ordering and quorum selection. Our simulations validate the effectiveness of the proposed rendezvous algorithms, their PU detection accuracy, and their robustness to insider attacks.