Utilization Of Radio Spectrum Research Articles

Enhancing spectrum sensing efficiency in multi‐channel cognitive device‐to‐device networks: Medium Access Control layer strategies and analysis

AbstractThe detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs).

Smart sensing enabled dynamic spectrum management for cognitive radio networks

Cognitive Radio Networks (CRNs) have ushered in a transformative era in wireless communication, reshaping the landscape of radio spectrum utilization and management. At the core of CRNs lies the pivotal capability to sense the radio frequency spectrum dynamically and adapt transmission parameters to preemptively address interference and optimize spectrum utilization. This article addresses the escalating challenges associated with Quality of Service (QoS) management in CRNs, exacerbated by their dynamic nature, especially in scenarios characterized by high mobility. Concurrently, the article underscores the critical significance of energy efficiency, given its direct implications on network operational costs and sustainability. To effectively navigate the intricate interplay between QoS and energy management in CRNs, we propose a Smart Sensing Enabled Dynamic Spectrum Management scheme (SSDSM). Within the SSDSM framework, cognitive user energy undergoes intelligent sensing, while QoS is governed through dynamic spectrum management. The proposed scheme optimizes service response time by refining fuzzy-based controllers and curtails energy consumption through periodic sensing triggered by predefined rules. Operationalizing within a centralized paradigm, the entire network is overseen by a central controlling node, tasked with formulating an optimal channel list using the SSDSM scheme and allocating it to cognitive users. The efficacy of the proposed scheme is evaluated and validated through rigorous testing using MATLAB. Results reveal tangible enhancements in system efficiency, encompassing maximized throughput, reduced handoff ratio, and minimized service response delay. This research contributes to the ongoing discourse on advancing the performance metrics of cognitive radio networks in the pursuit of reliable and sustainable wireless communication services.

Experimental Assessment of Common Crucial Factors That Affect LoRaWAN Performance on Suburban and Rural Area Deployments.

LoRaWAN networks might be a technology that could facilitate extreme energy-efficient operation while offering great capacity for suburban and rural area deployment, but this can be a challenging task for a network administrator. Constraints that deform the trade-off triangle of coverage, scalability and energy efficiency need to be overcome. The scope of this study is to review the limitations of the LoRaWAN protocol in order to summarize and assess the crucial factors that affect communication performance, related to data rate allocation, bidirectional traffic and radio spectrum utilization. Based on the literature, these factors correspond mostly to configurable payload transmission parameters, including transmission interval, data rate allocation, requirement for acknowledgements and retransmission. In this work, with simulation experiments, we find that collision occurrences greatly affect channel occupancy. In particular, it was evaluated that collision occurrence is increasingly affected by transmission intervals, which have the most significant negative impact on packet delivery rate (PDR). We then validated that clustering of end nodes in the vicinity of a gateway, taking into account distance and transmission settings, can improve network scalability. This can assure distribution of the total transmission time to end nodes with respect to application-related QoS requirements. Following this clustering approach, we achieved a PDR greater than 0.90 in a simulation setting with 6000 end nodes in a 10 km coverage.

Optimized MAC Protocol Using Fuzzy-Based Framework for Cognitive Radio AdHoc Networks

Cognitive radio networks (CRNs) have been widely used in various applications for effective radio spectrum utilization in recent years. It is essential to fend off the growing demand for this finite natural resource for next-generation communications. In CRNs, detecting the activity of the primary user requires opportunistic spectrum sensing for efficient usage of the available radio spectrum, which is a limited and exquisite resource. Thus, CRNs are the key component in solving the spectrum scarcity issue in the presence of primary user bands through secondary users. Cognitive radio AdHoc networks (CRAHNs) are a unique kind of CRNs where infrastructures less cognitive radio (CR) nodes are furnished. In CRAHN, the CR-MAC protocol works slightly differently from the traditional wireless network MAC protocols. Particularly, the proposed method includes a high traffic scenario under contention-based IEEE 802.11 DCF MAC protocol. Accordingly, it can be observed that both, throughput and delay, increase as the CW size and packet length of the 802.11 (DCF) MAC protocol for CRAHN varies. Therefore, this paper proposed a fuzzy-based optimization framework for the 802.11 (DCF) MAC protocol in CRAHNs. Furthermore, it optimized throughput and delay by training a database of input parameters, contention window, and packet length for the Mamdani and Sugeno fuzzy inference system (FIS) models of the 802.11 (DCF) protocol simultaneously. The experimental result of the proposed framework for CRAHN with FIS shows that altering the contention window increases throughput by 25% and reduces the delay by 38% compared to the IEEE802.11 (DCF) protocol for CRAHNs without FIS. Moreover, it is also revealed that the throughput is increased by and 7% and the delay is reduced by 40% to 50% due to altering the packet length.

Reinforcement Learning-based Wi-Fi Contention Window Optimization

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the increase of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. In our proposed approach, we assess two different observation metrics, the average of the normalized level of the transmission queue of all associated stations and the probability of collisions. The overall network’s throughput is defined as the reward. The action is the contention window (CW) value that maximizes throughput while minimizing the number of collisions. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for both static and dynamic scenarios, regardless of the number of stations. Additionally, our results show that observations based on the average of the normalized level of the transmission queues have a slightly better performance than observations based on the collision probability. Moreover, the performance difference with BEB is amplified as the number of stations increases, with DQN and DDPG showing a 45.52% increase in throughput with 50 stations.

Joint Deep Reinforcement Learning and Unsupervised Learning for Channel Selection and Power Control in D2D Networks.

Device-to-device (D2D) technology enables direct communication between devices, which can effectively solve the problem of insufficient spectrum resources in 5G communication technology. Since the channels are shared among multiple D2D user pairs, it may lead to serious interference between D2D user pairs. In order to reduce interference, effectively increase network capacity, and improve wireless spectrum utilization, this paper proposed a distributed resource allocation algorithm with the joint of a deep Q network (DQN) and an unsupervised learning network. Firstly, a DQN algorithm was constructed to solve the channel allocation in the dynamic and unknown environment in a distributed manner. Then, a deep power control neural network with the unsupervised learning strategy was constructed to output an optimized channel power control scheme to maximize the spectrum transmit sum-rate through the corresponding constraint processing. As opposed to traditional centralized approaches that require the collection of instantaneous global network information, the algorithm proposed in this paper used each transmitter as a learning agent to make channel selection and power control through a small amount of state information collected locally. The simulation results showed that the proposed algorithm was more effective in increasing the convergence speed and maximizing the transmit sum-rate than other traditional centralized and distributed algorithms.

An efficient radio-frequency spectrum utilization technique for cognitive radio networks

Cognitive radio (CR) is a rising technology that unlocks the doors for radio spectrum scarcity problem, which is of great concern nowadays among the researchers at various levels. CR enables to unlicensed users or secondary users (SUs) to access the primary channels when licenced users or primary users (PUs) are not using these channels. Two fundamental access methods namely overlay and underlay are very popular in utilizing the free available channels or the white spaces. Various hybrid access methods have been proposed and recommended by many researchers to further enhance the radio spectrum utilization. Of course hybrid access methods are the better ways to deal this spectrum scarcity problem, but a comprehensive and directed effort is required to optimize the modality of these methods. This work proposes a Markov chain based hybrid access method named as Hybrid Spectrum Utilization Technique (HSUT), which tries to maximize the radio spectrum utilization by enhancing the PU’s detection probability. This work also analyzes the performance of the HSUT and results obtained through OMNeT++ simulator are very encouraging. At last, this work also compares the performance of the HSUT with the overlay, underlay, Hybrid-P1 (Dhurandher et al., 2021), and the Hybrid-P2 (Dhurandher et al., 2021) access methods.

Implementation of Cognitive Radio using SDR and GNU Concepts

The increased use of wireless transmission has increased the demand for wireless radio spectrum, which can be used for a variety of social and individual benefits. As a result, radio spectrum allocation and utilization are also critical tasks. Cognitive Radio (CR) is a cutting-edge technology that claims to overcome this problem by allowing unlicensed users to access the radio spectrum without interfering with licensed users.Cognitive radio is a new technique that uses an intelligent Software Defined Radio (SDR) mechanism to assist manage the impending spectrum issue through dynamic spectrum allocation and accommodate growing data traffic. With the use of software defined protocols, SDR avoids frequent changes to the hardware structure.The key innovation of this study is the practical implementation of CR on GNU radio for real-time transmission as a primary user utilizing an energy-based spectrum sensing approach. The goal of this project is to develop a spectrum sensing approach that is best suited for detecting white spaces during SDR transmission.

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Cognitive Radio (CR) system has been adopted for efficient utilization of radio frequency spectrum. The classification of signal modulation schemes is one of the main characteristics of the CR for appropriate demodulation of sensed signals. However, conventional Modulation Classification (MC) techniques require extensive extraction of signal features, which is not often guaranteed. Thus, Deep Learning (DL) has been seen as a promising solution to this drawback in MC. This paper proposes a DL-based MC technique using the Long Short-Term Memory (LSTM) network architecture. The proposed LSTM model was trained on M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude Modulation (MQAM) signal types. The LSTM directly learns the features of a given modulation scheme of a signal sample during training. The signal samples were generated via computer simulation for Signal-to-Noise Ratios (SNRs) from -10 dB to 20 dB with an interval of 5 dB over flat fading channel and Additive white Gaussian noise (AWGN). Simulation results show that the proposed LSTM model achieves an average classification accuracy of 95% at SNRs above 0 dB.

Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network.

Cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Comparison of Automatic Modulation Classification Techniques

The advancement of digital communication and technology triggered new challenges related to the channel and radio spectrum utilization. From the other hand, real-time communications are keen of time where requests need to be processed in very short time. Automatic modulation is one of promising approaches that relies on pretrained classifiers in order to recognize the type of modulation techniques used by the transmitter. Considering that noise is dominating between the transmitter and receiver, the task of automatic modulation classification is become harder. Noise is destroying the obvious features of the signals and degrade the classification accuracy. The modulation identification technique is made to recognize the type of modulation using the deep learning technology. This paper is listing the common stat of the arts used in automatic modulation classification along with their performance measures. It was realized that deep learning classifier manifested in Conventional Neural Network (CNN) is outperformed in AMC scoring of 85.41 % of recognition accuracy.

Control of Electromagnetic Radiation on Coexisting Smart Radio Environment

Efficient spectrum utilization is always the fundamental challenge of mobile communication technology toward 6G. Instead of conventional spectral efficiency in bps/Hz, geographical region shall be brought into consideration in bps/Hz/unit-area, which suggests spatial domain technology as a generalization of conventional MIMO to elevate next generation mobile communication technology. Therefore, this paper introduces the formation of smart radio environment considering efficient utilization of radio spectrum in any given geographical area, which can be also viewed as a generalization of cognitive radio technology. By smart management of reconfigurable intelligent surface (RIS) and ambient backscatter communication (ABC) technologies, smart radio environment can be formed in any given area to achieve spectral-spatial efficiency. In this paper, the control of electromagnetic spatial radiation over a region is explored by utilizing repositionable dynamic RIS and harmony of multiple ABC nodes assisted with machine learning (ML) based control mechanism to form spectrum map. Such smart and reconfigurable radio technology demonstrates the superiority of mutual usage of RIS and ABC in terms of shaping the electromagnetic energy in coexisting radio systems. First, the use of multiple RISs is studied to enhance the capacity of secondary use inside the specific area by shaping the electromagnetic energy in the spatial domain. Secondly, a new degree of freedom with repositionable dynamic RIS is introduced. Controlling the time varied shadowing effects by re-positioning the RIS, it is shown that 15% more capacity can be achieved. Finally, the use of multiple coordinated ABCs to protect the region of primary use against the radiation due the secondary uses is investigated. Having coordinated network with repositionable dynamic RIS and multiple ABCs allow us to shape the electromagnetic wave in spatial domain by creating radiation rejection and coverage extension zones. A centralized or distributed mechanism to construct the spectrum map based on ABC sensor is also introduced to instruct existing repositionable dynamic RISs for better coverage. Merging these promising technologies will pave the way for the smart radio environment creating high spectral efficient wireless systems.

A Centralized Model Enabling Channel Reuse for Spectrum Allocation in Cognitive Radio Networks

Abstract Cognitive Radio (CR) is an advanced technology, which intends to boost the radio spectrum utilization. On perceiving the spectrum holes, next there is a need to provide a fair distribution of the vacant licensed channels amongst Secondary Users (SUs) during the spectrum allocation process. In this context, our paper introduces two allocation models to resolve the spectrum allocation problem. Initially, we design a simple centralized model to assign the channels. Then, we extend it to a centralized fair allocation model that aims to impart a better utilization of the free channels. Both approaches assign a common channel to a group of non-interfering SUs simultaneously. This facilitates spectrum reuse. The constraint related to dynamics in spectrum opportunities in CR is handled during channel allocation. Simulation study analyzes the proposed approaches with an existing allocation mechanism and reveals the performance improvement of centralized fair allocation model in terms of spectrum utilization.

Analysis of energy‐efficient cooperative spectrum sensing with improved energy detectors and multiple antennas over Nakagami‐q/n fading channels

SummaryCognitive radio (CR) is an advanced technique used for the utilization of radio spectrum. A CR node uses the radio spectrum of a licensed user (called as primary user, PU) deviously. In this work, a cooperative sensing network (CSN) with improved energy detection (IED)‐based CR nodes is proposed. Each CR node consists of multiple antennas and perform themselves selection diversity (SC) technique. Each CR node senses a PU through erroneous sensing channel and reports its sensing data (1‐bit binary‐decision) to the fusion center (FC) through erroneous reporting channels. At FC, existence of PU (presence or absence) is identified using k‐out‐of‐N fusion rule. The novel expressions for detection probability over noise plus Nakagami‐q/n fading channels are derived and the mathematical frameworks for analysis of throughput and energy‐efficiency of the proposed network are developed. The simulation testbed is also developed and presented in this work to validate the analytically obtained performance characteristics. For several network parameters values, the investigation on comparison between IED and traditional energy detection method for various decision fusion rules is studied. Further, performance characteristics are analytically illustrated for throughput and energy‐efficiency over noise plus Nakagami‐q/n fading channels. The impact of diversity technique and effect of fading severity parameters on the throughput and energy‐efficiency are also investigated. The effect of channel error on total error performance for both proposed and conventional networks is shown. Finally, optimal fusion rule and optimal network parameters values correspond to maximum throughput and maximum energy‐efficiency are also identified.

Opportunistic Channel Allocation Model in Collocated Primary Cognitive Network

The growing demand of radio spectrum to facilitate the primary/secondary users in a cellular network is a challenging task. Many channel allocation models, applying cognition, have been proposed to increase the radio spectrum utilization. The proposed model peruses three types of users: primary users (PUs), opportunistic primary users (OPUs), and secondary users (SUs) that use the radio resources in collocated primary base stations. Out of these users, the opportunistic primary users and secondary users may request for handover as per their requirements. The objective of the model is to enhance the radio spectrum utilization by the opportunistic utilization of radio resources by OPUs and by enabling cognitive radio base stations to collect free channel information dynamically. The cognitive radio base station maintains the centralized free channel at collocated primary base stations to facilitate the SUs opportunistically. The proposed channel allocation technique maintains the Quality of Experience (QoE) of the users as well. The performance analysis of the model is done by simulation which diversifies the importance of the proposed model in the view of minimum blocked services.

Optimal energy efficiency resource allocation strategy for cognitive clustering network under PUEA attack

5G has pushed the use of radio spectrum to a new level, and cognitive clustering network can effectively improve the utilization of radio spectrum, which is a feasible way to solve the growing demand for wireless communications. However, cognitive clustering network is vulnerable to PUEA attack, which will lead to the degradation of system detection performance, thereby reducing the energy efficiency. Aiming at these problems, this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack. A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance. We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem, which is solved by Lagrangian function and Karush-Kuhn-Tucker condition. Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed. Simulation results indicate that proposed method is effective when subjected to PUEA attacks, and the impact of different parameters on energy efficiency is analyzed.

Imperfect CSI Based Intelligent Dynamic Spectrum Management Using Cooperative Reinforcement Learning Framework in Cognitive Radio Networks

The rapid development of wireless traffic pushed the wireless community to research different solutions towards the efficient utilization of the available radio spectrum. However, a recent study shows that most of the dynamically allocated spectrum bands (radio frequency resources), experience significant underutilization as cognitive radio (CR) technology still lacks intelligence. An intelligence in CRs can be incorporated with machine learning algorithms. Further, the perfect channel state information (CSI) is hardly obtained and CSI imperfections play a crucial role in Dynamic Spectrum Management. Thus, for efficient utilization of available spectrum, a decentralized Multi-Agent Reinforcement Learning based resource allocation scheme has been proposed. A robust resource allocation scheme is proposed which integrates machine learning and CR technology into a sophisticated multi-agent system (MAS). Moreover, assisted with cloud computing which provides a huge amount of storage space, reduces operating expenditures, and provides wider flexibility of cooperation. Hence, to foster the performance of the proposed scheme, a cooperative framework in MAS is introduced which enhances the performance of the proposed scheme in terms of network capacity, outage probability, and convergence speed. Numerical results verify the effectiveness of the proposed scheme and show the non-negligible impact of imperfect CSI, thus highlighting the importance of robust designs that maintains users’ QoS in practical wireless networks.

Energy-Efficient Spectrum Sensing for IoT Devices

Device-to-device communications have been considered as an indispensable enabler, which reduces the traffic burden associated with fifth-generation (5G) mobile networks. To improve the radio spectrum utilization under such a communications scheme, cognitive spectrum sensing can be used to identify temporarily available spectrum chunks for direct interconnections among user devices. Although various sensing techniques have been proposed during the last decade, improving the sensing efficiency (SE), such as energy reduction and positive sensing ratio, remains an open challenge. The problem becomes more pronounced in 5G networks, wherein battery-constrained Internet-of-Things devices (IoTDs) are densely interconnected. In this article, we optimize the SE by leveraging a lightweight yet effective adaptive medium learning method with a probabilistic decay feature. Specifically, the wireless channels that are likely available for IoTDs are sorted and sensed in the descending order of their availability likelihood/probabilities, which indicate the estimated percentage of the availability of the sensed channels. These probabilities learn from the preceding sensing results, and they decay with time. Numerical results show that the proposed sensing approach achieves significant SE improvement compared to state-of-the-art algorithms.

Performance Analysis of Different Channel Allocation Schemes of Random Access (RA) MAC Protocol with Back-Off Algorithm (BOA)

Cognitive radio technology is an effective method to improve the utilization and efficiency of radio spectrum. Cognitive radio technology solves spectrum scarcity problem by allowing cognitive users to use spectrum holes when primary users are absent. Cognitive radio networks are classified according to the infrastructure into: (A) centralized cognitive radio networks and (B) cognitive radio ad-hoc networks. The cognitive media access control (MAC) protocols are the master key of designing cognitive radio network. These allow cognitive users to identify and use the available frequency spectrum in a manner constrains the levels of interference to the primary users. Cognitive radio MAC protocols can be classified into: (A) random access control protocol (B) time slotted control protocols (C) hybrid protocols. In this paper, the dynamic open spectrum sharing (DOSS) MAC protocol is proposed as an example for random access control protocol. Due to the collision among users, the network throughput will be reduced. In order to enhance the performance of DOSS protocol, a back-off algorithm (BOA) will be executed and analyzed to enhance the network throughput. An optimum number of data retransmissions that decrease blocking and dropping ratio are investigated. Moreover, two accessing channels techniques for the proposed algorithm will be presented: fixed channel allocation (FCA) and dynamic channel allocation (DCA) schemes. DCA scheme allows to users to choose the control channel in a dynamic manner. The dynamic behavior decreases the limitation of control channel saturation. So, it can increase the network throughput. The performance analysis of the FCA and DCA schemes for the proposed protocol will be simulated. Finally, the performance analysis of DCA scheme of DOSS mac protocol with BOA will be investigated.

Real-Time Quantile-Based Estimation of Resource Utilization on an FPGA Platform Using HLS

Hardware accelerated modules that can continuously measure/analyze resource (frequency channels, power, etc.) utilization in real-time can help in achieving efficient network control, and configuration in cloud managed wireless networks. As utilization of various network resources over time often exhibits broad and skewed distribution, estimating quantiles of metrics to characterize their distribution is more useful than typical approaches that tend to focus on measuring average values only. In this paper, we describe the development of a real-time quantile-based resource utilization estimator module for wireless networks. The intensive processing tasks run on the FPGA, while the command and control runs on an embedded ARM processor. The module is implemented by using high level synthesis (HLS) on a Xilinx's Zynq-7000 series all programmable system on chip board. We test the performance of the implemented quantile estimator module, and as an example, we focus on forecasting congestion with real frequency channel utilization data. We compare the results from the implemented module against the results from a theoretical quantile estimator. We show that with high accuracy and in real time, the implemented module can perform quantile estimation and can be utilized to perform forecasting of congestion in wireless frequency spectrum utilization.