29 research outputs found
Using hypergraph theory to model coexistence management and coordinated spectrum allocation for heterogeneous wireless networks operating in shared spectrum
Electromagnetic waves in the Radio Frequency (RF) spectrum are used to convey wireless transmissions from one radio antenna to another. Spectrum utilisation factor, which refers to how readily a given spectrum can be reused across space and time while maintaining an acceptable level of transmission errors, is used to measure how efficiently a unit of frequency spectrum can be allocated to a specified number of users.
The demand for wireless applications is increasing exponentially, hence there is a need for efficient management of the RF spectrum. However, spectrum usage studies have shown that the spectrum is under-utilised in space and time. A regulatory shift from static spectrum assignment to DSA is one way of addressing this. Licence exemption policy has also been advanced in Dynamic Spectrum Access (DSA) systems to spur wireless innovation and universal access to the internet. Furthermore, there is a shift from homogeneous to heterogeneous radio access and usage of the same spectrum band. These three shifts from traditional spectrum management have led to the challenge of coexistence among heterogeneous wireless networks which access the spectrum using DSA techniques.
Cognitive radios have the ability for spectrum agility based on spectrum conditions. However, in the presence of multiple heterogeneous networks and without spectrum coordination, there is a challenge related to switching between available channels to minimise interference and maximise spectrum allocation. This thesis therefore focuses on the design of a framework for coexistence management and spectrum coordination, with the objective of maximising spectrum utilisation across geographical space and across time. The amount of geographical coverage in which a frequency can be used is optimised through frequency reuse while ensuring that harmful interference is minimised. The time during which spectrum is occupied is increased through time-sharing of the same spectrum by two or more networks, while ensuring that spectrum is shared by networks that can coexist in the same spectrum and that the total channel load is not excessive to prevent spectrum starvation.
Conventionally, a graph is used to model relationships between entities such as interference relationships among networks. However, the concept of an edge in a graph is not sufficient to model relationships that involve more than two entities, such as more than two networks that are able to share the same channel in the time domain, because an edge can only connect two entities. On the other hand, a hypergraph is a generalisation of an undirected graph in which a hyperedge can connect more than two entities. Therefore, this thesis investigates the use of hypergraph theory to model the RF environment and the spectrum allocation scheme.
The hypergraph model was applied to an algorithm for spectrum sharing among 100 heterogeneous wireless networks, whose geo-locations were randomly and independently generated in a 50 km by 50 km area. Simulation results for spectrum utilisation performance have shown that the hypergraph-based model allocated channels, on average, to 8% more networks than the graph-based model. The results also show that, for the same RF environment, the hypergraph model requires up to 36% fewer channels to achieve, on average, 100% operational networks, than the graph model. The rate of growth of the running time of the hypergraph-based algorithm with respect to the input size is equal to the square of the input size, like the graph-based algorithm. Thus, the model achieved better performance at no additional time complexity.Electromagnetic waves in the Radio Frequency (RF) spectrum are used to convey wireless transmissions from one radio antenna to another. Spectrum utilisation factor, which refers to how readily a given spectrum can be reused across space and time while maintaining an acceptable level of transmission errors, is used to measure how efficiently a unit of frequency spectrum can be allocated to a specified number of users.
The demand for wireless applications is increasing exponentially, hence there is a need for efficient management of the RF spectrum. However, spectrum usage studies have shown that the spectrum is under-utilised in space and time. A regulatory shift from static spectrum assignment to DSA is one way of addressing this. Licence exemption policy has also been advanced in Dynamic Spectrum Access (DSA) systems to spur wireless innovation and universal access to the internet. Furthermore, there is a shift from homogeneous to heterogeneous radio access and usage of the same spectrum band. These three shifts from traditional spectrum management have led to the challenge of coexistence among heterogeneous wireless networks which access the spectrum using DSA techniques.
Cognitive radios have the ability for spectrum agility based on spectrum conditions. However, in the presence of multiple heterogeneous networks and without spectrum coordination, there is a challenge related to switching between available channels to minimise interference and maximise spectrum allocation. This thesis therefore focuses on the design of a framework for coexistence management and spectrum coordination, with the objective of maximising spectrum utilisation across geographical space and across time. The amount of geographical coverage in which a frequency can be used is optimised through frequency reuse while ensuring that harmful interference is minimised. The time during which spectrum is occupied is increased through time-sharing of the same spectrum by two or more networks, while ensuring that spectrum is shared by networks that can coexist in the same spectrum and that the total channel load is not excessive to prevent spectrum starvation.
Conventionally, a graph is used to model relationships between entities such as interference relationships among networks. However, the concept of an edge in a graph is not sufficient to model relationships that involve more than two entities, such as more than two networks that are able to share the same channel in the time domain, because an edge can only connect two entities. On the other hand, a hypergraph is a generalisation of an undirected graph in which a hyperedge can connect more than two entities. Therefore, this thesis investigates the use of hypergraph theory to model the RF environment and the spectrum allocation scheme.
The hypergraph model was applied to an algorithm for spectrum sharing among 100 heterogeneous wireless networks, whose geo-locations were randomly and independently generated in a 50 km by 50 km area. Simulation results for spectrum utilisation performance have shown that the hypergraph-based model allocated channels, on average, to 8% more networks than the graph-based model. The results also show that, for the same RF environment, the hypergraph model requires up to 36% fewer channels to achieve, on average, 100% operational networks, than the graph model. The rate of growth of the running time of the hypergraph-based algorithm with respect to the input size is equal to the square of the input size, like the graph-based algorithm. Thus, the model achieved better performance at no additional time complexity
Optimal resource allocation for GAA users in spectrum access system using Q-learning algorithm
Spectrum access system (SAS) is a three-tier layered spectrum sharing architecture proposed by the Federal Communications Commission (FCC) for Citizens Broadband Radio Service (CBRS) 3.5 GHz band. The available 150 MHz spectrum is dynamically shared among Incumbent Access (IA), Primary Access Licensees (PAL) and General Authorized Access (GAA) users. IA users are the highest priority federal military users, PAL users are the licensed users and the GAA users are the least priority unlicensed users. In this scenario, PAL operators are willing to give access to their idle spectrum to GAA users to generate extra revenue. SAS will ensure to protect IA users and PAL users from interference caused by lower-tier users. It is the responsibility of SAS to allocate resources to GAA users but the method to do so is left open. In this article, a novel auction algorithm based on Q-learning for dynamic spectrum access (SAS-QLA) is proposed. In SAS-QLA, multiple GAA users dynamically and intelligently bid using Q-learning to access PAL reserved idle channels. SAS will decide to allocate the channels to GAA users with maximum bidding offers. GAA users have their own quality of service (QoS) demands i.e., transmission rate, packet loss, bidding efficiency, and maintain the preference of available PAL reserved idle channels based on Q-learning considering the available QoS. The proposed scenario is also modeled as a knapsack NP-hard problem and solved using dynamic programming and distributed relaxation method. Numerical results demonstrate the effectiveness of the SAS-QLA algorithm in improving the bidding efficiency, maximizing the data rate per unit cost and spectrum utilization.Web of Science10608046079
Wideband Spectrum Sensing for Dynamic Spectrum Sharing
The proliferation of wireless devices grows exponentially, demanding more and more data
communication capacity over wireless links. Radio spectrum is a scarce resource, and traditional
wireless networks deployed by Mobile Network Operators (MNO) are based on an exclusive
spectrum band allocation. However, underutilization of some licensed bands in time and geographic
domains has been reported, especially in rural areas or areas away from high population density
zones. This coexistence of increasingly high data communication needs and spectrum
underutilization is an incomprehensible scenario. A more rational and efficient use of the spectrum
is the possibility of Licensed Users (known as Primary Users – PU) to lease the spectrum, when
not in use, to Unlicensed Users (known as Secondary Users – SU), or allowing the SU to
opportunistically use the spectrum after sensing and verifying that the PU is idle. In this latter
case, the SU must stop transmitting when the PU becomes active.
This thesis addresses the spectrum sensing task, which is essential to provide dynamic spectrum
sharing between PUs and SUs. We show that the Spectral Correlation Function (SCF) and the
Spectral Coherence Function (SCoF) can provide a robust signal detection algorithm by exploiting
the cyclostationary characteristics of the data communication signal. We enhance the most used
algorithm to compute de SCF - the FAM (FFT Accumulation Method) algorithm – to efficiently
compute the SCF in a local/zoomed region of the support ( ; ) plane (frequency/cycle frequency
plane). This will provide the quick identification of spectral bands in use by PUs or free, in a
wideband sampling scenario.
Further, the characterization of the probability density of the estimates of the SCF and SCoF
when only noise is present, using the FAM algorithm, will allow the definition of an adaptive
threshold to develop a blind (with respect to the noise statistics) Constant False Alarm Rate
(CFAR) detector (using the SCoF) and also a CFAR and a Constant Detection Rate (CDR)
detector when that characterization is used to obtain an estimate of the background noise variance
(using the SCF).A proliferação de dispositivos sem fios cresce de forma exponencial, exigindo cada vez mais
capacidade de comunicação de dados através de ligações sem fios. O espectro radioelétrico é um
recurso escasso, e as redes sem fios tradicionais implantadas pelos Operadores de Redes Móveis
baseiam-se numa atribuição exclusiva de bandas do espectro. No entanto, tem sido relatada a
subutilização de algumas bandas licenciadas quer ao longo do tempo, quer na sua localização
geográfica, especialmente em áreas rurais, e em áreas longe de zonas de elevada densidade
populacional. A coexistência da necessidade cada vez maior de comunicação de dados, e a
subutilização do espectro é um cenário incompreensível. Uma utilização mais racional e eficiente
do espectro pressupõe a possibilidade dos Utilizadores Licenciados (conhecidos como Utilizadores
Primários – Primary Users - PU) alugarem o espectro, quando este não está a ser utilizado, a
Utilizadores Não Licenciados (conhecidos como Utilizadores Secundários – Secondary Users - SU),
ou permitir ao SU utilizar oportunisticamente o espectro após a deteção e verificação de que o PU
está inativo. Neste último caso, o SU deverá parar de transmitir quando o PU ficar ativo.
Nesta tese é abordada a tarefa de deteção espectral, que é essencial para proporcionar a partilha
dinâmica do espectro entre PUs e SUs. Mostra-se que a Função de Correlação Espectral (Spectral
Correlation Function - SCF) e a Função de Coerência Espectral (Spectral Coherence Function -
SCoF) permitem o desenvolvimento de um algoritmo robusto de deteção de sinal, explorando as
características ciclo-estacionárias dos sinais de comunicação de dados. Propõe-se uma melhoria ao
algoritmo mais utilizado para cálculo da SCF – o método FAM (FFT Accumulation Method) -
para permitir o cálculo mais eficiente da SCF numa região local/ampliada do plano de suporte
/ (plano de frequência/frequência de ciclo). Esta melhoria permite a identificação rápida de
bandas espectrais em uso por PUs ou livres, num cenário de amostragem de banda larga.
Adicionalmente, é feita a caracterização da densidade de probabilidade das estimativas da SCF e
SCoF quando apenas o ruído está presente, o que permite a definição de um limiar adaptativo,
para desenvolver um detetor de Taxa de Falso Alarme Constante (Constant False Alarm Rate –
CFAR) sem conhecimento do ruído de fundo (usando a SCoF) e também um detetor CFAR e Taxa
de Deteção Constante (Constant Detection Rate – CDR), quando se utiliza aquela caracterização
para obter uma estimativa da variância do ruído de fundo (usando a SCF)
Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) downlink interference based on Artificial Neural Network Learning Models (ANN-LMS)
Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques.Agencia Estatal de Investigación | Ref. PID2020-115323RB-C33Agencia Estatal de Investigación | Ref. PID2020-113240RB-I0
Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid
The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency.
To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario.
In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices.
To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches
The Intersection of Radar and Communications: A Study on Spectrum Management for Addressing RF Interference
The radio frequency (RF) spectrum is a fruitful yet competitive frontier that enables technologies like 5G, Wi-Fi, Bluetooth, GPS, long-range telescopes, microwave ovens, radar, and more. With finite supply and increasing demand, the RF spectrum is highly contested for both government and private use. Industry innovators need an increasing stake in the spectrum to keep up with modern data consumption needs, yet governments around the world require the same spectrum for important issues like national defense. This duality in demand often results in highly congested bands of frequency that host both stakeholders in dense configuration, increasing interference and difficulties in managing the spectrum. Interference of radar signals by communication waveforms, like orthogonal frequency division multiplexing (OFDM), is on the rise and can greatly impact system performance. This thesis introduces a novel interference mitigation algorithm that leverages the known structure of OFDM waveforms to estimate and subtract interference from a pulse-Doppler radar system. The proposed technique can significantly improve radar performance in the face of OFDM interference and quantify interference metrics to inform new regulations pertaining to spectrum management
Observation of WiMAX Radio Parameters to Enhance Spectrum Utilization in Mixed Environment, Journal of Telecommunications and Information Technology, 2018, nr 1
It is believed that 5G networks will provide 1000 times more capacity than current solutions. One of the keys to achieve that goal is not only the utilization of additional radio bands, but also and foremost, the dynamic and efficient spectrum sharing. To successfully implement it such feature statistical observation and analysis of currently operational legacy systems are required. Comprehensive data on the signal parameters will allow then to determine and tune the approach to simultaneous bandwidth usage by existing and new systems. Therefore, to define and introduce the problem this paper presents a conceptual analysis of IEEE 802.16e based WiMAX network operating in the 3.6--3.8 GHz band on the eve of spectrum sharing introduction