In this paper, spectrum sensing techniques with dynamic primary user (PU) are considered in the environment of Laplacian noise. It means the PU may not be present or absent during the whole sensing time. However, PU arrives or departs randomly in the sensing time interval. We consider three different detection schemes such as energy detection, absolute value cumulation detection (AVCD) and improved AVCD (i-AVCD). We present the performance in terms of receiver operating characteristic (ROC) and detection probability versus average signal-to-noise ratio (SNR) using simulations. We conclude that the detection performance in the dynamic scenario is better than the performance in the static scenario, when the arrival/departure parameter ( θ _AT / θ _DT ) is beyond one, where θ _A and θ _D are corresponding to arrival rate and departure rate of the PU respectively, and T is the sampling interval. Furthermore, the i-AVCD scheme outperforms AVCD and energy detection in the considered scenario.
In this work, a tool for topology sensing of a non-collaborative wireless network using power profiles captured by radio-frequency (RF) sensors is proposed. Assuming that the features of the network (i.e., the number of nodes, medium access control (MAC) and routing protocols) are unknown and that the sensors observe signal mixtures because of the wireless medium, blind source separation (BSS) is used to separate the traffic profiles. Successively, the topology of the network is inferred by detecting causal relationships between the separated streams. According to the numerical results, the proposed tool senses the topology with promising accuracy when operating in mild shadowing conditions, even with a relatively low number of radio-frequency (RF) sensors.
Active user detection is a standard problem that concerns many applications using random access channels in cellular or ad hoc networks. Despite being known for a long time, such a detection problem is complex, and standard algorithms for blind detection have to trade between high computational complexity and detection error probability. Traditional algorithms rely on various theoretical frameworks, including compressive sensing and bayesian detection, and lead to iterative algorithms, e.g. orthogonal matching pursuit (OMP). However, none of these algorithms have been proven to achieve optimal performance. This paper proposes a deep learning based algorithm (NN-MAP) able to improve on the performance of state-of-the-art algorithm while reducing detection time, with a codebook known at training time.
The Deferred Acceptance (DA) algorithm has often been used to study spectrum sharing and to solve the assignment problem of Device-to-Device (D2D) links co-existing with traditional cellular users. We present a new reward-based DA algorithm denoted as priced-DA or (p-DA) to improve reuse gains in scenarios where there are variations in the number of cellular users and D2D links and when there are variations in the length of the preference lists. Interference coordination is implemented by jointly managing the power allocation and quality of service (QoS) admission control based on the inter-distances between devices. Simulation case studies demonstrate the advantages of the presented p-DA technique in comparison to other tested centralised approaches.
Mobile communication research is increasingly addressing the use of 5G in verticals, which has led to the emergence of local and often private 5G networks. At the same time, research on 6G has started, with a bold goal of building a strong linkage between 6G and the United Nations Sustainable Development Goals (UN SDGs). Both of these developments call for a highly multi-disciplinary approach covering the inter-related perspectives of business, regulation and technology. This paper summarizes recent advances in using 5G to serve vertical sectors’ needs and describes a path towards sustainable 6G considering business, regulation and technology viewpoints. By focusing on key trends, the research summarizes four alternative scenarios for the futures business of 6G and considers related regulatory and technology aspects. Our findings highlight the importance of understanding the complex relations of business, regulation and technology perspectives and the role of ecosystems in both 5G in verticals and ultimately in the development of sustainable 6G to bring together stakeholders to solve long-term sustainability problems.
New spectrum bands are being released to respond to the growing need for locally deployed industrial and private networks. This calls for new licensing schemes and spectrum sharing approaches. New challenges are faced from the ever-increasing variety of released spectrum bands with different technical and operational requirements and the increasing fragmentation of spectrum management approaches. While the standardization is progressing and technical solutions are developed for the new networks, less attention has been paid to the radio product related regulation, including equipment authorization frameworks. With roots in engineering, policy and economics, this paper looks through the lenses of business model framework at scenarios of wireless equipment authorization related to novel spectrum management approaches. This paper provides an overview of radio authorization for mobile communication networks and develops a conceptual framework to depict and analyze authorization policies. The results indicate the strong impact the authorization frameworks have on the scalability and replicability of the business. This calls for novel models of governance and regulation that highlight utilization of harmonized, widely employed frequency bands and the associated technical requirements.
One of the challenges in cooperative spectrum sensing is to optimize the energy consumption of the network. Delivery of all measurements from all the nodes to the fusion centre is not the best solution from the perspective of energy-efficiency. Clustering of nodes with similar channel conditions may reduce the amount of transmitted data, and in consequence reduce the amount of consumed energy. In this paper we investigate the performance of selected algorithms known in the domain of artificial intelligence, applied to perform reliable yet energy-aware spectrum sensing.
We consider the communication between a transmitter (user) and a receiver in the presence of a jammer where the jammer, in contrast to the user, has access to local information about jamming fading gain (reflecting distance of the jammer to the receiver) and jamming cost (reflecting its technical characteristics). The problem is modeled as a Bayesian game. Signal-to-interference-plus-noise ratio (SINR) is considered as user’s communication utility. Nash equilibrium as well as Stackelberg equilibrium are derived in closed form and compared.
The ongoing 5G evolution transforming network from connectivity driven to service dominant logic will impact the stakeholder roles, ecosystem and business models. Systemic change will lower the barriers to entry and expand the ecosystem to new roles such as local operators, edge cloud services providers and resource aggregators and agents. Spectrum regulation has traditionally acted as a gate keeper of the mobile service provisioning, and lately national authorities have reacted via allocating new frequency bands and considering novel flexible spectrum administration and management methods and tools. This paper provides a comprehensive overview of the most recent spectrum regulation decisions for mobile communication networks and shows how local licensing, spectrum sharing, and unlicensed commons approaches work as novel business model antecedent. The study analyzes key spectrum antecedents for the open ecosystemic business model value configuration.
This paper presents the improved distance estimation for the purpose of Database-assisted Autonomous Platooning and V2V channel modelling. The proposed approach combines commonly used GPS-based measurements with UWB-based measurements to benefit from both solutions. While GPS allow for unlimited measurement range, the UWB improves accuracy for short range. The paper is based on real-word measurements.
A software defined radio (SDR) approach to demonstrate the coexistence in Machine Type Communication (MTC) scenarios is presented. MTC in recent years has gained significant attention with its inclusion in the 5G business model. Spectrally efficient asynchronous communication is a key enabler in situations involving MTC. Past research has shown that some modifications to baseline cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) can achieve better out-of-band (OOB) suppression and enable asynchronous coexistence. Inspired by this research, we provide a real world example of coexistence using SDR. We demonstrate the ability to asynchronously transmitting waveforms in adjacent channels with very narrow guard bands in between, and still be able to receive and demodulate them with low error vector magnitude (EVM) and low bit error rate (BER) that are comparable to the baseline CP-OFDM that uses synchronous communication.
Intelligent transportation is an important component of future smart cities, and electric autonomous vehicles (EAVs) are envisioned to be the main form of transportation because EAVs can save energy, protect the environment, and improve service efficiency. With limited vehicle-specific energy storage capacity and overall constraint in the smart grid’s electric load, we propose a novel intelligent management scheme to jointly schedule the travel and charging activities of the EAV fleet in one geographical area. This scheme not only schedules EAVs to meet the passengers’ requests but also explores the matching problem between the energy requirement of EAVs and the deployment of charging piles in smart cities. We minimize the total cruise energy consumption of EAVs under the condition of limited energy supply while guaranteeing the quality-of-service (QoS). Network Calculus (NC) is extended to model the electric traffic flow in this paper. With the real-world electric taxi data in Beijing, simulation results demonstrate that the proposed scheme can achieve substantial energy reduction and remarkable improvements in both the order completion rate and utilization rate of the charging stations.
One of the beyond-5G developments that is often highlighted is the integration of wireless communication and radio sensing. This paper addresses the potential of communication-sensing integration of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the potential for high throughput and efficient multiplexing of wireless links, an LIS can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment, we develop sensing techniques that leverage the usage of computer vision combined with machine learning. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.
In this paper, we present a realistic use case in order to investigate the feasibility of a secondary service transmission over an existent satellite infrastructure. By introducing the overlay cognitive radio paradigm towards satellite communications, we compute a theoretical achievable data rate greater than 16 kbps for the secondary service, which is suitable for most M2M applications. Using simulation results, we show that this can be achieved while preserving the primary service performance. In addition, a system design framework is discussed in order to dimension such systems.
We present a techno-economic analysis of a cellular market that operates under the licensed shared access (LSA) regime, consisting of a mobile network operator (MNO) that leases spectrum to a number of Programme Making and Special Events (PMSE) users. The MNO offers two quality-of-service (QoS) classes (high and low), differentiating the price based on the QoS class. The key question that we address is whether and to which extent the MNO has incentive to adopt this form of QoS-aware pricing. The first step is to model the parameters that are controlled by each PMSE user: i) the way to choose between the two QoS classes and ii) the available budget per QoS class. The second step is to compute the maximum revenue of the MNO. Our analysis reveals that the MNO can always tune the prices so as to maximise its revenue for the scenario where all users belong to the high QoS class. This is a consistent result throughout our study, that holds for any considered set of user-controlled parameters and of technical parameters. We conclude that the adoption of QoS-aware pricing in the LSA market generates a tussle between the MNO and the regulator. The MNO has incentive to support fewer users but with high QoS and charge them more, which is not aligned with the regulator's goal for social welfare maximisation.
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real world situations where topologies evolve over time. To remedy this, the work rests on a series of datasets gathered in the Future Internet of Things / Cognitive Radio Testbed [4] (FIT/CorteXlab) to train a convolutional neural network (CNN), where focus has been given to reduce channel bias that has plagued previous works and constrained them to a constant environment or to simulations. The most challenging scenarios provide the trained neural network with resilience and show insight on the best signal type to use for identification , namely packet preamble. The generated datasets are published on the Machine Learning For Communications Emerging Technologies Initiatives web site 4 in the hope that they serve as stepping stones for future progress in the area. The community is also invited to reproduce the studied scenarios and results by generating new datasets in FIT/CorteXlab.
In this paper, we present the dependency between density of a sensor network and map quality in the radio environment map (REM) concept. The architecture of REM supporting military communications systems is described. The map construction techniques based on spatial statistics and transmitter location determination are presented. The problem of REM quality and relevant metrics are discussed. The results of field tests for UHF range with a different number of sensors are shown. Exemplary REM maps with different interpolation algorithms are presented. Finally, the problem of density of a sensor network versus REM map quality is analyzed.
As new mobile base stations (mBSs) have been constantly developed with various capacities, mobile coverage, and mobility models, the level of heterogeneity in public safety networks (PSNs) has been increasing. Since disasters and emergencies require the ad hoc PSN deployments, dynamic mBS placement and movement algorithm is one of the most important decisions to provide the critical communication channels for first responders (FRs). In this paper, we propose a heterogeneous mBS placement algorithm in an ad hoc public safety network. We define different classes of mobile base stations that have varying performance characteristics and consider three different FRs mobility models. Our proposed algorithm applies the modern clustering technique to deal with the characteristics of different kinds of mBSs.
Since the main feature of Named Data Network (NDN) is in-net caching, it is crucial to motivate users to offer resource such as bandwidth and storage. However, few research works on incentive mechanism design for NDN. This paper proposes a market for NDN to lease bandwidth and storage from Access Points (APs). Since blockchain can supply a traceable and credible environment while public chain has long latency and low throughput, the paper combines permissioned chain with public chain, constructs a hybrid chain based environment without hurting its truthfulness. Furthermore, the paper formulates the market as a reverse auction running by a Content Provider (CP) who aims to serve more users for profit by leasing resource from APs, and investigates incentive mechanism for motivating APs. Especially, the paper designs an optimal mechanism, which could overcome defects of traditional mechanism, get the most profit for CP with guaranteeing interest of AP. Evaluation results compare effectiveness of mechanism proposed with traditional incentive mechanism, and prove that the mechanism we designed could get better results.
With the ever-increasing demand for spectrum to support wireless innovation, it is critical to understand the fine-grained characteristics of spectrum use in frequency, space and time to facilitate greater spectrum sharing. Contextual information is needed to analyze how the spectrum is being utilized and understand the drivers for spectrum use dynamics. Since human activity often drives spectrum use, understanding this activity can provide significant insight. Analysis of wideband spectrum is extremely time consuming as each band has unique characteristics, domain knowledge and usage drivers. Toward automated analysis, this paper proposes an approach to incorporate contextual information into the analysis utilizing semantic models to capture domain and human activity knowledge. This approach is illustrated through analysis of spectrum measurements of four frequencies licensed to the Chicago White Sox.
