Efficient Dynamic Spectrum Access Research Articles

Device-to-device Communications at the Terahertz Band: Open Challenges for Realistic Implementation

One of the key parameters that plays a major role in enabling the data rate requirements is spectrum or bandwidth, which is scarce and expensive. Therefore, spectrum management policies are required to optimize its usage to meet all the requirements and the promised data rate growth. Another strategy to deal with spectrum scarcity is to move toward higher frequency bands (terahertz bands), which are expected in the next 6G communication standard. It is therefore important to develop not only new techniques that enable efficient dynamic spectrum access and sharing at such bands, but also suitable channel models for the terahertz bands. Meanwhile, offloading mechanisms are very promising for cellular networks where a plethora of options have been proposed in the research arena in terms of device-to-device, licensed assisted access, or WiFi offloading, among others; but their behavior, when operated at high frequencies (terahertz band) remains unclear. Therefore, this article will tackle two technologies that will shape future networks: terahertz channel modeling/communications and offloading mechanisms.

PRAM: A Practical Sybil-Proof Auction Mechanism for Dynamic Spectrum Access With Untruthful Attackers

Auction is becoming increasingly popular for dynamic spectrum access (DSA), while it is extremely vulnerable to sybil attacks. Existing studies on sybil-proof DSA auction impractically assume that attackers bid truthfully based on true appraisals. This paper, for the first time, considers untruthful attackers and investigates the sybil-proof auction design in such more hazardous scenarios. To justify the new assumption, we first show that attackers obtain higher utilities by bidding untruthfully, especially in networks with inadequate channels. Based on this novel finding, we then design a practical sybil attack model named EqualSumBid Sybil, where attackers follow an equal-sum rule (i.e., the sum bid value of the multiple identities of an attacker equals the bid value when it bids with only one identity) instead of their true appraisals. To ensure efficient DSA under the new attack, we finally propose the PRAM, a Practical sybil-pRoof Auction Mechanism, where suspicious identity merging and bid-independent bidder sorting methods are introduced to alleviate the effect of untruthfulness on spectrum auction. Furthermore, winner selection and payment methods are designed to resist the EqualSumBid Sybil attack. Theoretical analyses and numerical results show that PRAM not only resists the EqualSumBid Sybil attack but also achieves individual rationality and truthfulness.

Spectrum utilization efficiency in the cognitive radio enabled 5G-based IoT

The cognitive radio enabled cooperative communication is envisioned to be a vital part of the 5G-based Internet of Things (IoT). Since IoT contains billions of resource constrained devices, spectrum utilization efficiency (SUE) becomes a significant problem in order to accommodate such a large number of devices within the scarce spectrum. Therefore, in this paper, SUE of cognitive radio networks (CRNs) is investigated in terms of channel availability and service completion probability. We propose a spectrum efficient CRN (SE-CRN) that employs a hybrid underlay-interweave (UI) mode of CRNs for secondary users (SUs) under cooperative communication. Two types of SUs, with low and high priority levels, are considered. Moreover, a multi-attribute based approach for service determination for low priority users’ interruption is proposed that ensures the optimal channel availability for high priority users. SE-CRN is composed of two algorithms, namely, spectrum efficient dynamic spectrum access (SE-DSA) and spectrum efficient dynamic channel reservation (SE-DCR). SE-DSA dynamically assigns available channels to primary and secondary users based on their priority levels. SE-DCR dynamically keeps a number of channels reserved to support interruption of ongoing services. We utilize continuous time Markov chain (CTMC) for modeling and also form mathematical expressions for a number of QoS parameters. The scheme is evaluated under varying levels of network traffic loads and multiple rates of channel failure. We show through numerical results that SUE can be reasonably enhanced by SE-CRN.

Design of Spectrum Usage Detection in Wideband Spectrum Measurements

We investigate the design of signal processing in wideband spectrum usage (SPU) measurements for efficient and smart dynamic spectrum access (DSA). In particular, we focus on spectrum usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

AbstractFor a cognitive radio (CR) user to dynamically access the available primary user channels, the spectrum sensing data is required at various locations; however, the data is not generally available at the points present in between the two spectrum sensors. To obtain this data, it is also not feasible to conduct the spectrum measurement surveys at every location. This calls for an interpolation‐based spectrum occupancy data collection in the space. Moreover, the information about the future primary user activity is necessary for highly enhanced dynamic channel allocation with better quality of experience (QoE) of CR users in terms of improved channel utilization and reduced spectrum sensing energy requirement. Most of the existing works are inclined toward either spatial spectrum interpolation or temporal spectrum prediction. In this work, a novel hybridized approach combining the Kriging‐based statistical spatial interpolation and the recurrent neural networks–based temporal prediction of the spectrum has been proposed to obtain the spectrum occupancy probability information using empirical spectrum measurements data in cellular CR networks. Furthermore, the aforementioned framework has been utilized for dynamic channel allocation with the help of a newly investigated CR channel allocation scheme. The corresponding results have been validated through various CR QoE measures and compared with the existing channel allocation strategies, where it is found that the proposed channel allocation scheme significantly outperforms the other schemes with the enhanced QoE of CR. Finally, a spatio‐temporal channel allocation map has been presented for secondary user to perform its best possible CR operation with efficient dynamic spectrum access.

Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models

Spectrum occupancy information is necessary in a cognitive radio network (CRN) as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access (DSA). However, in a CRN, it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior. In this context, the spectrum occupancy prediction proves to be very useful in enhancing the quality of experience of the secondary user. This paper investigates the practical prowess of various time-series modeling approaches and the machine learning (ML) techniques for predicting spectrum occupancy, based on a spectrum measurement campaign conducted in Jaipur, Rajasthan, India. Moreover, the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data. Nevertheless, prediction through ML-based recurrent neural network proves to perform reasonably well, thereby providing an accurate future spectrum occupancy information for DSA.

Virtualization-based Cognitive Radio Networks

The emerging network virtualization technique is considered as a promising technology that enables the deployment of multiple virtual networks over a single physical network. These virtual networks are allowed to share the set of available resources in order to provide different services to their intended users. While several previous studies have focused on wired network virtualization, the field of wireless network virtualization is not well investigated. One of the promising wireless technologies is the Cognitive Radio (CR) technology that aims to handle the spectrum scarcity problem through efficient Dynamic Spectrum Access (DSA). In this paper, we propose to incorporate virtualization concepts into CR Networks (CRNs) to improve their performance. We start by explaining how the concept of multilayer hypervisors can be used within a CRN cell to manage its resources more efficiently by allowing the CR Base Station (BS) to delegate some of its management responsibilities to the CR users. By reducing the CRN users’ reliance on the CRN BS, the amount of control messages can be decreased leading to reduced delay and improved throughput. Moreover, the proposed framework allows CRNs to better utilize its resources and support higher traffic loads which is in accordance with the recent technological advances that enable the Customer-Premises Equipments (CPEs) of potential CR users (such as smart phone users) to concurrently run multiple applications each generating its own traffic. We then show how our framework can be extended to handle multi-cell CRNs. Such an extension requires addressing the self-coexistence problem. To this end, we use a traffic load aware channel distribution algorithm. Through simulations, we show that our proposed framework can significantly enhance the CRN performance in terms of blocking probability and network throughput with different primary user level of activities.

Software Defined Cognitive Radio Network Framework

Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.

A FRAMEWORK FOR STATISTICAL CHARACTERIZATION OF INDOOR DATA TRAFFIC FOR EFFICIENT DYNAMIC SPECTRUM ACCESS IN THE 2.4 GHZ ISM BAND

The key for efficient dynamic spectrum access (DSA) is to model the spectral resources accurately. A large number of measurement campaigns have been performed to estimate the spectrum usage in outdoor and indoor scenarios. This spectrum usage estimation helps policy makers to optimize the spectrum management methodologies. The spectrum usage studies also assist researchers to constitute a way for efficient DSA using prior knowledge of the distribution of the observed data traffic in cognitive radio (CR) systems. In this paper we extend our previous work which statistically modeled the observed data traffic in the industrial, scientific and medical (ISM) band at 2.4 -GHz in two neighboring frequency subbands and time slots, respectively, to three neighboring frequency subbands and time slots, respectively. As before, the frequency and time correlation functions of the observed data traffic are modeled by an exponentially decaying function. The multivariate Gaussian mixture (MGM) is validated as a good candidate to model the joint distribution of measured data and also to estimate the correlation between the measured data in neighboring frequency subbands and as well as in time domain samples.

Low complexity and efficient dynamic spectrum learning and tunable bandwidth access for heterogeneous decentralized cognitive radio networks

This paper deals with the design of the low complexity and efficient dynamic spectrum learning and access (DSLA) scheme for next-generation heterogeneous decentralized Cognitive Radio Networks (CRNs) such as Long Term Evolution-Advanced and 5G. Existing DSLA schemes for decentralized CRNs are focused predominantly on the decision making policies which perform the task of orthogonalization of secondary users to optimum vacant subbands of fixed bandwidth. The focus of this paper is the design of DSLA scheme for decentralized CRNs to support the tunable vacant bandwidth requirements of the secondary users while minimizing the computationally intensive subband switchings. We first propose a new low complexity VDF which is designed by modifying second order frequency transformation and subsequently combining it with the interpolation technique. It is referred to as Interpolation and Modified Frequency Transformation based VDF (IMFT-VDF) and it provides tunable bandpass responses anywhere over Nyquist band with complete control over the bandwidth as well as the center frequency. Second, we propose a tunable decision making policy, ρt_rand, consisting of learning and access unit, and is designed to take full advantage of exclusive frequency response control offered by IMFT-VDF. The simulation results verify the superiority of the proposed DSLA scheme over the existing DSLA schemes while complexity comparisons indicate total gate count savings from 11% to as high as 87% over various existing schemes. Also, lower number of subband switchings make the proposed scheme power-efficient and suitable for battery-operated cognitive radio terminals.

Decentralized Bargain: A Two-Tier Market for Efficient and Flexible Dynamic Spectrum Access

Market mechanisms have been exploited as important means for spectrum acquisition and access in cognitive radio networks. In this paper, we propose a two-tier market for decentralized dynamic spectrum access. In the proposed Tier-1 market, spectrum is traded from a primary user (PU) to secondary users (SUs) in a relatively large time scale to reduce signaling overhead. Then, driven by dynamic traffic demands, SUs set up the Tier-2 market to redistribute channels among themselves in a small time scale. More specifically, we use a Nash bargain game to model the spectrum acquisition of SUs in the Tier-1 market and derive the equilibrium prices. We then employ a strategic bargain game to study the spectrum redistribution in the Tier-2 market, where SUs can exchange channels with low overhead through random matching, bilateral bargain, and the predetermined market equilibrium price. We investigate how various factors, such as the availability of channels and bargain partners, matching strategies, and traffic dynamics, affect the market relationships. This work provides new understanding on the spectrum market and valuable guidelines to primary and secondary network operators.

Delay-sensitive and multimedia communication in cognitive radio sensor networks

Multimedia and delay-sensitive data applications in cognitive radio sensor networks (CRSN) require efficient real-time communication and dynamic spectrum access (DSA) capabilities. This requirement poses emerging problems to be addressed in inherently resource-constrained sensor networks, and needs investigation of CRSN challenges with real-time communication requirements. In this paper, the main design challenges and principles for multimedia and delay-sensitive data transport in CRSN are introduced. The existing transport protocols and algorithms devised for cognitive radio ad hoc networks and wireless sensor networks (WSN) are explored from the perspective of CRSN paradigm. Specifically, the challenges for real-time transport in CRSN are investigated in different spectrum environments of smart grid, e.g., 500kV substation, main power room and underground network transformer vaults. Open research issues for the realization of energy-efficient and real-time transport in CRSN are also presented. Overall, the performance evaluations provide valuable insights about real-time transport in CRSN and guide design decisions and trade-offs for CRSN applications in smart electric power grid.

MAC Protocol for Opportunistic Cognitive Radio Networks with Soft Guarantees

Cognitive radio (CR) is the key enabling technology for an efficient dynamic spectrum access. It aims at exploiting an underutilized licensed spectrum by enabling opportunistic communications for unlicensed users. In this work, we first develop a distributed cognitive radio MAC (COMAC) protocol that enables unlicensed users to dynamically utilize the spectrum while limiting the interference on primary (PR) users. The main novelty in COMAC lies in not assuming a predefined CR-to-PR power mask and not requiring active coordination with PR users. COMAC provides a statistical performance guarantee for PR users by limiting the fraction of the time during which the PR users' reception is negatively affected by CR transmissions. To provide such a guarantee, we develop probabilistic models for the PR-to-PR and the PR-to-CR interference under a Rayleigh fading channel model. From these models, we derive closed-form expressions for the mean and variance of interference. Empirical results show that the distribution of the interference is approximately lognormal. Based on the developed interference models, we derive a closed-form expression for the maximum allowable power for a CR transmission. We extend the min-hop routing to exploit the available channel information for improving the perceived throughput. Our simulation results indicate that COMAC satisfies its target soft guarantees under different traffic loads and arbitrary user deployment scenarios. Results also show that exploiting the available channel information for the routing decisions can improve the end-to-end throughput of the CR network (CRN).

Dynamic Spectrum Access to the Combined Resource of Commercial and Public Safety Bands Based on a WCDMA Shared Network

The public safety spectrum is generally under-utilized due to the unique traffic characteristics of bursty and mission critical. This letter considers the application of dynamic spectrum access (DSA) to the combined spectrum of public safety (PS) and commercial (CMR) users in a common shared network that can provide both PS and CMR services. Our scenario includes the 700 MHz Public/Private Partnership which was recently issued by the Federal Communications Commission. We first propose an efficient DSA mechanism to coordinate the combined spectrum, and then establish a call admission control that reflects the proposed DSA in a wideband code division multiple access based network. The essentials of our proposed DSA are opportunistic access to the public safety spectrum and priority access to the commercial spectrum. Simulation results show that these schemes are well harmonized in various network environments.