Hyoil Kim

Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks

Sensing/monitoring of spectrum-availability has been identified as a key requirement for dynamic spectrum allocation in cognitive radio networks (CRNs). An important issue associated with MAC-layer sensing in CRNs is how often to sense the availability of licensed channels and in which order to sense those channels. To resolve this issue, we address (1) how to maximize the discovery of spectrum opportunities by sensing-period adaptation and (2) how to minimize the delay in finding an available channel. Specifically, we develop a sensing-period optimization mechanism and an optimal channel-sequencing algorithm, as well as an environment- adaptive channel-usage pattern estimation method. Our simulation results demonstrate the efficacy of the proposed schemes and its significant performance improvement over nonoptimal schemes. The sensing-period optimization discovers more than 98 percent of the analytical maximum of discoverable spectrum-opportunities, regardless of the number of channels sensed. For the scenarios tested, the proposed scheme is shown to discover up to 22 percent more opportunities than nonoptimal schemes, which may become even greater with a proper choice of initial sensing periods. The idle-channel discovery delay with the optimal channel-sequencing technique ranges from 0.08 to 0.35 seconds under the tested scenarios, which is much faster than nonoptimal schemes. Moreover, our estimation method is shown to track time-varying channel-parameters accurately.

In-band spectrum sensing in cognitive radio networks: energy detection or feature detection?

In a cognitive radio network (CRN), in-band spectrum sensing is essential for the protection of legacy spectrum users, with which the presence of primary users (PUs) can be detected promptly, allowing secondary users (SUs) to vacate the channels immediately. For in-band sensing, it is important to meet the detectability requirements, such as the maximum allowed latency of detection (e.g., 2 seconds in IEEE 802.22) and the probability of mis-detection and false-alarm. In this paper, we propose an effcient periodic in-band sensing algorithm that optimizes sensing-frequency and sensing-time by minimizing sensing overhead while meeting the detectability requirements. The proposed scheme determines the better of energy or feature detection that incurs less sensing overhead at each SNR level, and derives the threshold aRSSthreshold on the average received signal strength (RSS) of a primary signal below which feature detection is preferred. We showed that energy detection under lognormal shadowing could still perform well at the average SNR < SNRwall [1] when collaborative sensing is used for its location diversity. Two key factors affecting detection performance are also considered: noise uncertainty and inter-CRN interference. aRSSthreshold appears to lie between -114.6 dBm and -109.9 dBm with the noise uncertainty ranging from 0.5 dB to 2 dB, and between -112.9 dBm and -110.5 dBm with 1~6 interfering CRNs.

Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks

We address the problem of rapidly discovering spectrum opportunities for seamless service provisioning for secondary users (SUs) in cognitive radio networks (CRNs). Specifically, we propose an efficient sensing-sequence that incurs a small opportunity-discovery delay by considering (1) the probability that a spectrum band (or a channel) may be available at the time of sensing, (2) the duration of sensing on a channel, and (3) the channel capacity. We derive the optimal sensing-sequence for channels with homogeneous capacities, and a suboptimal sequence for channels with heterogeneous capacities for which the problem of finding the optimal sensing-sequence is shown to be NP-hard. To support the proposed sensing-sequence, we also propose a channel-management strategy that optimally selects and updates the list of backup channels. A hybrid of maximum likelihood (ML) and Bayesian inference is also introduced for flexible estimation of ON/OFF channel-usage patterns and prediction of channel availability when sensing produces infrequent samples. The proposed schemes are evaluated via in-depth simulation. For the scenarios we considered, the proposed suboptimal sequence is shown to achieve close-to-optimal performance, reducing the opportunity-discovery delay by up to 47% over an existing probability-based sequence. The hybrid estimation strategy is also shown to outperform the ML-only strategy by reducing the overall opportunity-discovery delay by up to 34%.

Cognitive radios for dynamic spectrum access: from concept to reality

This article provides a comprehensive survey of cognitive radio technology, focusing on its application to dynamic spectrum access, especially from the perspective of realizing consumer- oriented CR networks. We first overview the state of the art in CR technology and identify its key functions across the protocol stack, such as spectrum sensing, resource allocation, CR MAC protocol, spectrum-aware opportunistic routing, CR transport protocol, QoS awareness, spectrum trading, and security. We also review the various schemes proposed for each of these functions and discuss the suitability, advantages, and limitations of their usage in the future CR market. Finally, we introduce the activities in CR research communities and industry in terms of development of real-life applications, such as IEEE 802.22, Ecma 392, and IEEE 802.11af (also known as Wi-Fi 2.0 or White-Fi), and then identify necessary steps for future CR applications.

In-Band Spectrum Sensing in IEEE 802.22 WRANs for Incumbent Protection

In cognitive radios, in-band spectrum sensing is essential for the protection of legacy spectrum users, enabling secondary users to vacate channels immediately upon detection of primary users. For in-band sensing, it is important to meet detectability requirements, such as the maximum allowed detection latency and the probability of misdetection and false alarm. In this paper, we propose key techniques for efficient in-band sensing. We first advocate the use of clustered sensor networks, and propose a periodic in-band sensing algorithm that optimizes sensing period and sensing time to meet the detectability requirements while minimizing sensing overhead. The scheme also determines the better of energy or feature detection incurring less sensing overhead at each SNR level, and derives the threshold aRSSthreshold on the average received signal strength of a primary signal above which energy detection is preferred to feature detection. We consider two key factors affecting aRSSthreshold noise uncertainty and inter-CRN interference. aRSSthreshold appears to lie between -114.6 dBm and -109.9 dBm with noise uncertainty ranging from 0.5 dB to 2 dB, and between -112.9 dBm and -110.5 dBm with 1-6 interfering CRNs. We also investigate how strict the detection requirement must be for efficient reuse of idle channels without incurring unnecessary channel switches due to false detection of primaries.

Wi-Fi could be much more

Wi-Fi has become an essential wireless technology in our daily lives, although the original intention of its introduction was to replace Ethernet cable. In this article, we outline the most remarkable features introduced during its ongoing technological evolution in terms of three major directions: throughput enhancement, longrange extension, and greater ease of use. By stitching these advanced features together, we also envision a promising future that Wi-Fi technology will bring us in terms of spectrum heterogeneity, seamless service provisioning, and possible relations with cellular networks.

Wi-Fi 2.0: Price and quality competitions of duopoly cognitive radio wireless service providers with time-varying spectrum availability

The whitespaces (WS) in the legacy spectrum provide new opportunities for the future Wi-Fi-like Internet access, often called Wi-Fi 2.0, since service quality can be greatly enhanced thanks to the better propagation characteristics of the WS than the ISM bands. In the Wi-Fi 2.0 networks, each wireless service provider (WSP) temporarily leases a licensed spectrum band from the licensees and opportunistically utilizes it during the absence of the legacy users. The WSPs in Wi-Fi 2.0 thus face unique challenges since spectrum availability of the leased channel is time-varying due to the ON/OFF spectrum usage patterns of the legacy users, which necessitates the eviction control of in-service customers at the return of legacy users. As a result, to maximize its profit, a WSP should consider both channel leasing and eviction costs to optimally determine a spectrum band to lease and a service tariff. In this paper, we consider a duopoly Wi-Fi 2.0 market where two co-located WSPs compete for the spectrum and customers. The competition between the WSPs is analyzed using game theory to derive the Nash Equilibria (NE) of the price (of the service tariffs) and the quality (of the leased channel, in terms of channel utilization) competitions. The NE existence condition and market entry barriers are also derived. Via an extensive numerical analysis, we show the tradeoffs between leasing/eviction cost, customer arrivals, and channel usage patterns by the legacy users.

Optimal Online Sensing Sequence in Multichannel Cognitive Radio Networks

We address the problem of rapidly discovering spectrum opportunities for seamless service provisioning in cognitive radio networks (CRNs). In particular, we focus on multichannel communications via channel-bonding with heterogeneous channel characteristics of ON/OFF patterns, sensing time, and channel capacity. Using dynamic programming (DP), we derive an optimal online sensing sequence incurring a minimal opportunity-discovery delay, and propose a suboptimal sequence that presents a near-optimal performance while incurring significantly less computational overhead than the DP algorithm. To facilitate fast opportunity discovery, we also propose a channel-management strategy that maintains a list of backup channels to be used at building the optimal sequence. A hybrid of maximum likelihood (ML) and Bayesian inference is introduced as well for flexible estimation of ON/OFF channel-usage patterns, which selectively chooses the better between the two according to the frequency of sensing and ON/OFF durations. The performance of the proposed schemes, in terms of the opportunity-discovery delay, is evaluated via in-depth simulation, and for the scenarios we considered, the proposed suboptimal sequence achieves a near-optimal performance with only an average of 0.5 percent difference from the optimal delay, and outperforms the previously proposed probabilistic scheme by up to 50.1 percent. In addition, the backup channel update scheme outperforms the no-update case by up to 49.9 percent.

Understanding Wi-Fi 2.0: from the economical perspective of wireless service providers [Dynamic Spectrum Management]

Wi-Fi 2.0 refers to Wi-Fi-like Internet access operating on whitespaces in the licensed spectrum using cognitive radio technology. Wi-Fi 2.0 is expected to provide better performance and larger coverage than todays Wi-Fi, thanks to the good propagation characteristics of the legacy spectrum such as TV bands. Wi-Fi 2.0 is modeled as a network consisting of an access point (called CR hotspot) and end-user terminals (CR devices) operated by a CR wireless service provider. In this article we focus on the economical perspective of Wi-Fi 2.0 and discuss various aspects in profit management of Wi-Fi 2.0 WSPs. In particular, we consider profit-maximizing optimal strategies in terms of customer admission/eviction control and inter-WSP market competition. We first show that Wi-Fi 2.0 operates on time-varying spectrum availability due to the ON-OFF channel usage of legacy users, and advocate the necessity of customer eviction control upon appearance of legacy users. We also identify two types of WSP-WSP market competition in leasing the limited spectrum resources from the licensees and in enticing end customers with a competitive price. Then we enumerate the key factors affecting the profit of collocated WSPs, such as channel leasing cost, service tariff, QoS provisioning, and coexistence with legacy services. By examining Wi-Fi 2.0 from an economic point of view, we show its commercial value in developing next-generation CR applications that benefit both legacy and CR users.

Asymmetry-Aware Real-Time Distributed Joint Resource Allocation in IEEE 802.22 WRANs

In IEEE 802.22 Wireless Regional Area Networks (WRANs), each Base Station (BS) solves a complex resource allocation problem of simultaneously determining the channel to reuse, power for adaptive coverage, and Consumer Premise Equipments (CPEs) to associate with, while maximizing the total downstream capacity of CPEs. Although joint power and channel allocation is a classical problem, resource allocation in WRANs faces two unique challenges that has not yet been addressed: (1) the presence of small-scale incumbents such as wireless microphones (WMs), and (2) asymmetric interference patterns between BSs using omnidirectional antennas and CPEs using directional antennas. In this paper, we capture this asymmetry in upstream/downstream communications to propose an accurate and realistic WRAN-WM coexistence model that increases spatial reuse of TV spectrum while protecting small-scale incumbents. Based on the proposed model, we formulate the resource allocation problem as a mixed-integer nonlinear programming (MINLP) which is NP-hard. To solve the problem in real-time, we propose a suboptimal algorithm based on the Genetic Algorithm (GA), and extend the basic GA algorithm to a fully-distributed GA algorithm (dGA) that distributes computational cost over the network and achieves scalability via local cooperation between neighboring BSs. Using extensive simulation, the proposed dGA is shown to perform as good as 99.4- 99.8% of the optimal solution, while reducing the computational cost significantly.