Alexander W. Min

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.

Improving energy efficiency of Wi-Fi sensing on smartphones

Mobile data usage over cellular networks has been dramatically increasing over the past years. Wi-Fi based wireless networks offer a high-bandwidth alternative for offloading such data traffic. However, intermittent connectivity, and battery power drain in mobile devices, inhibits always-on connectivity even in areas with good Wi-Fi coverage. This paper presents WiFisense, a system that employs user mobility information retrieved from low-power sensors (e.g., accelerometer) in smartphones, and further includes adaptive Wi-Fi sensing algorithms, to conserve battery power while improving Wi-Fi usage. We implement the proposed system in Android-based smartphones and evaluate the implementation in both indoor and outdoor Wi-Fi networks. Our evaluation results show that WiFisense saves energy consumption for scans by up to 79% and achieves considerable increase in Wi-Fi usage for various scenarios.

Secure Cooperative Sensing in IEEE 802.22 WRANs Using Shadow Fading Correlation

Cooperative (or distributed) sensing has been recognized as a viable means to enhance the incumbent signal detection by exploiting the diversity of sensors. However, it is challenging to secure such distributed sensing due mainly to the unique features of dynamic spectrum access networks-openness of low-layer protocol stacks in software-defined radio devices and the absence of interactions/coordination between primary and secondary devices. To meet this challenge, we propose an attack-tolerant distributed sensing protocol (ADSP) for DTV signal detection in IEEE 802.22 WRANs, under which sensors in close proximity are grouped as a cluster, and sensors within a cluster cooperate to safeguard the integrity of sensing. The heart of ADSP is a novel filter based on shadow-fading correlation, by which the fusion center cross-validates reports from the sensors to identify and penalize abnormal sensing reports. By realizing this correlation filter, ADSP significantly reduces the impact of an attack on the performance of distributed sensing, while incurring minimal processing and communication overheads. ADSP also guarantees the detectability requirements of 802.22 to be met even with the presence of sensing report manipulation attacks by scheduling sensing within the framework of sequential hypothesis testing. The efficacy of ADSP is validated on a realistic 2D shadow-fading field. Our extensive simulation-based study shows that ADSP reduces the false-alarm rate by 99.2 percent while achieving 97.4 percent of maximum achievable detection rate, and meets the detection requirements of IEEE 802.22 in various attack scenarios.

Attack-tolerant distributed sensing for dynamic spectrum access networks

Accurate sensing of the spectrum condition is of crucial importance to the mitigation of the spectrum scarcity problem in dynamic spectrum access (DSA) networks. Specifically, distributed sensing has been recognized as a viable means to enhance the incumbent signal detection by exploiting the diversity of sensors. However, it is challenging to make such distributed sensing secure due mainly to the unique features of DSA networks—openness of a low-layer protocol stack in SDR devices and non-existence of communications between primary and secondary devices. To address this challenge, we propose attack-tolerant distributed sensing protocol (ADSP), under which sensors in close proximity are grouped into a cluster, and sensors in a cluster cooperatively safeguard distributed sensing. The heart of ADSP is a novel shadow fading correlation-based filter tailored to anomaly detection, by which the fusion center prefilters abnormal sensor reports via cross-validation. By realizing this correlation filter, ADSP minimizes the impact of an attack on the performance of distributed sensing, while incurring minimal processing and communications overheads. The efficacy of our scheme is validated on a realistic two-dimensional shadow-fading field, which accurately approximates real-world shadowing environments. Our extensive simulation-based evaluation shows that ADSP significantly reduces the impact of attacks on incumbent detection performance.

Attack Prevention for Collaborative Spectrum Sensing in Cognitive Radio Networks

Collaborative spectrum sensing is vulnerable to data falsification attacks, where malicious secondary users (attackers) submit manipulated sensing reports to mislead the fusion centers decision on spectrum occupancy. This paper considers a challenging attack scenario, where multiple attackers cooperatively maximize their aggregate spectrum utilization. Without attack-prevention mechanisms, we show that honest secondary users (SUs) are unable to opportunistically transmit over the licensed spectrum and may even get penalized for collisions caused by attackers. To prevent such attacks, we propose two attack-prevention mechanisms with direct and indirect punishments. Our key idea is to identify collisions with the primary user (PU) that should not happen if all SUs follow the fusion centers decision. Unlike prior work, the proposed simple mechanisms do not require the fusion center to identify and exclude attackers. The direct punishment can effectively prevent all attackers from behaving maliciously. The indirect punishment is easier to implement and can prevent attacks when the attackers care enough about their long-term reward.

Detection of Small-Scale Primary Users in Cognitive Radio Networks

In cognitive radio networks (CRNs), detecting small-scale primary devices, such as wireless microphones, is a challenging, but very important, problem that has not yet been addressed well. Recently, cooperative sensing and sensing scheduling have been advocated as an effective MAC (medium access control) layer approach to detecting large-scale primary signals. However, it is unclear whether and how they can improve the detection of a small-scale primary signal because of (i) its small signal footprint due to the use of weak transmit-power, and (ii) the unpredictability of its spatial and temporal spectrum-usage patterns. Based on extensive analysis and simulation, we identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary signals. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primarys location and transmit-power. Based on these observations, we propose an efficient sensing framework that jointly performs Detection, LOCation estimation, and transmit-power estimation (DeLOC) for small-scale primary users. Our extensive evaluation results in a realistic CRN environment show that DeLOC achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft. These findings provide useful insights and guidelines in designing a sensing scheme for detection of small-scale primaries in CRNs.

Robust cooperative sensing via state estimation in cognitive radio networks

Cooperative sensing, a key enabling technology for dynamic spectrum access, is vulnerable to various sensing-targeted attacks, such as the primary user emulation or spectrum sensing data falsification. These attacks can easily disrupt the primary signal detection process, thus crippling the operation of dynamic spectrum access. While such sensing-targeted attacks can be easily launched by an attacker, it is very challenging to design a robust cooperative spectrum sensing scheme due mainly to the practical constraints inherent in spectrum sensing, particularly the shared/open nature of the wireless medium and the unpredictability of signal propagation. In this paper, we develop an efficient, yet simple attack detection framework, called IRIS (robust cooperatIve sensing via iteRatIve State estimation), that safeguards the incumbent detection process by checking the consistency among sensing reports via the estimation of system states, namely, the primary users transmit-power and path-loss exponent. The key insight behind the design of IRIS is that the sensing results are governed by the network topology and the law of signal propagation, which cannot be easily compromised by an attacker. Consequently, the sensing reports must demonstrate consistency among themselves in estimating system states. Our analytical and simulation results show that, by performing consistency-checks, IRIS provides high attack-detection capability, and preserves satisfactory performance in estimating the system states even under very challenging attack scenarios. Based on these observations, we propose a new incumbent detection rule that can further improve the spectrum efficiency. IRIS can be readily deployed in infrastructure-based cognitive radio networks, such as IEEE 802.22 WRANs, with manageable processing and communication overheads.

Opportunistic spectrum access for mobile cognitive radios

Cognitive radios (CRs) can mitigate the impending spectrum scarcity problem by utilizing their capability of accessing licensed spectrum bands opportunistically. While most existing work focuses on enabling such opportunistic spectrum access for stationary CRs, mobility is an important concern to secondary users (SUs) because future mobile devices are expected to incorporate CR functionality. In this paper, we identify and address three fundamental challenges encountered specifically by mobile SUs. First, we model channel availability experienced by a mobile SU as a two-state continuous-time Markov chain (CTMC) and verify its accuracy via in-depth simulation. Then, to protect primary/incumbent communications from SU interference, we introduce guard distance in the space domain and derive the optimal guard distance that maximizes the spatio-temporal spectrum opportunities available to mobile CRs. To facilitate efficient spectrum sharing, we formulate the problem of maximizing secondary network throughput within a convex optimization framework, and derive an optimal, distributed channel selection strategy. Our simulation results show that the proposed spectrum sensing and distributed channel access schemes improve network throughput and fairness significantly, and reduce SU energy consumption for spectrum sensing by up to 74%.

Improving energy efficiency for mobile platforms by exploiting low-power sleep states

Reducing energy consumption is one of the most important design aspects for small form-factor mobile platforms, such as smartphones and tablets. Despite its potential for power savings, optimally leveraging system low-power sleep states during active mobile workloads, such as video streaming and web browsing, has not been fully explored. One major challenge is to make intelligent power management decisions based on, among other things, accurate system idle duration prediction, which is difficult due to the non-deterministic system interrupt behavior. In this paper, we propose a novel framework, called E2S3 (Energy Efficient Sleep-State Selection), that dynamically enters the optimal low-power sleep state to minimize the system power consumption. In particular, E2S3 detects and exploits short idle durations during active mobile workloads by, (i) finding optimal thresholds (i.e., energy break-even times) for multiple low-power sleep states, (ii) predicting the sleep-state selection error probabilities heuristically, and by (iii) selecting the optimal sleep state based on the expected reward, e.g., power consumption, which incorporates the risks of making a wrong decision We implemented and evaluated E2S3 on Android-based smartphones, demonstrating the effectiveness of the algorithm. The evaluation results show that E2S3 significantly reduces the platform energy consumption, by up to 50% (hence extending battery life), without compromising system performance.

Spatio-Temporal Fusion for Small-scale Primary Detection in Cognitive Radio Networks

In cognitive radio networks (CRNs), detecting small-scale primary devices---such as wireless microphones (WMs)---is a challenging, but very important, problem that has not yet been addressed well. We identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary devices. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primarys location and transmit-power. Based on these observations, we propose an efficient sensing framework, called DeLOC, that iteratively performs location/transmit-power estimation and dynamic sensor selection for cooperative sensing. Our extensive simulation results in a realistic CRN environment show that DeLOC achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft.