Weijia Han

Efficient Soft Decision Fusion Rule in Cooperative Spectrum Sensing

In cognitive radio (CR), the soft decision fusion (SDF) rule plays a critical role in cooperative spectrum sensing (CSS). However, the computational cost on obtaining efficient SDF rule becomes infeasible even with a small number of cooperative users. In this paper, the efficiency of SDF rule in inhomogeneous background is studied from the perspective of quantization theory. We formulate the calculation of sensing performance including the probabilities of detection and false alarm when regarding both i) the quantization impact and ii) the inhomogeneous background, and then conclude a condition under which the sensing performance can be calculated by the fast Fourier transform (FFT). Based on this condition, two novel quantization schemes with two optimization methods are proposed to guarantee both the quantizer and decision threshold of SDF rule can be obtained efficiently, at the same time, the SDF can achieve high sensing performance.

Spatial False Alarms in Cognitive Radio

This letter introduces an observed spectrum sensing issue that: with certain probability, a secondary user (SU) misinterprets a non-interfered primary user (PU) is being interfered, resulting in access opportunity loss. This issue termed as spatial false alarm (SFA) problem is one of determinants on SUs medium access probability. However, it is neglected in current research related to spectrum sensing. By thoroughly study, the principle of SFA is presented to reveal the cause of this problem. Moreover, we quantify the impact of SFA by a closed-form expression to correctly evaluate the medium access probability for SU. The simulation results evidence both the importance of SFA and the validity of the derived medium access probability.

Spatial False Alarm in Cognitive Radio Network

In cognitive radio, secondary user (SU) performs spectrum sensing with a certain sensing range. This sensing range has a similar definition as the carrier sensing range of IEEE 802.11 MAC. It is widely considered that a SU is permitted to utilize the primary channel if no primary user (PU) transmits data inside its sensing range. However, it is observed that a busy PU outside the sensing range still can be detected by SU. As a result, the SU misinterprets that this busy PU is inside its sensing range, and hereby loses opportunity to utilize the primary channel. This new sensing issue is termed as spatial false alarm (SFA) problem. In this paper, we show the SFA can obviously reduce the SUs sensing performance, and cause the probabilities of false alarm and detection evaluated in current research have a great deviation with the actual results. To solve this SFA issue, the principle of SFA is studied thoroughly: i) this paper reveals the cause of this sensing problem, and presents the related approaches to ameliorate its negative impact; ii) this paper provides a reliable performance evaluation method to allow the SU to be aware of the real sensing results rather than the current works. Furthermore, with regarding the SFA, this paper derives the general upper and lower bounds of SUs sensing performance to have an insight into various sensing applications.

Orthogonal Power Division Multiple Access: A Green Communication Perspective

In cellular networks, since Media Access Control (MAC) layer plays a key role in every access equipment, it fascinates that little progress on multiple access protocol could save considerable energy. Accordingly, this paper studies a novel MAC protocol, i.e., the power division multiple access (PDMA) protocol, with the purpose of green communication. As a fundamental study of PDMA, we first propose a power division multiplexing (PDM) scheme, analogous to the time division multiplexing and frequency division multiplexing. It is proved that the transmit power could be divided into multiple regular power segments (PSs) to simultaneously transmit multiple independent information/data streams in peer to peer communications. Based on our fundamental studies of PDM, an orthogonal PDMA (OPDMA) protocol is proposed to utilize multiplexing and degraded channel gains for energy saving. By adopting the orthogonal PSs proposed in OPDMA, multiple information streams in different channels could be transmitted efficiently and concurrently with quality of service guarantee. This paper shows that the proposed OPDMA not only has low computational complexity as the conventional Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) protocols but also gains better energy efficiency, which consists with the energy saving requirement in green communications.

Correlation-Based Spectrum Sensing With Oversampling in Cognitive Radio

In wireless communication, the amplitude and phase of the transmitted signal have certain patterns during one symbol duration, which introduces high correlation among the samples obtained by oversampling at the receiver. In this work, we aim to explore such correlation information for cognitive radios to enhance the performance of spectrum sensing. By jointly considering the signal modulation, multipath fading, and oversampling rate, we derive the distribution of the empirical autocorrelation function for the obtained samples, on which we propose two efficient spectrum-sensing algorithms, and then analyze their performance. Our theoretical results reveal that the proposed algorithms with oversampling perform strictly better than the conventional energy detection scheme, while requiring the same level of prior information. Finally, we show through simulations that the derived statistical characteristics approximate the true statistical distribution of the autocorrelation function well, and the proposed sensing algorithms significantly improve the sensing performance compared to several existing sensing schemes.

Video delivery in heterogenous crans: architectures and strategies

Video traffic has become a major part of mobile data traffic, and will keep growing in the coming years. The performance of video delivery is fundamentally constrained by the structure of the underlying wireless networks. The recently proposed heterogeneous cloud access networks have been widely recognized as an inevitable evolution trend of the current cellular system toward the future 5G system, where multiple hybrid radio access technologies coexist to provide flexible access for mobile users. As a key enabling functional block for high-performance video delivery, a powerful centralized baseband processing unit pool is adopted to control all the radio access technologies, and possibly facilitate the video encoding and transmission, which opens up the potential to achieve higher throughput, lower traffic delay, and greater robustness compared to its basic baseband processing unit counterpart without central control functions. However, such a centralized control framework also raises many new research challenges to be addressed. In this article, we provide an overview of the state-of-the-art video delivery architectures and video packet transmission strategies in heterogeneous cloud radio access networks, with highlights on the networking architectures, transmission strategies, performance analysis, and design challenges. This article also sheds some light on the design principles for future big-data-oriented wireless networks.

Power Versus Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester-based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary versus secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem.

Energy-optimal partial computation offloading using dynamic voltage scaling

The incorporation of dynamic voltage scaling (DVS) technology into computation offloading could further reduce energy consumption of smart mobile device (SMD), which has not been fully studied. In this paper, this issue is formulated with jointly considering computational speed of SMD, transmit power of SMD, and the partial offloading, resulting in a nonconvex problem. To circumvent such a problem, we firstly study the thresholding when the offloading decision could always obtain positive gain. Then to yield more gains from offloading, we propose an alternating algorithm, which converges to a locally optimal solution fast. Extensive simulations show that the proposed algorithm not only significantly reduces energy consumption but also ensures more stringent latency against the existing schemes.

Spectrum sensing method without the impact of noise uncertainty

In cognitive radio, the spectrum sensing plays a key role in determining the performance of both the primary and the secondary networks. The eigenvalue based detection (EbD) algorithm has received broad attentions, since it shows significant robustness to the noise uncertainty problem. However, in EbD algorithm, it is quite difficult to obtain the distribution for the eigenvalues of the statistic covariance matrix, causing the false alarm or detection probability fail to be controlled efficiently. To this end, in this paper, we reinvestigate the eigenvalues from the perspective of the circulant matrix and propose a power spectrum density (PSD) based detection (PbD) algorithm. The proposed algorithm not only shares the same principle as the EbD algorithm, but also its error probability can be calculated efficiently. The closed-form expressions for the sensing threshold and the detection probability of our PdD algorithm are also provided. Simulation results validate our theoretical analysis well.

Sensing Statistical Primary Network Patterns via Bayesian Network Structure Learning

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users, as a large number of applications demand for more comprehensive knowledge on primary network behaviors in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship among primary nodes by introducing a Bayesian network (BN)-based framework. How to efficiently learn such a BN structure is a long-standing issue that is not fully understood even in the statistical learning community. To address such an issue in CR, this paper proposes a BN structure learning scheme consisting of a concise directional dependence checking function and a regular BN graph, which achieves significantly lower computational complexity compared with existing approaches. With this result, cognitive users could efficiently understand the statistical behavior patterns in the primary networks, such that more efficient cognitive protocols could be designed across different network layers.