Dongliang Duan

Cooperative Diversity of Spectrum Sensing for Cognitive Radio Systems

Spectrum sensing is a crucial issue in cognitive radio networks. To improve the sensing performance, cooperation among the secondary users can be utilized to collect space diversity. In this paper, we quantify the diversity order for various cooperative spectrum sensing strategies. We first notice that, in a spectrum sensing problem, diversity should reflect both the false alarm and missed detection behaviors since they respectively capture the efficiency and reliability of the overall cognitive system. Hence, we consider both false alarm and missed detection probabilities individually and jointly via the average error probability. With the knowledge of both the noise strength and the primary user signal strength, the threshold of the Neyman-Pearson detector can be varied to control the system performance in desirable ways. Interesting tradeoffs between system efficiency and reliability are found in various cooperative strategies and analytical results are presented to guide practical system designs with different preferences.

Relay Selection from a Battery Energy Efficiency Perspective

Relay network is proposed as a means of improving the error performance and energy efficiency of the wireless networks. Inspired by the battery power efficiency (BPE) analysis in [1], relay selection from the battery energy efficiency perspective turns out to be an interesting topic. Under the general network setup which integrates typical transmission and reception modules, we adopt the realistic nonlinear battery model, extend the BPE results in [1] to relay network over Rayleigh fading channels, investigate the optimum and suboptimum energy allocation solutions for the relaying transmission, and finally establish the relay selection criterion from the battery energy efficiency point of view. Both the theoretical derivation and numerical results show that, subject to the same average bit error rate (BER) performance constraint, relaying does not necessarily increase the system energy efficiency, but is a possible alternative of the direct transmission depending on the relaying situation. Additionally, we prove that the two relaying protocols Decode and Forward (DF) and Amplify and Forward (AF) are equivalent for relay selection over Rayleigh fading channels at high SNR.

Modulation Selection from a Battery Power Efficiency Perspective

In this paper, we compare the battery power efficiencies of various pulse-based modulations widely adopted for their low complexity. Taking into account circuit modules and battery imperfectness, we establish simple closed-form analytical formulas which can be used to conveniently determine the relative preference between arbitrary pulse-based modulation pairs in terms of their actual average battery energy consumption.

Relay selection from a battery energy efficiency perspective

Relay network is proposed as a means of improving the error performance and energy efficiency of the wireless networks. Inspired by the battery power efficiency (BPE) analysis in [1], relay selection from the battery energy efficiency perspective turns out to be an interesting topic. Under the general network setup which integrates typical transmission and reception modules, we adopt the realistic nonlinear battery model, extend the BPE results in [1] to relay network over Rayleigh fading channels, investigate the optimum and suboptimum energy allocation solutions for the relaying transmission, and finally establish the relay selection criterion from the battery energy efficiency point of view. Both the theoretical derivation and numerical results show that, subject to the same average bit error rate (BER) performance constraint, relaying does not necessarily increase the system energy efficiency, but is a possible alternative of the direct transmission depending on the relaying situation. Additionally, we prove that the two relaying protocols Decode and Forward (DF) and Amplify and Forward (AF) are equivalent for relay selection over Rayleigh fading channels at high SNR.

Cooperative Spectrum Sensing with Ternary Local Decisions

By collecting diversity among multiple sensing users, cooperative spectrum sensing can overcome the channel fading effect. Usually, only the local binary decisions are available for sensing cooperation due to the limited channel bandwidth. However, in our previous work, we have shown that this strategy will either compromise diversity or signal-to-noise ratio (SNR) gain. In this paper, we will study cooperative sensing with ternary local decisions. Compared with the binary cooperative sensing, this strategy can reclaim diversity and recover the SNR loss by appropriate threshold selection.

Phasor state estimation from PMU measurements with bad data

The phasor measurement units (PMU) are expected to enhance state estimation in the power grid by providing accurate and timely measurements. However, due to communication errors and equipment failures, some detrimental data can occur among the measurements. The largest residual removal (LRR) algorithm is commonly used for phasor state estimation with bad data. Here, we show that this method cannot guarantee correctness unless data redundancy is very abundant. We then establish the equivalence between the approaches of bad data removal and bad data estimation and subtraction. In addition, we propose two new algorithms by exploiting the sparsity of the bad data. All algorithms are tested by simulations and our projection and minimization (PM) algorithm provides the best performance.

QoS-Oriented Wireless Routing for Smart Meter Data Collection: Stochastic Learning on Graph

To ensure resilient and reliable meter data collection that is essential for the smart grid operation, we propose a QoS-oriented wireless routing scheme. Specifically tailored for the heterogeneity of the meter data traffic in the smart grid, we first design a novel utility function that not only jointly accounts for system throughput and transmission latency, but also allows for flexible tradeoff between the two with a strict transmission latency constraint, as desired by various smart meter applications. Then, we model the interactions among smart meter data concentrators as a mixed-strategy network formation game. To avoid potential information exchange which is not always practical in meter data collection scenario, a stochastic reinforcement learning algorithm with only private and incomplete information is proposed to solve the network formation problem. Such a problem formulation, together with our proposed stochastic learning algorithm on graph, results in a steady probabilistic route. Both contributions are novel and unique in comparison with existing work on this topic. Another distinct feature of our approach is its capability of effectively maintaining the QoS of smart meter data collection, even when the network is under fault or attack, as verified by simulations.

Signaling With Imperfect Channel State Information: A Battery Power Efficiency Comparison

Due to complexity considerations, pulse-based modulations such as pulse position modulation (PPM) and on-off keying (OOK) are well suited for wireless sensor networks (WSNs) since they provide the possibility of carrier-less signaling and, more importantly, noncoherent reception bypassing channel estimation at the receiver. In this paper, we compare PPM and OOK in terms of their battery power efficiencies, using a nonlinear battery model, and under the same bandwidth occupancy, bandwidth efficiency and cutoff rate requirement. Cutoff rate is used as our comparison criterion because it leads to a tractable analysis that is often impossible through direct evaluation of random coding exponent or capacity. In addition, cutoff rate gives a universal expression for both coherent and noncoherent detection by simply setting various channel state information (CSI) qualities. Our system model integrates typical WSN transmission and reception modules with realistic nonlinear battery models. Circuit power consumption, dc-dc converter efficiency, and power amplifier efficiency are also taken into consideration. Our analytical results characterize the signal-to-noise ratio (SNR)-cutoff rate-CSI region where PPM is more power efficient than OOK, and vice versa. We provide an interpretation in terms of the transmission range and symbol set size. Numerical results are also provided to verify the analysis.

A new DFT-based frequency estimator for single-tone complex sinusoidal signals

Frequency estimation for single-tone complex sinusoidal signals under additive white Gaussian noise is a classical and fundamental problem in many applications, such as communications, radar, sonar and power systems. In this paper, we propose a new algorithm by interpolating discrete Fourier transform (DFT) samples. Different from other existing interpolation methods for frequency estimation, our algorithm is based on a much simpler expression and has mathematically tractable bias expression in closed form, which can potentially assist future bias correction. Simulations confirm that our proposed algorithm outperforms all existing alternatives in the literature with comparable complexity.

The optimal fusion rule for cooperative spectrum sensing from a diversity perspective

Cooperation among secondary users can greatly improve the spectrum sensing performance in cognitive radio. In our previous work [1], we proposed the diversity as a measure of the cooperative gain and developed cooperative spectrum sensing schemes based on some pre-defined local threshold selections. In this paper, on the other hand, we start the cooperative spectrum sensing development from the fusion rule selection. The detailed analysis of two relatively simple and mathematically tractable rules, namely the AND and the OR rules, is provided in this paper. Interestingly, it is found that the AND rule results in the same performance as the non-cooperative sensing strategy and thus cannot achieve diversity gain at all. On the other hand, it is shown that when SNR is large, the OR rule is optimum according to the large deviation analysis [2] and proved to achieve the maximum diversity gain with better performance than our previous sensing scheme developed in [1]. Based on the fixed decision rule, we find the optimal local sensing threshold accordingly. Numerical results are provided to verify our analyses.