Chun-Hao Liu

Primary User Traffic Estimation for Dynamic Spectrum Access

This paper presents a mathematical analysis of the accuracy of estimating Primary Users (PUs) mean duty cycle u, as well as the mean off- and on-times, where the estimation accuracy is expressed in terms of the Cramer-Rao bound on the mean squared estimation error. For estimating u, we derive the mean squared estimation error for uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood (ML) estimation. The estimation accuracy of the mean PU off- and on-times is studied when ML estimation is employed. Besides, the impact of spectrum sensing errors on the estimation accuracy is studied analytically for the averaging estimators, while simulation results are used for the ML estimators. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions.

Traffic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks

In this paper we present new results on the problem of finding the best channel sensing order for multi-channel Dynamic Spectrum Access (DSA) networks. We start with the general assumption that all Secondary Users (SUs) cooperatively sense each Primary User (PU) channel at one time. Then, the SU sensing results are reported to a DSA base station that schedules SU transmissions in order to maximize DSA network throughput. We then assume that PU traffic parameters are not perfectly known to DSA network and change over time, and propose a novel PU channel sensing order scheme based on the quality of PU traffic estimation. We adopt a maximum likelihood estimator to estimate the traffic statistics of PU channels and derive the Cramer-Rao (CR) bounds for the PU traffic estimation performance. Based on the CR bound and its Gaussian approximation, we analyze the impact of the estimation error on the DSA network throughput by computing a new metric called sensing order confidence, i.e., the probability that the best selected sensing order is not affected by PU traffic estimation errors. Finally, we formulate a convex optimization problem to determine the minimum number of PU channel state samples required for estimating PU traffic parameters after determining a certain constraint on the sensing order confidence metric to achieve the best sensing order.

Prediction of Erlang-2 Distributed Primary User Traffic for Dynamic Spectrum Access

In this letter, we propose prediction of primary user spectrum activity by constructing a continuous-time Markov chain model for Erlang-2 distributed on/off channel utilization intervals. Moreover, we propose a maximum-likelihood estimator for traffic parameters utilized by the predictor. Finally, we analyze the prediction confidence as the probability of the prediction performance that is not affected by traffic estimation errors. The prediction confidence is quantified by exploiting the proposed prediction region analysis.

Primary User Traffic Classification in Dynamic Spectrum Access Networks

This paper focuses on analytical studies of the primary user (PU) traffic classification problem. colorblack{Observing} that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.

An O(qlogq) log-domain decoder for non-binary LDPC over GF(q)

This paper presents a log-domain decoder for non-binary LDPC over GF(q). Comparing with the conventional O(q2) decoders, the proposed decoder can efficiently reduce the decoding complexity to O(qlogq) with only negligible degradation in BER. Comparisons on both simulated BER performance and computational complexity between the proposed and existing log-domain decoders are also provided.

Robust Cooperative Spectrum Sensing Scheduling Optimization in Multi-Channel Dynamic Spectrum Access Networks

Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on, we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity.

Cooperative spectrum sensing scheduling optimization in multi-channel dynamic spectrum access networks

Dynamic spectrum access (DSA) for secondary networks improves the spectrum utilization by finding spectrum opportunities and exploiting them efficiently. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, thus increasing the transmission throughput. However, in a multi-channel system, the problem becomes more complex since a sensing schedule, indicating to each user the channel that it must sense at different sensing moments, must be created to optimize system performance. In this paper, we first propose a general sensing strategy to schedule the users according to network parameters. We propose three sensing strategies, and within each one of them several solutions striking a balance between throughput performance, memory usage, and computational complexity are proposed. In addition, we show that the proposed sequential sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, the parallel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel sensing strategy achieves the best performance in all scenarios at the cost of extra complexity.

Primary user traffic classification in dynamic spectrum access networks

We propose a primary user (PU) traffic distribution classifier for dynamic spectrum access networks based on multi-hypothesis sequential probability ratio test (MSPRT). In specific, we propose two classifiers: (i) an estimate-then-classify classifier, and (ii) a modified MSPRT classifier based on the average likelihood function considering partial knowledge of the PU traffic parameters. Using the sequential algorithm, we show that our proposed classifiers can achieve higher classification performance compared to the traditional maximum likelihood classifier using constant number of samples.

Blind estimation of primary user traffic parameters under sensing errors

In this work we investigate the bounds on the estimation accuracy of Primary User (PU) traffic parameters with exponentially distributed busy and idle times. We derive closed-form expressions for the Cramér-Rao bounds on the mean squared estimation error for the blind joint estimation of the PU traffic parameters, specifically, the duty cycle, and the mean arrival and departure rates. Moreover, we present the corresponding maximum-likelihood estimators for the traffic parameters and discuss the effect of sensing errors in the joint estimation of PU traffic.

Joint ICI cancellation and channel estimation with real-time channel adaptation for high-mobility OFDM systems

Orthogonal frequency division multiplexing systems suffer severely from several highly time-variant channel effects under conditions of high-mobility. To cope with this issue, we propose a low-complexity receiver architecture utilizing interference cancellation followed by a real-time channel parameter extraction for channel estimation. The estimated parameters for the delay spread in power delay profiles can be tracked iteratively using an adaptive filter, according to the current received signal and channel information. The numerical simulations show that this approach achieves system performance closely to the ideal channel correlation. With the real testbed field measured channel models on Taiwan high speed rail, we proved a significant gain improvement (2–8 dB at SNR of 30 dB) under scenarios of high-mobility.