Mort Naraghi-Pour

Multicell CDMA network design

Traditional design rules for cellular networks are not directly applicable to code division multiple access (CDMA) networks where intercell interference is not mitigated by cell placement and careful frequency planning. For transmission quality requirements, a minimum signal-to-interference ratio (SIR) must be achieved. The base-station location, its pilot-signal power (which determines the size of the cell), and the transmission power of the mobiles all affect the received SIR. In addition, because of the need for power control in CDMA networks, large cells can cause a lot of interference to adjacent small cells, posing another constraint to design. In order to maximize the network capacity associated with a design, we develop a methodology to calculate the sensitivity of capacity to base-station location, pilot-signal power, and transmission power of each mobile. To alleviate the problem caused by different cell sizes, we introduce the power compensation factor, by which the nominal power of the mobiles in every cell is adjusted. We then use the calculated sensitivities in an iterative algorithm to determine the optimal locations of the base stations, pilot-signal powers, and power compensation factors in order to maximize the capacity. We show examples of how networks using these design techniques provide higher capacity than those designed using traditional techniques.

Autocorrelation-Based Spectrum Sensing for Cognitive Radios

We propose a new spectrum-sensing technique based on the sample autocorrelation of the received signal. We assume that the received signal is oversampled and allow for frequency offset between the local oscillator and the carrier of the primary signal. We evaluate the performance of this algorithm for both additive white Gaussian noise (AWGN) and Rayleigh-fading channels and study its sensitivity to carrier frequency offset. Simulation results are presented to verify the accuracy of the approximation assumptions in our analysis. The performance of the proposed algorithm is also compared with those from the energy detector, the covariance detector, and the cyclic-autocorrelation detector. The results show that our algorithm outperforms the covariance detector and the cyclic autocorrelation detector. It also outperforms the energy detector in the presence of noise power uncertainty or in the case of unknown primary signal bandwidth. Finally, we investigate three diversity combining techniques, namely 1) equal gain combining, 2) selective combining and 3) equal gain correlation combining. Our simulations show that for detection probabilities of interest (e.g., > 0.9), a system with a four-branch diversity achieves a signal-to-noise ratio (SNR) gain of more than 5 dB over the no-diversity system that uses the same number of received signal samples.

A Novel Algorithm for Distributed Localization in Wireless Sensor Networks

We present a novel algorithm for localization of Wireless Sensor Networks (WSNs) called Distributed Randomized Gradient Descent (DRGD) and prove that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes. For noisy distance measurements, the convergence properties of DRGD are discussed and an error bound on the location estimation error is obtained. In contrast to several recently proposed methods, DRGD does not require that the blind nodes be contained in the convex hull of the anchor nodes, and it can accurately localize the network with only a few anchors. Performance of DRGD is evaluated through extensive simulations and compared with three other algorithms, namely, the relaxation-based Second-Order Cone Programming (SOCP), the Simulated Annealing (SA), and the Semi-Definite Programing (SDP). Similar to DRGD, SOCP and SA are distributed algorithms, whereas SDP is centralized. The results show that DRGD successfully localizes the nodes in all the cases, whereas in many cases SOCP and SA fail. Finally, we present a modification of DRGD for mobile WSNs and demonstrate the efficacy of DRGD for localization of mobile networks with several simulation results.

Blind Spectrum Sensing Using Antenna Arrays and Path Correlation

In this paper, we consider the problem of spectrum sensing in cognitive radios (CRs) when the receiver of the secondary user (SU) is equipped with a multiantenna system. Using an estimate of the cross correlation among the signals received at different antenna elements, we propose a blind detection method, which assumes no prior knowledge of the signaling scheme used by the PU, the noise power, or the channel path coefficients. The cross correlation among the received signals is a result of the correlation among the channel path coefficients from the primary user (PU) transmitter to different antenna elements of the secondary receiver. The detection and false alarm probabilities of the proposed algorithms are evaluated using an asymptotic analysis, and the results are compared with simulation results. It is shown that the proposed methods outperform several recently proposed blind-sensing techniques for CRs using multiple antennas.

A Comparison of Three Classes of Spectrum Sensing Techniques

Spectrum sensing is used to identify the (temporarily) unused (licensed) frequency bands and as such plays a key role in dynamic spectrum access. Spectrum sensing is currently being investigated by a number of researchers. In this paper we compare the performance of three classes of algorithms-energy detectors, autocorrelation detectors, and the cyclic autocorrelation detector. The focus of the study is on the trade-offs of the three approaches under fixed false alarm and detection probabilities.

A Survey of Traffic Issues in Machine-to-Machine Communications Over LTE

Machine-to-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-term evolution (LTE) and LTE-advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-to-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.

Blind Modulation Classification over Fading Channels Using Expectation-Maximization

We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].

Scalable PHY-Layer Security for Distributed Detection in Wireless Sensor Networks

The problem of binary hypothesis testing is considered in a bandwidth-constrained low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method we propose maps the output of the quantizer to one of the possible quantizer output levels randomly according to a probability matrix. This operation is similar to that of a discrete memoryless channel. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. A good suboptimal solution to this problem is found. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. As the number of levels in the quantizer increases the performance loss of the secure system compared to insecure system is reduced. Compared to the existing data encryption methods, the proposed method is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack.

Fast Detection of Malicious Behavior in Cooperative Spectrum Sensing

In this paper we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving nodes. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. In contrast to previous work we assume that the FC has no prior information about the radios in the network except that the honest radios are in majority. As shown in the paper this is required for any algorithm to uniquely identify the CRs. Another distinguishing feature is that our approach can classify the radios into more than just two classes of honest and malicious CRs. This applies in cases where the honest CRs have different detection and false alarm probabilities, which may arise when they employ different spectrum sensing techniques or encounter dissimilar channel and noise conditions. Another case is when the CRN includes more than one type of misbehaving CRs. Our numerical results show significant improvements over the widely popular reputation-based classifier (RBC). In particular, with only a few decisions from the CRs, the proposed algorithm can quickly and efficiently classify the CRs whereas the RBC method fails even for networks with a large number of CRs. In all of our numerical results the EM algorithm converged in five or fewer iterations resulting in fast convergence of the proposed method. This makes the proposed method a good candidate for implementation in CRNs. The numerical results are also compared with the Cramer-Rao lower bound and show a close match. Simulation results are also presented to demonstrate the efficacy of the proposed algorithm in the presence of correlated observations among the radios.

Computationally efficient resource allocation for multiuser OFDM systems

The optimal subcarrier, bit and power allocation problem for multiuser OFDM systems has been investigated. To achieve a numerically efficient solution the problem is divided into two separate optimization problems: one for subcarrier allocation and one for bit and power allocation. A novel and computationally efficient algorithm is presented for the bit and power allocation problem which results in a near-optimal solution. An efficient suboptimal solution is also presented for the subcarrier allocation problem. Numerical results are presented to show that the proposed methods achieve performance close to that of the optimal solution with considerably less computational complexity than those previously reported in the literature