Ali Jamshidi

Probabilistic spectrum sensing data falsification attack in cognitive radio networks

Abstract Cognitive radio has emerged as a solution to the problem of spectrum scarcity in recent years. Spectrum sensing is a key task in cognitive radio systems that can be performed collaboratively. Although collaboration enhances the performance of spectrum sensing, it can put the cognitive network in a vulnerable position. The cognitive users may send falsified reports, hence degrade the performance of spectrum sensing. In this paper, we propose a flexible structure which enables the attacker to reconfigure the attack parameters based on the defense strategy employed at the fusion center (FC) adaptively. In particular, we consider a cognitive network in which the cognitive users send their observations in a quantized format. It is assumed that a soft-decision-based defense strategy is employed at FC to detect the attackers. The attacker maps its quantized observations onto the other quantization levels probabilistically and reports them to FC. The attacker’s objective is to perform probabilistic mapping such that the performance of FC degrades as much as possible. At the same time, the attacker considers the attack costs. We demonstrate that the proposed attack method leads to a convex linear programming problem. A low complexity algorithm is proposed to solve the problem. Simulation results are presented to show the effectiveness of the proposed scheme.

Selective physical layer network coding in bidirectional relay channel

In this paper, the authors show that using physical layer network coding (PNC) in wireless relay systems does not always increase throughput performance, and to achieve maximum throughput, PNC should be exploited intelligently regarding to the channel conditions. In this study, they propose a selective PNC (SPNC) scheme in bidirectional relay systems for binary phase shift keying. In the proposed scheme, the relay chooses either to detect network-coded data from superimposed signal by PNC scheme or single source data by a so-called single node detection (SND) scheme, based on which offers more end-to-end throughput for current channel realisation. They analytically determine the region of channel state plane in which SPNC selects SND scheme. Also, they obtain expressions for instantaneous bit error rates (BERs) of PNC and SND protocols. Moreover, they derive upper bounds in closed-form for average BERs of PNC and SND schemes during multiple access phase at the relay. Then, they investigate SPNC with high-order modulations. Simulations and analytical results confirm that SPNC achieves considerable throughput gain over PNC in Rayleigh fading channels.

A new probabilistic classifier based on decomposable models with application to internet traffic

Abstract Probabilistic models are one of the common approaches for classification. One way to create these models is to form a decomposable model by selecting a set of marginal distributions. Although decomposable models are attractive by having some desirable properties, they would face the over-fitting issue in model-based classification approaches. Considering this issue, we propose a new method for selecting a set of marginal distributions and creating a proper decomposable model while controlling the complexity. The obtained model will be able to capture the interdependencies among different features and can be used for classification. The proposed method is compared with three existing methods namely TAN and Averaged TAN classifiers and t-Cherry algorithm by focusing on internet traffic data. Experimental results show that our obtained model can effectively extract dependencies among features, and hence, its performance as a classifier is superior compared to other three methods.

A new channel code for decreasing inter-symbol-interference in diffusion based molecular communications

Molecular communication is a new framework in data communication based on biological mechanisms and systems. In molecular communication system, the transmitter sends information by transmitting certain chemical molecules called data molecules and after propagation of the molecules in the environment they are collected and decoded by the receiver. Due to randomness in the movement of the molecules, they would arrive at the receiver out of order and cause inter-symbol interferences (ISI) problem. Channel coding techniques are commonly used in this situation to tackle the problem. In this paper, we introduce a new family of channel codes, called Crossover Resistant Coding with Time Gap (CRCTG), to reduce the effect of ISI in molecular communications. The performance of the proposed coding schemes are analyzed and compared by simulations with the existing techniques.

Joint power and admission control based on hybrid users in cognitive radio network

Abstract Hybrid users or primary-secondary users (PSUs) are advanced primary users that are able to access the secondary network using their cognitive functions. Hybrid users can appear as a new type of cognitive users that would be promising for future cognitive radio networks (CRNs). Regarding the unique capabilities of such users and due to the lack of considerable research on this subject, we study the problem of joint power and admission control in spectrum underlay CRN based on hybrid users. In order to fully take the advantages of cognitive capability, an advanced cognitive radio network (ACRN) is proposed by employing the PSUs, and the corresponding power and signal-to-interference-plus-noise ratio (SINR) are derived. Then, feasibility checking mechanism is investigated and the optimal value of interference temperature limit for PSUs is also obtained. A new formulation to maximize the number of admitted secondary users (SUs) in ACRN is presented. Moreover, two power and admission control algorithms are proposed which significantly improve the network performance not only in the number of admitted SUs but also in transmit power consumption. For a feasible network, the problem of aggregate throughput maximization is solved using successive geometric programming. Afterwards, it is proved that our proposed ACRN can improve the aggregate throughput of SUs. The superior efficiency of ACRN in terms of the number of admitted SUs, transmit power consumption and aggregate throughput is verified by simulation results for different scenarios.

Diversity-multiplexing tradeoff analysis in cooperative wireless networks with a multiple-antenna relay

Multiple antennas can be used for increasing the amount of diversity or number of degrees of freedom in wireless communication systems. In this study, the authors consider a wireless half-duplex relay network consisting of one source–destination pair each with a single antenna and one relay with multiple antennas. They assume an approach where cooperation of the relay in the transmission depends on the decoding status at the relay. If the relay participates in transmission, the source and the relay use the distributed space-time code simultaneously. They derive the outage probability and the diversity-multiplexing tradeoff for the proposed protocol at high SNR. They have shown that the use of the centralised antennas with simultaneously decoding compared to the distributed antennas with independently decoding can improve the system performance.

On the diversity analysis of symbol remapping in cooperative BICM–OFDM scheme for frequency selective Rayleigh fading channels

In this paper, diversity analysis of a symbol remapping scheme in cooperative systems is considered. We employ Bit Interleaved Coded Modulation and Orthogonal Frequency Division Multiplexing, which are widely used at wireless networks. In this paper, at the cooperative scheme, relays operate in DF mode and the effect of their erroneous detection will be considered. We present a closed form of the asymptotic worst-case pairwise error probability at the destination for our proposed scheme. It will be shown that this scheme exploits full spatial and frequency diversities. After that, we work on designing a new precoder matrix. Moreover, we propose two new subcarrier allocations for the proposed scheme. All new methods have been suggested to achieve the available diversities with a better performance than current state of the art. Finally, the simulation results reveal the analytical derivations and confirm the obtained diversity gain of the proposed systems.

A realistic receiver model for neuro-spike communication

Nano communication is a branch of nanotechnology, which is exploring biological systems via the development and designing of algorithms which can be used in molecular communications (MC). One of the most attractive fields in MC is neuro-spike communications, which dominates the communications among neurons, is a greatly unexplored area. In this study, we investigated a realistic detection method concerning neuro-spike communication in different locations of neurons that have difference sense for improving error probability and achieving optimum threshold voltage level in different SNR. Moreover there are some modulators in dendrites of neurons in receiver mode that add a random amplitude to the signals that receive from another neurons in transmitter mode. By manipulating the characters of distribution function of random amplitude, we can achieved less error probability. The obtained theoretical receiver model for molecular communications may aid to design new synthetic Nano scale communications approaches for the promoting of practical nanonetworks, suitable in the interconnections of Nanomachines as well as seeking medical treatment for neural disorders.

A low-complexity near-optimal algorithm for blind estimation of pseudo-noise sequences in DSSS communication systems

A novel algorithm is proposed for blind estimation of the spreading sequence in direct-sequence spread-spectrum (DSSS) communication systems. This algorithm is based on the maximum likelihood decision rule. Due to high computational complexity, the maximum likelihood method is feasible only for spreading sequence with very short periods. The novel algorithm has two steps for estimation. At first, a prior estimation of the spreading sequence is obtained via a simple algorithm which has a very low computational complexity. In the second step, using the prior estimation and the maximum likelihood decision rule, the estimation accuracy increases greatly. The proposed algorithm can reach the performance of the eigen value decomposition (EVD) method with low computational complexity.