Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
Smart interference management methods are required to enhance the throughput, coverage, and energy efficiency of a dense\nsmall cell network. In this paper, we propose a transmit power control for energy efficient operation of a dense small cell\nnetwork. We cast the power control problem as a noncooperative game to satisfy the design requirement that small cells do\nnot need any information exchange among them. We analyze the sufficient condition for the existence of a Nash equilibrium\n(NE) state of the proposed game. We also analyze that the NE state is unique by transforming the original nonlinear fractional\nprogramming problem into a nonlinear parametric programming problem. Through simulation studies, we verify our analysis\nresults. In addition, we show that the proposed method achieves higher energy efficiency of a network and balances the energy\nefficiency among cells more evenly than the methods based on the AIMD (additive increase and multiplicative decrease)\nalgorithm....
This paper considers coordinated multi-cell multicast precoding for massive\nmultiple-input-multiple-output transmission where only statistical channel state information of\nall user terminals (UTs) in the coordinated network is known at the base stations (BSs). We adopt\nthe sum of the achievable ergodic multicast rate as the design objective. We first show the optimal\nclosed-form multicast signalling directions of each BS, which simplifies the coordinated multicast\nprecoding problem into a coordinated beam domain power allocation problem. Via invoking the\nminorization-maximization framework, we then propose an iterative power allocation algorithm with\nguaranteed convergence to a stationary point. In addition, we derive the deterministic equivalent of\nthe design objective to further reduce the optimization complexity via invoking the large-dimensional\nrandom matrix theory. Numerical results demonstrate the performance gain of the proposed\ncoordinated approach over the conventional uncoordinated approach, especially for cell-edge UTs....
To achieve the advantages provided by massive multiple-input multiple-output (MIMO),\na large number of antennas need to be deployed at the base station. However, for the reason of cost,\ninexpensive hardwares are employed in the realistic scenario, which makes the system distorted\nby hardware impairments. Hence, in this paper, we analyze the downlink spectral efficiency in\ndistributed massive MIMO with phase noise and amplified thermal noise. We provide an effective\nchannel model considering large-scale fading, small-scale fast fading and phase noise. Based on the\nmodel, the estimated channel state information (CSI) is obtained during the pilot phase. Under the\nimperfect CSI, the closed-form expressions of downlink achievable rates with maximum ratio\ntransmission (MRT) and zero-forcing (ZF) precoders in distributed massive MIMO are derived.\nFurthermore, we also give the user ultimate achievable rates when the number of antennas tends to\ninfinity with both precoders. Based on these expressions, we analyze the impacts of phase noise on\nthe spectral efficiency. It can be concluded that the same limit rate is achieved with both precoders\nwhen phase noise is present, and phase noise limits the spectral efficiency. Numerical results show\nthat ZF outdoes MRT precoder in spectral efficiency and ZF precoder is more affected by phase noise....
Android platform is increasingly targeted by attackers due to its popularity and openness. Traditional defenses to malware are\nlargely reliant on expert analysis to design the discriminative features manually, which are easy to bypass with the use of sophisticated\ndetection avoidance techniques. Therefore, more effective and easy-to-use approaches for detection of Android\nmalware are in demand. In this paper, we present MobiSentry, a novel lightweight defense system for malware classification and\ncategorization on smartphones. Besides conventional static features such as permissions and API calls, MobiSentry also employs\nthe N-gram features of operation codes (n-opcode). We present two comprehensive performance comparisons among several\nstate-of-the-art classification algorithms with multiple evaluation metrics: (1) malware detection on 184,486 benign applications\nand 21,306 malware samples, and (2) malware categorization on DREBIN, the largest labeled Android malware datasets. We\nutilize the ensemble of these supervised classifiers to design MobiSentry, which outperforms several related approaches and gives\na satisfying performance in the evaluation. Furthermore, we integrate MobiSentry with Android OS that enables smartphones\nwith Android to extract features and to predict whether the application is benign or malicious. Experimental results on real\nsmartphones show that users can easily and effectively protect their devices against malware through this system with a small runtime\noverhead....
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