Multi-antenna Decode and Forward Relay Selection over Nakagami-m Fading Channels

This paper analyzes the performance of opportunistic relay under aggregate power constraint in Decode-and-Forward (DF) relay networks over independent, non-identical, Nakagami-m fading channels, assuming multiple antennas are available at the relay node. According to whether instantaneous Signal-to-Noise Ratio (SNR) or average SNR can be exploited for relay selection, two opportunistic relay schemes, opportunistic multi-antenna relay selection (OMRS) and average best relay selection (ABRS) are proposed. The closed form expressions of outage probability and error performance for binary phase shift keying (BPSK) modulation of OMRS and ABRS are determined using the moment generating function (MGF) of the total signal-to-noise ratio (SNR) at the destination. Simulations are provided to verify the correctness of theoretical analysis. It is observed that OMRS is outage-optimal among multi-antenna relay selection schemes and approaches the Beamforming (BF) scheme known as theoretical outage-optimal very closely. Compared with previous single-antenna Opportunistic Relaying (OR) scheme, OMRS brings remarkable performance improvement obtained from maximum ratio combining (MRC) and beamforming, which proves that multiple antennas at the relays could provide more array gain and diversity order. It also shows that the performance of ABRS in asymmetric channels is close to OMRS in the low and median SNR range.     

Interference Cancelling Codes for Ultra-Reliable Random Access

Combinatorial code designs (CCDs) are proposed as a means for achieving ultra-reliability in the random access channel. In contrast to traditional access protocols that use random repetition coding, we show that by uniquely allocating repetition patterns to users, successful reception may be guaranteed up to a number of simultaneously active users in small frame sizes. Such codes are particularly robust in the low activity region where mission-critical machine-type communication is expected to operate. We also present deterministic codes designed to work in conjunction with successive interference cancellation to further improve reliability. The optimal IC code for frames of 5 access slots is given. Unlike slotted ALOHA, it is shown to limit packet losses to well below the ultra-reliability threshold (10^?5). These error performance gains come at the cost of a strict limitation on the supported user population (11 users in the case of 5 slots). We therefore consider larger frames of 24 slots, and analyse heuristic, low-complexity CCDs with fixed repetition factors that support up to 2024 users. While these are sub-optimal IC codes, significant gains are still observed compared to random codes.     

Direction of arrival estimation via reweighted $$l_1$$ norm penalty algorithm for monostatic MIMO radar

In this paper, a reweighted \(l_1\) norm penalty algorithm for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output radar is proposed. In the proposed method, exploiting the inherent multidimensional structure of received data after matched filtering, the singular value decomposition (SVD) technique of the data matrix is employed to reduce the dimension of the received signal. Then a novel weight matrix is designed for reweighting the \(l_1\) norm minimization by exploiting the coefficients of the reduced-dimensional Capon (RD-Capon) spatial spectrum. The proposed algorithm enhances the sparsity of the solution by the reweighted \(l_1\) norm constraint minimization, and the DOAs can be estimated by finding the non-zero rows of the recovered matrix. Owing to utilizing the SVD technique and the novel weight matrix, the proposed algorithm can provide better angle estimation performance than RD-Capon and \(l_1\)-SRACV algorithms. Furthermore, it is suitable for coherent sources and has a low sensitivity to the incorrect determination of the source numbers. The effectiveness and superior performance of the proposed algorithm are demonstrated by numerical simulations.     

A queueing model of an energy harvesting sensor node with data buffering

Battery lifetime is a key impediment to long-lasting low power sensor nodes and networks thereof. Energy harvesting—conversion of ambient energy into electrical energy—has emerged as a viable alternative to battery power. Indeed, the harvested energy mitigates the dependency on battery power and can be used to transmit data. However, unfair data delivery delay and energy expenditure among sensors remain important issues for such networks. We study performance of sensor networks with mobile sinks: a mobile sink moves towards the transmission range of the different static sensor nodes to collect their data. We propose and analyse a Markovian queueing system to study the impact of uncertainty in energy harvesting, energy expenditure, data acquisition and data transmission. In particular, the energy harvesting sensor node is described by a system with two queues, one queue corresponding to the battery and the other to the data buffer. We illustrate our approach by numerical examples which show that energy harvesting correlation considerably affects performance measures like the mean data delay and the effective data collection rate.     

A fuzzy logic based buffer management scheme with traffic differentiation support for delay tolerant networks

Delay tolerant networks (DTNs) are an emerging class of wireless networks which enable data delivery even in the absence of end-to-end connectivity. Under these circumstances, message replication may be applied to increase the delivery ratio. The requirement of long term storage and message replication puts a burden on network resources such as buffer and bandwidth. Buffer management is an important issue which greatly affects the performance of routing protocols in DTNs. Two main issues in buffer management are drop decision when buffer overflow occurs and scheduling decision when a transmission opportunity arises. The objective of this paper is to propose an enhancement to the Custom Service Time Scheduling traffic differentiation scheme by integrating it with a fuzzy based buffer ranking mechanism based on three message properties, namely, number of replicas, message size and remaining time-to-live. It uses fuzzy logic to determine outgoing message order and to decide which messages should be discarded within each traffic class queue. Results of simulation study show that the proposed fuzzy logic-based traffic differentiation scheme achieves improved delivery performance over existing traffic differentiation scheme for DTNs.     

Remove key escrow from the BF and Gentry identity-based encryption with non-interactive key generation

Key escrow is inherent in identity based encryption (IBE) since it was first put forward by Shamir. A key generation center (KGC) or private key generator is in charge of generating the user’s secret key, therefore a malicious one can decrypt any ciphertext without being found. This paper introduces a new scheme to eliminate key escrow from IBE. This scheme is based on the security concept of anonymous ciphertext indistinguishability against key generation center (ACI-KGC) which is introduced by Chow. We equip the Boneh and Franklin IBE (BF-IBE) and Gentry-IBE with our scheme and give the security analysis to show the accuracy and the security of it. In contrast to Chow’s scheme, to generate the secret key, our approach does not require the interactive protocol which brings about much extra costs.     

Mining user preferences of new locations on location-based social networks: a multidimensional cloud model approach

In recent years, the prevalent of location-based social networks contributes massive data for location recommendation. Although collaborative filtering (CF) algorithm has been widely employed for location recommendation, it suffers the data sparsity and the high time complexity as it estimates the similarity of users by the common locations. In this paper, we extend the two-dimensional cloud model to the multidimensional cloud model and utilize it to the measure the similarity of user preferences and user behaviors. This method not only considers the multiple attributes of users (e.g., the diversity of user preferences), but also alleviates the sparsity of location recommendation based on CF algorithm to some extent. Then we integrate the similarity of user preferences, social ties and user behaviors into CF algorithm, which is expected to mine user preferences of new locations (MUPNL) more precisely. Furthermore, in order to improve the efficiency of the MUPNL algorithm, we parallelize it with Mapreduce framework. Experimental results on Yelp academic dataset demonstrate the good performance of the distributed MUPNL algorithm in accuracy and efficiency.     

Power allocation in small cell networks with full-duplex self-backhauls and massive MIMO

With the dense deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most of them do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. In this paper, we propose a self-backhaul scheme for small cell networks by combining FD and massive MIMO technologies. In our proposed scheme, the macro base station (MBS) is equipped with massive MIMO antennas, and the SBSs have the FD communication ability. By treating the SBSs as special macro users, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency. Furthermore, considering the existence of inter-tier and intra-tier interference, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transform the original problem into a difference of convex program by successive convex approximation method and variable transformation, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.     

Misalignment controller in wireless battery charger for electric vehicle based on MPPT method and metaheuristic algorithm

This paper focuses on applying a wireless power transfer WPT technology in an electric vehicle battery charger. The wireless charger system is based on the principle of resonance inductive coupling power through a coreless transformer. The WPT system is considered as a multi parameter and multi constrained nonlinear system. The main contribution in this paper is the use of PSO and GA metaheuristic algorithms in the optimization of a transformer design regarding the impact of a lateral misalignment and the separation distance between the primary and secondary coils. To find the best global solution which is considered as the maximum efficiency in the complex system, both algorithms are compared. A perturbation-and-observation-based tracking system is developed through an efficiency sensing system to act on the misalignment issue and the car position. An additional PSO controller is performed to control the duty cycle of the boost converter in order to follows the maximum efficiency operating points of a WPT system. The discrepancy is the use of the resonant inductive coupling as a source of the MPPT so as to perturb the car position and observe the transferred. Furthermore, the modeling of a contactless transformer is optimized using metaheuristic algorithm.     

CRWSNP: cooperative range-free wireless sensor network positioning algorithm

Sensing events occur in an area without knowing the events locations, is meaningless. Since there is no priorly knowledge about the locations of most of the sensors which scattered randomly in an area, wireless sensor network localization methods try to find out where sensors are located. A new cooperative and distributed range-free localization algorithm, based on only connectivity information is proposed in this paper. The method first uses convex optimization techniques to find primitive target nodes locations estimation, then nodes cooperate with each other in several iterations to improve the whole network location estimation. CRWSNP converges after a finite number of iterations because of applying two novel heuristic location correction techniques. As well as, results of the algorithm have been compared with six range-free based methods like CPE, DV-hop, APIT; and CRWSNP algorithm provides more accurate results over 50 random topologies for the network, in mean error and maximum error metrics.