Best Minimum Summation Scheduler for Long Term Evolution

Resource scheduling in Long Term Evolution (LTE) is an open and rising issue. It has an enormous impact on the entire system performance. Due to the nature of LTE system, the scheduler has to be designed carefully. It has to overcome many challenges such as limited processing time and the high dynamic behavior. This paper proposes a novel scheduling policy for the MAC layer in LTE called the Best Minimum Summation (BMS). The main aim of this scheduling policy is to achieve high performance with low complexity. Three sub-schedulers have been developed. Each one of these schedulers deals with scheduling table in different dimension. The first one operates on the scheduling table through the user dimension (BMS.UE); while the second one operates on the scheduling table through the resource block dimension (BMS.RB). The third scheduler operates on the scheduling table correlating both of these dimensions (BMS.2D). All of the proposed solutions were intensively evaluated in a system level simulator. Three performance metrics were used which are throughput, error rate and fairness. The results have shown that the ability of the BMS.UE scheduler to outperform other existing schedulers of LTE.     

Catalyst Design by NH4OH Treatment of USY Zeolite

Hierarchical zeolites are a class of superior catalysts which couples the intrinsic zeolitic properties to enhanced accessibility and intracrystalline mass transport to and from the active sites. The design of hierarchical USY (Ultra‐Stable Y) catalysts is achieved using a sustainable postsynthetic room temperature treatment with mildly alkaline NH₄OH (0.02 m ) solutions. Starting from a commercial dealuminated USY zeolite (Si/Al = 47), a hierarchical material is obtained by selective and tuneable creation of interconnected and accessible small mesopores (2–6 nm). In addition, the treatment immediately yields the NH₄⁺ form without the need for additional ion exchange. After NH₄OH modification, the crystal morphology is retained, whereas the microporosity and relative crystallinity are decreased. The gradual formation of dense amorphous phases throughout the crystal without significant framework atom leaching rationalizes the very high material yields (>90%). The superior catalytic performance of the developed hierarchical zeolites is demonstrated in the acid‐catalyzed isomerization of α‐pinene and the metal‐catalyzed conjugation of safflower oil. Significant improvements in activity and selectivity are attained, as well as a lowered susceptibility to deactivation. The catalytic performance is intimately related to the introduced mesopores, hence enhanced mass transport capacity, and the retained intrinsic zeolitic properties.     

A hybrid classifier fusion approach for motor unit potential classification during EMG signal decomposition

In this paper, we propose a hybrid classifier fusion scheme for motor unit potential classification during electromyographic (EMG) signal decomposition. The scheme uses an aggregator module consisting of two stages of classifier fusion: the first at the abstract level using class labels and the second at the measurement level using confidence values. Performance of the developed system was evaluated using one set of real signals and two sets of simulated signals and was compared with the performance of the constituent base classifiers and the performance of a one-stage classifier fusion approach. Across the EMG signal data sets used and relative to the performance of base classifiers, the hybrid approach had better average classification performance overall. For the set of simulated signals of varying intensity, the hybrid classifier fusion system had on average an improved correct classification rate (CCr) (6.1%) and reduced error rate (Er) (0.4%). For the set of simulated signals of varying amounts of shape and/or firing pattern variability, the hybrid classifier fusion system had on average an improved CCr (6.2%) and reduced Er (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CCr (7.5%) and reduced Er (1.7%).     

A bipolar implementation of the Beta neuron

The great flexibility of the Beta function and its universal approximation characteristics, make Beta basis function neural networks (BBFNNs) very useful. We present a hardware implementation of the Beta neuron. The proposed circuit was designed by using a standard bipolar technology. PSPICE simulations show the good concordance of the output of our circuit with the analytic Beta function. We also successfully integrated the electronic Beta neuron in the design of a BBFNN that approximates a nonlinear mapping.     

Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive Relays

Wireless Personal Communications - The cell-Free massive multiple input multiple output “mMIMO” networks can provide a satisfied performance for the fifth generation “5G”...     

Enhancing inter-PMIPv6-domain for superior handover performance across IP-based wireless domain networks

As a network-based localized mobility management protocol, Proxy Mobile IPv6 (PMIPv6) enables a Mobile Host (MH) to roam within a localized domain without MH intervention in the mobility-related signalling. However, the PMIPv6 maintains MH mobility support in a restriction domain. Therefore, whenever the MH roams away from the PMIPv6 domain, its reachability status will be broken-down causing high handover latency and inevitable traffic loss for its communication session. This article proposes a proactive mechanism to mange the MH handover and maintain its data session continually across inter-PMIPv6-domains. The proposed mechanism introduces an intermediate global mobility anchor entity, called, which is responsible to coordinate MH handover as well as redirect its traffic across inter-PMIPv6-domains. Through various simulation evaluations, via ns-2, several experiments were conducted, revealing numerous results that verify the proposed mechanism superior performance over the conventional inter-PMIPv6-domain handover schemes in terms of handover latency, achieved throughput, protocol signalling cost and end-to-end traffic delivery latency.     

Impact of Offloading on the Efficiency of Wireless Access Networks

International Journal of Wireless Information Networks - Wireless access networks need to deliver a satisfactory level of Quality of Service (QoS) to their subscribers. The service quality can be...     

An evolutionary bargaining‐based approach for incentivized cooperation in opportunistic networks

Opportunistic networking enables users to communicate in an environment where connectivity is intermittent or unstable. However, such networking scheme assumes that mobile nodes voluntary cooperate, which cannot be guaranteed. Some nodes can simply exhibit selfish behavior and thus diminish the effectiveness of the approach. In this paper, a game scenario is formulated in which the nodes try to convince each other to participate in packets forwarding. Each node is considered as a player in this game. When a node comes in the communication range of another, a bargaining game starts between them as part of the message forwarding process. Both players try to have a mutual agreement on a price for message forwarding. We present a new incentive mechanism called evolutionary bargaining‐based incentive scheme (EBIS) to motivate selfish nodes to cooperate in data forwarding. In EBIS, a node negotiates with other nodes to obtain an agreeable amount of credit for its forwarding service. Nodes apply a sequential bargaining game and then adapt their strategies using an evolutionary model to maximize the probability of reaching an agreement. Unlike classical bargaining games, nodes in our model are boundedly rational. In addition, we use the evolutionary stable strategy (ESS) concept to determine the adaptive strategies for the nodes. The comparison of EBIS with a benchmarked model demonstrates that EBIS performs better in terms of packet delivery ratio and average latency.     

REBTAM: reliable energy balance traffic aware data reporting algorithm for object tracking in multi-sink wireless sensor networks

Recently, Multi-sink Wireless Sensor Networks (WSNs) have received more and more attention due to their significant advantages over the single sink WSNs such as improving network throughput, balancing energy consumption, and prolonging network lifetime. Object tracking is regarded as one of the key applications of WSNs due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. However, many object tracking researches usually focus on how to track the location of objects accurately, while few researches focus on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Due to the limited energy resource of sensor nodes, it seems especially important to design an energy efficient data reporting algorithm for object tracking in WSNs. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. In addition, congestion in WSNs has negative impact on the performance, namely, decreased throughput, increased per-packet energy consumption and delay, thus congestion control is an important issue in WSNs. Consequentially, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in our model where the sensed data can reach the sink node in a more reliable way. Finally, this paper presents a solution that sufficiently exerts the underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. This work first formulates the problem as 0/1 Integer Linear Programming problem, and proposes a Reliable Energy Balance Traffic Aware greedy Algorithm in multi-sink WSNs (REBTAM) to solve the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols.     

Numerical modeling of ZnSnO/CZTS based solar cells

Numerical simulation has been performed to improve the performance of Cu2ZnSnS4 (CZTS) solar cells by replacing CdS with Zn1–xSnxO buffer layer. The influences of thickness, donor concentration and defect density of buffer layers on the performance of CZTS solar cells were investigated. It has been found that Zn1–xSnxO buffer layer for Sn content of 0.20 is better for CZTS solar cell. A higher efficiency can be achieved with thinner buffer layer. The optimized solar cell demonstrated a maximum power conversion efficiency of 13%.