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Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches. [ABSTRACT FROM AUTHOR]

Multicarrier transmission strategy provides great benefits in wireless communication technology. For this reason, all of the waveform contenders proposed for the fifth generation and beyond technologies are built on this strategy. Universal filtered multicarrier (UFMC) is one of the foremost representatives of the related waveform contenders. However, utilizing the multicarrier transmission strategy causes the UFMC to generate transmission signals with high peak-to-average power ratio (PAPR), which leads to serious deteriorations in the communication quality. In order to solve this issue, it is crucial to reduce the PAPR of transmission signal before giving it to the wireless channel. With the aim of developing an efficient PAPR reduction scheme for the UFMC waveform, we first developed a new discrete version of the invasive weed optimization (DIWO) algorithm and integrated it to one of the classical PAPR reduction techniques called partial transmit sequence (PTS) as a phase optimizer. By doing so, we obtained an advanced scheme called DIWO–PTS. The function of DIWO algorithm in this new scheme is to optimize the phase sequences in discrete space. The usage of DIWO as a phase optimizer enables PTS to reach better phase combinations in less number of searches. Thus, it becomes possible to boost the PAPR reduction performance of the PTS scheme to a quite high level. Simulation results support the advantage of DIWO-based phase optimization. In the simulations, neither of the algorithms used for comparison in our paper could reach the performance level achieved by DIWO algorithm due to its efficient optimization capability. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]

A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes. Many existing schemes were utilized for the purpose of increasing the data gathering capacity of sensor nodes from source to sink node. But the existing schemes suffered from maximum distance problem of reaching the sink node. Hence, to overcome those issues, we propose an energy efficient data gathering technique in WSN. Initially, in this paper, the sensor nodes are clustered using LEACH protocol. After the clustering process, cluster heads (CHs) are selected. These selected CHs in all the clusters are used for transferring the messages to the mobile node for data gathering. Then, optimal data transmission path is selected with the help of Optimal Minimum Covering Spanning Tree (OMST) algorithm. The OMST algorithm enhances data gathering and reduces the duration for reaching the sink node via selection of polling points. For reducing time complexity in MST, an efficient optimization algorithm is used. For an optimization purpose, particle gene swarm (PGS) model is used. From the experimental results, the proposed method achieved better results than the existing method in terms of energy, overhead, delay, delivery ratio, NLT and throughput. [ABSTRACT FROM AUTHOR]

In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider cross-layer optimization in wireless networks with wireless broadcast advantage, focusing on the problem of distributed scheduling of broadcast links. The wireless broadcast advantage is most useful in multicast scenarios. As such, we include network coding in our design to exploit the throughput gain brought in by network coding for multicasting. We derive a subgradient algorithm for joint rate control, network coding and scheduling, which however requires centralized link scheduling. Under the primary interference model, link scheduling problem is equivalent to a maximum weighted hypergraph matching problem that is NP-complete. To solve the scheduling problem distributedly, locally greedy and randomized approximation algorithms are proposed and shown to have bounded worst-case performance. With random network coding, we obtain a fully distributed cross-layer design. Numerical results show promising throughput gain using the proposed algorithms, and surprisingly, in some cases even with less complexity than cross-layer design without broadcast advantage. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new multicast delivery mechanism for bandwidth-demanding applications in IP networks. Our mechanism, referred to as Multiple-destination Overlay Multicast (MOM), combines the advantages of IP multicast and overlay multicast. We formulate the MOM routing problem as an optimization problem. We then design an algorithm based on Lagrangian relaxation on our formulation and propose a distributed protocol based on the algorithm. For network operators, MOM consumes less network bandwidth than both IP multicast and overlay multicast. For users, MOM uses less interface bandwidth than overlay multicast. [ABSTRACT FROM AUTHOR]

Flexible multimedia broadcast/multicast services such as mobile auction and advertisements are vital services for future mobile ad hoc networks. A great number of applications rely on a reliable and efficient medium access control (MAC) layer broadcast. The current IEEE 802.11 broadcast protocol based on carrier sense multiple access with collision avoidance does not offer any MAC layer recovery on broadcast frames. Consequently, unexpected lost frames may deteriorate the quality of broadcast/multicast services. Previous protocols extended request-to-send, clear-to-send, and acknowledgement to enhance the broadcast reliability. However, they still suffered from the hid- den terminal problem and the excessive retransmission problem at the same time. In this paper, we first formulate the excessive retransmission problem as an optimization problem and show that it is NP-hard even if the upper layer service is periodical beacon exchange. An approximation algorithm with a guaranteed approximation ratio is also suggested. Then, a reliable and efficient MAC layer broadcast protocol, which is named broadcast protocol with busy tone (BPBT), is proposed. BPBT applies a busy tone to solve the hidden terminal problem and applies the proposed approximation algorithm to solve the excessive retransmission problem. Finally, BPBT is compared with previous protocols for performance evaluation by simulation. [ABSTRACT FROM AUTHOR]

In this paper, we develop a fuzzy analytical model, which solves a multiobjective problem in multicast transmission with static trees over GMPLS networks. A model proponed by Donoso [4] was used. The multiobjective problem is solved through metaheuristic wit h evolutionary algorithms, in this case SPEA2 solving a NP-Hard problem in polynomial time. Additionally, a Fuzzy Evolutionary Algorithm based on SPEA2 is implemented. Finally, the comparison between FSPEA2 and SPEA2 is realized and we are giving some conclusions. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of evolutionary algorithms for multi objective optimization, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II) is presented. For this analysis, is taken as reference an optimization problem in a multicast data network, which has as objective functions the hop count and transmission delay. The algorithms performance is compared in tree different networks. Moreover, the model for two of this topologies using GAMS tool is resolved and results are compared with the NSGA-II and SPEA-II algorithms proposed. Problem results show the algorithms performance in their solution. [ABSTRACT FROM AUTHOR]