EURASIP Journal on Wireless Communications and Networking最新文献

Abstract

Pilot contamination is a serious issue in massive multi–input–multi–output systems which significantly degrades system performance. In this paper, we investigate a new pilot assignment scheme by integrating two-dimensional genetic algorithm with Tabu-Search algorithm (TS) to mitigate the pilot contamination problem. Firstly, we design a two-dimensional genetic algorithm equipped with elitism strategy as a principal algorithm for solving the pilot assignment problem; then, aiming to enhance the convergence speed of the genetic algorithm to the ideal optimal solution, we integrate TS with the genetic algorithm. This integrated pilot assignment scheme, henceforth designated as GATS-PA, is found to be powerful in mitigating the pilot contamination problem. Numerical simulation results verify that the proposed pilot assignment scheme is very close to the ideal optimal solution with few numbers of iterations and outperforms existing methods in terms of enhancing the average uplink rate per user over a wide range of simulation parameters.

Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS ({text {(The Code of TConNS is available at https://github.com/zhengLabs/NSO)}}). Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures.

This paper presents a Highly Isolation open-loop resonators (OLR)—based microstrip full-duplex Tx/Rx antenna systems with low insertion loss for contemporary wireless system applications. Through a T-junction combiner, the proposed diplexer is accomplished by combining two OLR—based band-pass filters tuned at two distinct frequencies. The system is implemented on a Rogers TMM4 substrate with a loss tangent of 0.002, a dielectric constant of 4.7, and a thickness of 1.52 mm. The suggested full duplex has dimensions of (90 × 70) mm². It achieves a modest frequency space ratio of R = 0.1646 in both transmit and receive modes by having two resonance frequencies of ({f}_{t}) = 2.191 GHz and ({f}_{r}) = 2.584 GHz, respectively. The simulated structure displays good insertion losses (IL) of approximately 1.2 dB and 1.79 dB for the two channels, respectively, at fractional bandwidths of 1.24% at 2.191 GHz and 0.636% at 2.584 GHz. The simulated isolation values for 2.191 GHz and 2.584 GHz are 53.3 dB and 66.5 dB, respectively.

The Internet of Things (IoT) is spreading rapidly around the world, and Message Queue Telemetry Transport (MQTT) is one of the main protocols used to explore device-to-device (D2D) communication. The industry typically requires communication systems that can transmit data continuously while optimizing both bandwidth and transmission time. Due to the vast amount of data that can be lost, companies often find that even short periods of network downtime lead to significant costs. In this paper, we propose a retransmission mechanism to allow sensor nodes to relay missing data via MQTT to a local server when it reconnects after an unexpected disconnection. To assess its performance, several tests in a digital healthcare use case scenario have been designed. Since the procedure involves transferring a considerable amount of data, our main goal is to determine the maximum payload of each message to restore the missing information, while minimizing the retransmission time without information loss.

With the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme.

The majority of IoT implementations demand sensor nodes to run reliably for an extended time. Furthermore, the radio settings can endure a high data rate transmission while optimizing the energy-efficiency. The LoRa/LoRaWAN is one of the primary low-power wide area network (LPWAN) technologies that has highly enticed much concentration. The energy limits is a significant issue in wireless sensor networks since battery lifetime that supplies sensor nodes have a restricted amount of energy and neither expendable nor rechargeable in most cases. A common hypothesis is that the energy consumed by sensors in sleep mode is negligible. With this hypothesis, the usual approach is to consider subsets of nodes that reach all the iterative targets. These subsets also called coverage sets, are then put in the active mode, considering the others are in the low-power or sleep mode. In this paper, we address this question by proposing an energy consumption model based on LoRa and LoRaWAN, which optimizes the energy consumption of the sensor node for different tasks for a period of time. Our energy consumption model assumes the following, the processing unit is in on-state along the working sequence which enhances the MCU unit by constructing it in low-power modes through most of the activity cycle, a constant time duration, and the radio module sends a packet of data at a specified transmission power level. The proposed analytical approach permits considering the consumed power of every sensor node element where the numerical results show that the scenario in which the sensor node transfers data to the gateway then receives an acknowledgment RX2 without receiving RX1 consumes the most energy; furthermore, it can be used to analyze different LoRaWAN modes to determine the most desirable sensor node design to reach its energy autonomy where the numerical results detail the impact of scenario, spreading factor, and bandwidth on power consumption.

Recently, there has been an increasing interest in monitoring and exploring the underwater environment for scientific applications such as oceanographic data collection, marine surveillance, and pollution detection. Underwater acoustic sensor networks (UASN) have been proposed as the enabling technology to observe, map and explore the ocean. Due to the unique characteristics of underwater aquatic environment, which are low bandwidth, long propagation delays, and high energy consumption, the data forwarding process is very difficult. This paper presents a survey of the routing protocols for UASN. The addressed routing protocols are classified from a mobility point of view in freely floating underwater sensor networks. Indeed, managing the mobility of freely floating underwater sensors is one of the most critical constraints in the design of routing protocols. That is why we classify the routing protocols into “reliable data forwarding protocols” and “prediction-based data forwarding protocols.” In the first category, the proposed protocols mainly endure nodes’ mobility by continuously updating location information aiming at delivering the packets to the sink. In the second category, routing protocols try to rather master the nodes’ mobility by predicting the future nodes’ positions either based on a mobility model or on historical nodes’ positions using filtering techniques. We believe that our classification will help not only in deeply understanding the main characteristics of each protocol but also in investigating the evolution of research work evolution to provide energy-efficient data forwarding solutions for freely floating UASN.

We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex relay station, and multiple users. To minimize the dissemination delay, rateless code is employed at the base station. Our goal is to find an efficient channel-aware scheduling policy at the half-duplex relay station, i.e., either fetch a packet from the base station or broadcast a packet to the users at each time slot, such that the file dissemination delay is minimized. We formulate the scheduling problem as a Markov decision process and propose an intelligent deep reinforcement learning-based scheduling algorithm. We also extend the proposed algorithm to adapt to dynamic network conditions. Simulation results demonstrate that the proposed algorithm performs very close to a lower bound on the dissemination delay and significantly outperforms baseline schemes.