EURASIP Journal on Wireless Communications and Networking最新文献

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%.

This paper investigates distributed cell-free massive multiple-input multiple-output with non-ideal user equipment hardware under spatially correlated channels. By employing the use-and-then-forget technique, a lower capacity bound is derived based on the established generalized UE hardware impairments model. In addition, maximum ratio combining can be used to derive a closed-form expression of the spectral efficiency (SE), which offers novel insights into the impact of non-ideal UE hardware on network performance. Furthermore, a max–min SE fairness problem with UE hardware impairments is established where the optimization variables are data power and large-scale fading decoding (LSFD) vectors. Since this is a non-convex problem, we devise an iterative alternating optimization algorithm based on the bisection search to acquire the globally optimal solution. Numerical results indicate that the recommended joint data power control and LSFD design algorithm provides higher SE for the weakest UE, thus significantly enhancing the total SE of the network.

Conventional optical spatial modulation (SM) scheme activates one of the light-emitting diodes (LEDs) to transmit an intensity-modulated optical signal, in which the index of the activated LED is determined by spatial symbol and the emitted intensity is controlled by temporal symbol. In order to enhance the spectral efficiency (bits per channel use), we propose a joint SM and pulse amplitude width modulation (PAWM) as a novel optical spatial–temporal signaling scheme. In this paper, the proposed SM-PAWM optical signaling scheme is applied in a multi-input multi-output (MIMO) visible light communication (VLC) system. Employing optimal maximum likelihood (ML) algorithm to extract the spatial and temporal symbols is computationally prohibitive; hence, we develop a novel low-complexity detection scheme that converts the joint optimization problem separately to decode the spatial and temporal symbols. Moreover, theoretical results in terms of the successful identification probability of activated LED as well as the overall symbol error rate are derived. Extensive computer simulations are performed to validate the analytical results. It is shown that the proposed detection scheme is a feasible alternative to the ML detector in the VLC-MIMO system employing SM-PAWM.

With the increasing user density of wireless networks, various user partitioning techniques or algorithms segregate users into smaller, more manageable clusters. The benefit of user clustering techniques in non-orthogonal multiple access (NOMA) is to optimize resource allocation and improve network performance, spectral efficiency, and user fairness in next-generation wireless networks, particularly in scenarios with a high density of users and diverse channel conditions. With increasing users, the network creates clusters before implementing non-orthogonal multiple access within these clusters. In this paper, we have organized and classified various user clustering techniques deployed from the perspective of NOMA-based communication in the current era. Furthermore, researchers have highlighted some works deploying joint resource allocation and clustering optimization based on various criteria to enhance the overall sum rate of the network. We also identify low-complexity user clustering techniques for multiple applications, e.g. the Internet of Things, unmanned aerial vehicles, and reconfigurable intelligent surfaces in the 5G and beyond communication networks.

Abstract

In the context of 6G and 5G communication networks, particularly in disaster-stricken areas with a surging demand for connectivity and the rapid evolution of the internet of everything (IoE), the imperative for augmenting spectrum efficiency in unmanned aerial vehicles (UAVs) communication-enabled wireless networks becomes ever more pronounced. This work embarks on the mission of significantly enhancing spectrum efficiency by capitalizing on redundancies, such as correlations inherent in data sources like co-located video sensors. While the intuitive potential of exploiting redundancies for spectrum efficiency enhancement is apparent, our approach is systematically structured. We introduce a linear programming framework designed to meticulously allocate bandwidth to individual links based on their respective demands, while judiciously adhering to spatial spectrum reuse constraints. Subsequently, we employ this linear program to empirically quantify the improvements in spectrum efficiency engendered by source correlations. Furthermore, we elucidate various strategies for harnessing these correlations to maximize spectrum efficiency gains, while navigating the trade-off terrain between computational complexity and precision. Our findings resoundingly underscore the trans-formative power of identifying the precise set of source correlations, resulting in spectrum efficiency enhancements of up to two orders of magnitude. In terms of network performance, the judicious exploitation of source correlations grants admission to nearly 100% of delay-intolerant traffic, alongside substantial reductions in mean delay for delay-tolerant traffic.”

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.