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Advances in Dense 5G/6G Wireless Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 12932

Special Issue Editors


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Guest Editor
Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland
Interests: wireless communications; spectrum management; waveform design
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland
Interests: telecommunications; wireless systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Computer Science Department, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: network & systems management; wireless networks; adaptive systems; innovation in education
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Networks Laboratory, Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", 15310 Athens, Greece
Interests: radio resource management for 5G heterogeneous networks; QoS management in mobile IoT; routing in mobile ad hoc networks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
RIMEDO Labs, 61-131 Poznan, Poland
Interests: SON, RRM, 5G, 3GPP, Radio Interface, Open RAN, O-RAN, Traffic Steering

Special Issue Information

Dear Colleagues,

Although the number of users and connected devices in contemporary wireless networks is very large and is expected to grow even larger in the future, incorporating the well-known concept of dense wireless networks in practice is still challenging. It is even envisaged that the number of deployed devices (including all smartphones, sensors and actuators, and other internet-of-things modules) will be so large that it can be referred to as ultra-dense wireless communications. In consequence, this will result in a considerable diversity of communication types observed in the heterogeneous networks, which vary from rare and bursty short-packet transmissions originating from wireless sensors to wireless broadband transmission between connected smartphones. This problem was initially addressed within 5G networks by considering the deployment of dedicated network slices. However, the recent trends in the development of communication systems and networking, both wired and wireless, indicate the need for tight and simultaneous cooperation between various kinds of devices in various contexts. For example, the widely deployed wireless sensors are becoming a crucial part of wireless broadband connectivity by monitoring various features of the communication environment, coordinating their operation, and exploiting their sensed information. Moreover, one can observe the continuously increasing impact of ubiquitous softwarization on the effective design of wireless systems and their underlying algorithms. The overall softwarization of the communication networks provides various benefits for network operators and changes the industry portfolio related to the communication networks. Thus, it is envisaged that future communication systems will have to face unprecedented challenges related to a high degree of diversity of prospective applications, types of devices, system requirements, types of environments, etc. Moreover, the foreseen increase in the number of active devices within the network, as well as in the volume of acquired and processed data, paves the way for novel implementation paradigms in the future. Finally, issues such as the scale of optimization problems, the presence of data with different levels of reliability and veracity, various types and sizes of data, and various requirements in processing delay entail the need for new algorithmic designs for future wireless networks.

This Special Issue covers topics related to recent advances in dense wireless communications and networking. We invite the authors to submit new research and review papers in the topics including (but not limited to) the ones listed below:

  • Solutions in SDN, VNF, CR, and SDR tailored to dense wireless networks;
  • Machine learning and application of artificial intelligence for 5G and beyond in the context of dense wireless networks;
  • Big data processing for dense wireless communications and networking;
  • Fuzzy logic schemes for dense wireless network;
  • Novel network design paradigms for radio access networks—open RAN;
  • Advances in massive communication (e.g., for massive machine-to-machine communications and for massive MIMO schemes);
  • Advances in high mobility scenarios including V2X and U2X scenarios;
  • Distributed, centralized, and hybrid architecture design for dense future wireless communications and networking;
  • Advanced signal processing algorithms for dense wireless networks;
  • Cooperation algorithms for dense wireless systems;
  • Distributed computing (in edge, fog, cloud) and processing in dense wireless networks;
  • Joint consideration of issues such as communications, computing, control, localization, and sensing in dense wireless networks.

Dr. Adrian Kliks
Dr. Paweł Sroka
Dr. Cynthia Hood
Dr. Nikos Dimitriou
Dr. Marcin Dryjanski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dense wireless networks
  • wireless communications and networking
  • 5G systems and beyond
  • big data processing

Published Papers (6 papers)

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Research

22 pages, 875 KiB  
Article
Communication and Computing Task Allocation for Energy-Efficient Fog Networks
by Bartosz Kopras, Filip Idzikowski, Bartosz Bossy, Paweł Kryszkiewicz and Hanna Bogucka
Sensors 2023, 23(2), 997; https://doi.org/10.3390/s23020997 - 15 Jan 2023
Cited by 3 | Viewed by 1436
Abstract
The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or [...] Read more.
The well known cloud computing is being extended by the idea of fog with the computing nodes placed closer to end users to allow for task processing with tighter latency requirements. However, offloading of tasks (from end devices to either the cloud or to the fog nodes) should be designed taking energy consumption for both transmission and computation into account. The task allocation procedure can be challenging considering the high number of arriving tasks with various computational, communication and delay requirements, and the high number of computing nodes with various communication and computing capabilities. In this paper, we propose an optimal task allocation procedure, minimizing consumed energy for a set of users connected wirelessly to a network composed of FN located at AP and CN. We optimize the assignment of AP and computing nodes to offloaded tasks as well as the operating frequencies of FN. The considered problem is formulated as a Mixed-Integer Nonlinear Programming problem. The utilized energy consumption and delay models as well as their parameters, related to both the computation and communication costs, reflect the characteristics of real devices. The obtained results show that it is profitable to split the processing of tasks between multiple FNs and the cloud, often choosing different nodes for transmission and computation. The proposed algorithm manages to find the optimal allocations and outperforms all the considered alternative allocation strategies resulting in the lowest energy consumption and task rejection rate. Moreover, a heuristic algorithm that decouples the optimization of wireless transmission from implemented computations and wired transmission is proposed. It finds the optimal or close-to-optimal solutions for all of the studied scenarios. Full article
(This article belongs to the Special Issue Advances in Dense 5G/6G Wireless Networks)
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14 pages, 680 KiB  
Article
Efficiency Maximization for Battery-Powered OFDM Transmitter via Amplifier Operating Point Adjustment
by Pawel Kryszkiewicz
Sensors 2023, 23(1), 474; https://doi.org/10.3390/s23010474 - 1 Jan 2023
Cited by 3 | Viewed by 1420
Abstract
While Orthogonal Frequency Division Multiplexing (OFDM) is a dominating spectrum access technology in modern, wideband access networks, it is important to maximize its transmission efficiency considering the underlying radio front-end characteristics. A practical front-end contains nonlinear components, e.g., a Power Amplifier (PA), resulting [...] Read more.
While Orthogonal Frequency Division Multiplexing (OFDM) is a dominating spectrum access technology in modern, wideband access networks, it is important to maximize its transmission efficiency considering the underlying radio front-end characteristics. A practical front-end contains nonlinear components, e.g., a Power Amplifier (PA), resulting in nonlinear distortion being injected into OFDM band deteriorating symbols detection. A PA operating point, defined here by Input Back-Off (IBO), can be adjusted to balance the wanted signal power and nonlinear distortion power. While it is the most common to maximize the spectral efficiency (SE), recently, energy efficiency (EE) maximization gained momentum. However, EE maximization requires, in addition to PA nonlinearity modeling, modeling of the power consumption of the PA and all other transmitter components. While it is commonly overlooked, if a battery is used to power the transmitter, its model should be considered as well. This paper derives mathematical expressions for EE and SE of an OFDM transmitter considering Rapp and soft-limiter models of PA nonlinearity, class A, class B, and perfect PA power consumption models, and two battery models: perfect and worst-case. While closed-form expressions cannot be obtained for most of the derived integrals, numerical methods have been used to obtain the optimal IBO value in each case. The numerical results show, in addition to optimal IBO values, the expected Signal-to-Noise and Distortion Ratios (SNDRs). It is shown that the optimal IBO value changes significantly with the wireless channel properties, utilized hardware architecture, or the utilized optimization goal. As such, the proposed optimization is an important topic for 5G and beyond transmitters. Full article
(This article belongs to the Special Issue Advances in Dense 5G/6G Wireless Networks)
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19 pages, 2720 KiB  
Article
A Deep Q-Network-Based Algorithm for Multi-Connectivity Optimization in Heterogeneous Cellular-Networks
by Juan Jesús Hernández-Carlón, Jordi Pérez-Romero, Oriol Sallent, Irene Vilà and Ferran Casadevall
Sensors 2022, 22(16), 6179; https://doi.org/10.3390/s22166179 - 18 Aug 2022
Cited by 5 | Viewed by 1634
Abstract
The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent [...] Read more.
The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access network. Full article
(This article belongs to the Special Issue Advances in Dense 5G/6G Wireless Networks)
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13 pages, 4710 KiB  
Article
Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)
by Pedro Enrique Iturria-Rivera, Han Zhang, Hao Zhou, Shahram Mollahasani and Melike Erol-Kantarci
Sensors 2022, 22(14), 5375; https://doi.org/10.3390/s22145375 - 19 Jul 2022
Cited by 16 | Viewed by 2977
Abstract
Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last [...] Read more.
Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments worldwide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning, more specifically multi-agent team learning (MATL), to optimize the RAN in a closed-loop where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with MATL can play an essential role in the orchestration of O-RAN controllers, i.e., near-real-time and non-real-time RAN Intelligent Controllers (RIC). In this article, we first provide an overview of the landscape in RAN disaggregation, virtualization and O-RAN, then we present the state-of-the-art research in multi-agent systems and team learning as well as their application to O-RAN. We present a case study for team learning where agents are two distinct xApps: power allocation and radio resource allocation. We demonstrate how team learning can enhance network performance when team learning is used instead of individual learning agents. Finally, we identify challenges and open issues to provide a roadmap for researchers in the area of MATL based O-RAN optimization. Full article
(This article belongs to the Special Issue Advances in Dense 5G/6G Wireless Networks)
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19 pages, 1206 KiB  
Article
Multi-Floor Indoor Localization Based on Multi-Modal Sensors
by Guangbing Zhou, Shugong Xu, Shunqing Zhang, Yu Wang and Chenlu Xiang
Sensors 2022, 22(11), 4162; https://doi.org/10.3390/s22114162 - 30 May 2022
Cited by 11 | Viewed by 2251
Abstract
High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost [...] Read more.
High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation. Full article
(This article belongs to the Special Issue Advances in Dense 5G/6G Wireless Networks)
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16 pages, 2082 KiB  
Article
A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
by Tingyao Wu, Hongxia Bie and Jinfang Wen
Sensors 2022, 22(10), 3937; https://doi.org/10.3390/s22103937 - 23 May 2022
Cited by 1 | Viewed by 1703
Abstract
Compared with orthogonal frequency division multiplexing (OFDM) systems, orthogonal time frequency space systems based on bi-orthogonal frequency division multiplexing (OTFS-BFDM) have lower out-of-band emission (OOBE) and better robustness to high-mobility scenarios, but suffer from a higher peak-to-average ratio (PAPR) in large data packets. [...] Read more.
Compared with orthogonal frequency division multiplexing (OFDM) systems, orthogonal time frequency space systems based on bi-orthogonal frequency division multiplexing (OTFS-BFDM) have lower out-of-band emission (OOBE) and better robustness to high-mobility scenarios, but suffer from a higher peak-to-average ratio (PAPR) in large data packets. In this paper, one-iteration clipping and filtering (OCF) is adopted to reduce the PAPR of OTFS-BFDM signals. However, the extra noise introduced by the clipping process, i.e., clipping noise, will distort the desired signal and increase the bit error rate (BER). We propose a message passing (MP)-assisted iterative cancellation (MP-AIC) method to cancel the clipping noise based on the traditional MP decoding at the receiver, which incorporates with the (OCF) at the transmitter to keep the sparsity of the effective channel matrix. The main idea of MP-AIC is to extract the residual signal fed to the MP detector by iteratively constructing reference clipping noise at the receiver. During each iteration, the variance of residual signal and channel noise are taken as input parameters of MP decoding to improve the BER. Moreover, the convergence probability of the modulation alphabet after MP decoding in the current iteration is used as the initial probability of MP decoding in the next iteration to accelerate the convergence rate of MP decoding. Simulation results show that the proposed MP-AIC method significantly improves MP-decoding accuracy while accelerating the BER convergence in the clipped OTFS-BFDM system. In the clipped OTFS-BFDM system with rectangular pulse shaping, the BER of MP-AIC with two iterations can be reduced by 72% more than that without clipping noise cancellation. Full article
(This article belongs to the Special Issue Advances in Dense 5G/6G Wireless Networks)
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