Dense Wireless Networks Research Articles

Multi-Armed Bandit Algorithms: Innovations and Applications in Dynamic Environments

This paper delves into the fundamental concept of the Multi-Armed Bandit (MAB) problem, structuring its analysis around two primary phases. The initial phase, exploration, is dedicated to investigating the potential rewards of each arm. Subsequently, the exploitation phase utilizes insights from exploration to maximize returns. The discussion then progresses to elucidate the core methodologies and workflows of three principal MAB algorithms: Upper Confidence Bound (UCB), Thompson Sampling, and Epsilon-Greedy. These algorithms are meticulously analyzed for their unique approaches and efficiencies in handling the MAB problem. Expanding the scope further, the paper spotlights three practical applications of MAB algorithms. The first application involves Dynamic Resource Allocation in Multi-Unmanned Aerial Vehicle (UAV) Air-Ground Networks, leveraging the K-armed Bandit framework. This is followed by an exploration of Product Pricing Algorithms grounded in MAB principles, offering innovative solutions for dynamic pricing strategies. Lastly, the paper examines a cost-effective MAB algorithm tailored for dense wireless networks, addressing the complexities and demands of modern network infrastructures. This comprehensive study not only highlights the versatility of MAB algorithms but also underscores their growing importance in diverse real-world applications.

A genetic scheduling strategy with spatial reuse for dense wireless networks

Novel networking technologies such as massive Internet-of-Things and 6G-and-beyond cellular networks are based on ultra-dense wireless communications. A wireless communication channel is a shared medium that demands access control, such as proper transmission scheduling. The SINR model can improve the performance of ultra-dense wireless networks by taking into consideration the effects of interference to allow multiple simultaneous transmissions in the same coverage area and using the same frequency band. However, scheduling in wireless networks under the SINR model is an NP-hard problem. This work presents a bioinspired solution based on a genetic heuristic to solve that problem. The proposed solution, called Genetic-based Transmission Scheduler (GeTS) produces a complete transmission schedule optimizing size, increasing the number of simultaneous transmissions (i.e., spatial reuse) thus allowing devices to communicate as soon as possible. Simulation results are presented for GeTS, including a convergence test and comparisons with other alternatives. Results confirm the ability of the solution to produce near-optimal schedules.

Dynamic Channel Allocation Scheme Based on Traffic Prediction in Dense Wireless Networks

Dynamic Channel Allocation Scheme Based on Traffic Prediction in Dense Wireless Networks

An AI-Enabled Framework to Defend Ingenious MDT-Based Attacks on the Emerging Zero Touch Cellular Networks

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.

Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks

Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel utilization efficiency although it always is regarded as a promising distributed channel access scheme, especially in dynamic dense wireless network scenarios. This paper investigates a Reinforcement Learning (RL) based multi-parameter joint optimization mechanism to build suitable access configuration for dense wireless multi-hop networks. Specifically, we propose a light-weight CSMA/CA mechanism based on Q-Learning (QL), namely QL-CSMA/CA, which jointly optimizes multiple channel access parameters, including Contention Window (CW), Maximum Backoff Exponent (MaxBE), and Maximum CSMA Backoffs (MaxCSMABackoffs), by setting mixed rewards and thus leading to higher network performance. Due to the limitation of the QL-CSMA/CA in large-scale state-space scenarios, we further present an enhanced CSMA/CA mechanism based on Deep Q-Network (DQN), namely DQN-CSMA/CA. Despite introducing a little complexity, the DQN-CSMA/CA can significantly improve transmission performance by utilizing more comprehensive network state information in complex dynamic network scenarios. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of throughput, packet loss rate, delay, and collision rate, especially in high-density scenarios. Specifically, compared to SCAMA and traditional CSMA/CA algorithms, the QL-CSMA/CA algorithm achieves 9% and 5% performance improvements in terms of throughput, while DQN-CSMA/CA algorithm achieves 14% and 10% improvements in terms of throughput, respectively.

Accuracy enhancement of Wi-Fi positioning by ranging in dense UWB networks

This study proposed a fusion-based approach of Wi-Fi fingerprint positioning with relatively accurate UWB ranging in dense wireless networks (DWNs) to optimize indoor localization accuracy. Such an optimization was achieved through a proposed objective or “loss” function that minimizes the total positioning errors of the nodes composing a virtual geometric structure with the internode distances preserved during optimization in the DWN. Theoretical analysis and simulation results of DWNs up to 100 nodes revealed that, with at least 6 extra measured distances among as few as 4 participating nodes including the device-under-targeting (DUT) itself, the resulting localization accuracy improvement was proportional to the square root of the number of participating nodes towards the limit of the ranging error. Per experiment outcomes of a 10-node UWB DWN, our Wi-Fi/UWB ensemble reduced the baseline Wi-Fi K-nearest neighbors (KNN) fingerprint localization error by up to 69.8 %, further validating our theoretical and simulation results.

Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem

In Wi-Fi standards, a relatively narrow range of frequency spectrums is declared as working, on the operation of which additional restrictions are imposed in different countries. When creating dense wireless network ecosystems focused on massive information traffic, this circumstance causes significant interference even in the case of using Wi-Fi 6/6E-compatible equipment. An effective solution to this problem is the implementation of a centralized management mechanism for the relevant parameters of the target network ecosystem. The growing attention to ecology and rational use of electricity makes the problem of maximizing energy consumption for useful information traffic in a dense Wi-Fi 6/6E ecosystem an urgent task. Only the addressed information traffic between the transmitter and the target subscriber, which are subjects of the OFDMA technology and the MU-MIMO multiple access system (with an emphasis on the latter), is considered useful. To solve the problem, the authors formalized the Wi-Fi 6/6E ecosystem’s energy consumption model, which takes into account the specifics of OFDMA and MU-MIMO, the influence of the communication channel characteristics on the speed of target information transfer, and detailed energy consumption for maintaining the network infrastructure in a functional state. Based on the created model, the research problem is represented by the difference between two monotonic functions, relative to which the problem of optimization with restrictions is set. The process of solving this problem is presented in the form of information technology with a branch-and-bound hierarchy and a nested unconditional optimization problem. The results of simulated modelling in the MATLAB-NS3 environment showed a significant advantage of the authors’ approach. The energy power consumption by the Wi-Fi 6/6E ecosystem, the parameters of which were adjusted with the help of the authors’ information technology, decreased by more than four times.

An Interface Setup Optimization Method Using a Throughput Estimation Model for Concurrently Communicating Access Points in a Wireless Local Area Network.

The IEEE 802.11 wireless local-area network (WLAN) has been deployed around the globe as a major Internet access medium due to its low cost and high flexibility and capacity. Unfortunately, dense wireless networks can suffer from poor performance due to high levels of radio interference resulting from adjoining access points (APs). To address this problem, we studied the AP transmission power optimization method, which selects the maximum or minimum power supplied to each AP so that the average signal-to-interference ratio (SIR) among the concurrently communicating APs is maximized.However, this method requires measurements of receiving signal strength (RSS) under all the possible combinations of powers. It may need intolerable loads and time as the number of APs increases. It also only considers the use of channel bonding (CB), although non-CB sometimes achieves higher performance under high levels of interference. In this paper, we present an AP interface setup optimization method using the throughput estimation model for concurrently communicating APs. The proposed method selects CB or non-CB in addition to the maximum or minimum power for each AP. This model approach avoids expensive costs of RSS measurements under a number of combinations. To estimate the RSS at an AP from another AP or a host, the model needs the distance and the obstacles between them, such as walls. Then, by calculating the estimated RSS with the model and calculating the SIR from them, the AP interface setups for a lot of APs in a large-scale wireless network can be optimized on a computer in a very short time. For evaluation, we conducted extensive experiments using Raspberry Pi for APs and Linux PCs for hosts under 12 network topologies in three buildings at Okayama University, Japan, and Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. The results confirm that the proposed method selects the best AP interface setup with the highest total throughput in any topology.

Ultra-Wideband Swarm Ranging Protocol for Dynamic and Dense Networks

Nowadays, not only wearable and portable devices but also aerial and ground robots can be made smaller, lighter, cheaper, and thus as large as hundreds of them may form a swarm to participate in a complicated cooperative application, such as searching, rescuing, mapping, and war-battling. Devices and robots in such a swarm have three important features, namely, large number, high mobility and short distance, hence they form a dynamic and dense wireless network. Successful swarm cooperative applications require low latency communications and real-time localization. This paper proposes to use ultra-wideband (UWB) radio technology to implement both functionalities, because UWB is very time-sensitive that an accurate distance can be calculated using the transmission and reception timestamps of data messages. A UWB swarm ranging protocol is designed to achieve simultaneously wireless data communication and swarm ranging that allows a device/robot to compute the distances to all the peer neighbors at the same time. This protocol is designed for dynamic and dense networks, meanwhile it can also be used in various wireless networks and implemented on various types of devices/robots including low-end ones. In our experiment, this protocol is implemented on Crazyflies, STM32 microcontroller powered micro drones, with onboard UWB wireless transceiver chips DW1000. Extensive real-world experiments are conducted to verify the proposed protocol on various performance aspects, with a total of 9 Crazyflie drones in a compact area. The implemented swarm ranging protocol is open-sourced at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SEU-NetSI/crazyflie-firmware</uri>

Local Delay Analysis and Improvement for User-SBS Correlated Deployments With Random DTX

Stochastic geometry has emerged as a well-established analysis tool for dense wireless networks. However, the commonly assumed homogeneous Poisson point process (PPP) model, where users are distributed in the space uniformly, may not be accurate enough, since the users are more likely to be clustered at densely populated sites. Driven by capacity needs, small base stations (SBSs) are deployed at the user hotspots, which results in a user-SBS correlated deployment. In order to capture this correlation, user locations are modeled as Poisson cluster process (PCP) with SBSs serving as the cluster centers. In this paper, by using the discontinuous transmission (DTX) mode, the local delay has been analyzed. We carried out the analysis under two classic PCP models—Thomas cluster process and Matérn cluster process. Moreover, it is found that under PCP the local delay is more dependent on base station (BS) density and transmit power than under the conventional PPP model. Finally, we found that the local delay increases as the cluster size increases, and it converges to the PPP result when the cluster size approaches the infinity.

A spectral clustering algorithm for intelligent grouping in Dense Wireless Networks

The density of wireless networks has been increasing with the popularization of mobile devices. Dense wireless networks (DWN) present challenges such as the current spectral scarcity and the growing demand for capacity. The Restricted Access Window (RAW) mechanism was introduced by the IEEE 802.11ah amendment to improve DWN performance. RAW restricts the number of stations that can access the channel by arbitrarily separating them into groups. K-Means clustering has shown potential to find more efficient groups using the geographical coordinates of each station. However, due to the mobile and dynamic nature of such networks, location information is difficult to obtain in practice. In this paper, we consider the use of spectral clustering to increase the performance of DWN with hidden terminals. We discuss how a spectral clustering algorithm that generates RAW groups can be implemented in practice without the geographic location of each node. We also compare the performance of the spectral clustering algorithm with the standard grouping method used in IEEE 802.11ah, with the K-Means clustering (i.e., based on node location information), and with the hidden matrix-based regrouping (HMR) algorithm. Simulation results considering several density levels, different traffic patterns, and different propagation models indicate that spectral clustering significantly outperforms both the standard grouping and HMR in terms of collision rate, throughput, and delay. It also closely approximates – and sometimes surpasses – the performance of the K-Means clustering while being much more practical to implement because it does not require knowledge on nodes’ geographical coordinates.

Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry

Device density in cellular networks is expected to increase considerably in the near future. Accordingly, the access point (AP) will be equipped with massive multiple-input multiple-output (mMIMO) antennas, using collimated millimeter-wave (mmW) and sub-THz communications, and increasing the bandwidth to accommodate the growing data rate demands. In this scenario, interference plays a critical role and, if not characterized and mitigated properly, might limit the performances of the network. In this context, this paper derives the statistical properties of the aggregated interference power for a cellular network equipping a mMIMO cylindrical array. The proposed statistical model considers the link blockage and other network parameters such as antenna configuration and device density. The findings show that the characteristic function (CF) of the aggregated interference power can be regarded as a weighted mixture of two alpha-stable distributions. Furthermore, by analyzing the service probability, it is found that there is an optimal configuration of the array depending on the AP height and device density. The proposed statistical model can be part of the design of dense networks providing valuable insights for optimal network deployment and resource management and scheduling.

Parallel Processing at the Edge in Dense Wireless Networks

This paper investigates the gain of parallel processing, when mobile edge computing (MEC) is implemented in dense wireless networks. In this scenario users connect to several access points (APs), and utilize the computation capability of multiple servers at the same time. This allows a balanced load at the servers, with the eventual cost of decreased spectrum efficiency. The problem of sum transmission energy minimization under response time constraints is considered and proved to be non-convex. The complexity of optimizing a part of the system parameters is investigated, and based on these results an iterative resource allocation procedure is proposed that converges to a local optimum. The performance of the joint resource allocation is evaluated by comparing it to lower and upper bounds defined by less or more flexible multi-cell MEC architectures. The results show that the free selection of the AP is crucial for achieving decent system performance. The average level of parallel processing is in general low in dense systems, but it is an important option for the rare, highly unbalanced instances.

Deep Reinforcement Learning-Based Hierarchical Time Division Duplexing Control for Dense Wireless and Mobile Networks

Future wireless and mobile network services must accommodate highly dynamic downlink and uplink traffic asymmetry. To fulfill this requirement, the third-generation partnership project (3GPP) introduced the enhanced interference mitigation and traffic adaptation strategy in addition to dynamic time division duplexing (TDD). In this study, we develop a reinforcement learning (RL)-based dynamic TDD framework that effectively controls interference and serves various traffic demands. First, we introduce an interference-penalty model that evaluates interference indirectly based on the duplexing policy. This can significantly reduce overhead for measuring and exchanging channel information in a dense network. Second, we design a new mixed-reward model that consists of the achievable data rate and the implicit interference penalty. Third, we implement deep RL algorithms that base station (BSs) use to train their radio frame configurations (RFCs). The training process at each BS takes into account the traffic demand and the RFCs of the surrounding BSs. The BSs are coordinated in a single-leader multi-follower Stackelberg game, which achieves a global RFC setup that maximizes the data rate and minimizes the interference. Extensive simulations show that the proposed framework stably converges in various environments and provides near-optimal performance equivalent to 95% or more of the full-search-based optimal performance, which is 48.84%, 41.92%, and 62.11% higher than the currently utilized random RFC, fixed RFC, and traffic-matched RFC approaches.

A centralized architecture for autonomic quality of experience oriented handover in dense networks

This paper presents an Optimised Handover (HO) Algorithm for Dense Wireless Local Area Networks (WLANs) based on a novel architecture of Software Defined Wireless Network (SDWN). The work has been designed to be effective in large network environments with a high density of Access Points (APs) and stations, which increase the chances of the Ping-Pong HO effect. Specifically, it considers Quality of Experience (QoE) by applying an optimised HO algorithm for WLANs, which relies on Fuzzy Logic Control Theory (FLCT) combined with Adaptive Hysteresis Values (AHVs). SDWN allows to monitor and manage the networks and to autonomously programme the APs through a centralized controller. The paper includes also a detailed performance analysis of the algorithm developed in an SDWN-based simulator implemented through OPNET. Specifically, our algorithm achieved promising performance results compared to the state of the art in terms of QoE, throughput, delay and reduction of the ping-pong HO effect.

Performance analysis of in-band collision detection for dense wireless networks

With the massive growth of wireless networks comes a bigger impact of collisions and interference, which has a negative effect on throughput and energy efficiency. To deal with this problem, we propose an in-band wireless collision and interference detection scheme based on full-duplex technology. To study its performance, we compare its throughput and energy efficiency with the performance of traditional half-duplex and symmetric in-band full-duplex transmissions. Our analysis considers a realistic protocol and overhead modeling, and a measurement-based self-interference model. Our results indicate that our proposed collision detection scheme can provide significant gains in terms of throughput and energy efficiency in large wireless networks. Moreover, when compared to half-duplex and symmetric full-duplex, our analysis shows that this scheme allows up to 45% more nodes in the network for the same energy consumption per bit. These results suggest that this could be an enabling technology towards efficient, dense wireless networks.

Brain-Inspired Data Transmission in Dense Wireless Network.

In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure and modes of operation of such an ultra-dense network of neurons and glial cells, the authors selected the most worthwhile when planning a dense wireless network. These ideas were captured, modeled in the context of wireless data transmission. The performance of such an approach have been analyzed in two ways, first, the theoretic limits of such an approach has been derived based on the stochastic geometry, in particular—based on the percolation theory. Additionally, computer experiments have been carried out to verify the performance of the proposed transmission schemes in four simulation scenarios. Achieved results showed the prospective improvement of the reliability of the wireless networks while applying proposed bio-inspired solutions and keeping the transmission extremely simple.

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

Packet loss recovery in wireless broadcast is challenging, particularly for real-time applications which have strict and short delivery deadline. To recover the maximum number of lost packets within a short time, existing packet recovery solutions often rely on instantly decodable network coding (IDNC). Some of these solutions can recover nearly the maximum number of lost packets possible at the cost of collecting feedback from all (or a large percentage of) users. This is impractical in dense networks. In addition, their runtime grows with the number of users, which is not desirable due to the urgent delivery deadline of real-time applications. In this work, we introduce random instantly decodable network coding (RIDNC), a random encoding approach to IDNC. We propose RA ndom IDN C E ncoder (RACE), a fast RIDNC encoder that can recover nearly as many lost packets as the optimal RIDNC encoder. We compare RACE with the CrowdWiFi encoder, a high performing packet loss recovery solution used in CrowdWiFi, a commercial system for broadcasting live video in dense networks. We show that RACE is up to two orders of magnitude faster than the CrowdWiFi encoder, and recovers more lost packets in practice, where there is not enough time to collect feedback from many users.

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Cognitive Radio (CR) provides a promising solution to the spectrum scarcity problem in dense wireless networks, where the sensing ability of cognitive users helps acquire knowledge of the environment. However, cognitive users are vulnerable to different types of attacks, due to its shared medium. In particular, jamming is considered as one of the most challenging security threats in CR networks. In jamming, an attacker jams the communication by transmitting a high power noise signal in the vicinity of the targeted node. The jammer could be an intelligent entity that is capable of exploiting the dynamics of the environment. In this work, we provide a machine-learning-based anti-jamming technique for CR networks to avoid a hostile jammer, where both the jamming and anti-jamming processes are formulated based on the Markov game framework. In our framework, secondary users avoid the jammer by maximizing its payoff function using an online, model-free reinforcement learning technique called Q-learning. We consider a realistic mathematical model, where the channel conditions are time-varying and differ from one sub-channel to another, as in practical scenarios. Simulation results show that our proposed approach outperforms existing approaches to combat jamming over a wide range of scenarios.