Dense Wireless Networks Research Articles

A MUD-Enhanced Multi-Beam Approach for Increasing Throughput of Dense Wireless Networks

Uncoordinated random access (URA) wireless communication schemes provide an attractive framework to enable the necessary communication backbone for the advancement of dense sensor systems, many sensors deployed in a relatively small area. By itself, URA is unsuitable for moderately dense networks due to the crippling levels of interference that result from within the communication network. Digital beamforming has been proposed to alleviate intra-URA-network interference with a post-reception formation of multiple simultaneous beams that allow a single receiver to “hear” only the transmissions meant for it and to block out all others. A high density network can result in interference originating from the same direction which cannot be mitigated with beamforming. We propose the addition of post-beamformed multiuser detection (MUD) with the use of direct sequence spread spectrum (DSSS) waveforms to help further combat the interference problem. Motivated by practical design choices such as transceiver weight and size as well as operational expectations of variable and/or evolving node densities, we explore trades between the DSSS time-spreading factor, number of antennas per node, and network node density. Our results show throughput improvements with the addition of MUD, while the most improvements are with the addition of MUD paired with an appropriate choice of spreading factor. Insights from these results provide guidelines for the development of future systems not only aided by MUD and beamforming, but also by on-the-fly adaptation of the DSSS signature waveform time-spreading factor to mitigate the level of interference present at any time.

Optical backbone and control of attocell network

For serving futuristic applications like distributed robotic systems with robots equipped with humanoid intelligence, wireless access to high-performance computing resources should be provided. For this purpose, a very dense wireless network, referred to as attocell network, consisting of radio cells typically less than 1 m$$^{2}$$, and antennas integrated in the floor tiles has been proposed. To serve these small cells, we propose several architectures for the backbone network using radio over fiber-based technology. In addition, we propose a MAC protocol based on simple polling approach to compute the splitting ratio for various cell sizes based on the network load for a delay requirement of 10 $$\upmu \hbox {s}$$. We propose an analytical model for the same to validate the results obtained from the simulation. The cost analysis of these architectures is also presented. Based on this, we give some general design recommendations of an attocell network.

Association stability and handoff latency tradeoff in dense IEEE 802.11 networks: A case study

Association instability is a common phenomenon in dense networks. The decision of whether or not to perform a handoff between access points in an infrastructured IEEE 802.11 network is taken exclusively by the wireless client stations. Even without mobility, static client devices may decide to migrate to another access point with the goal of improving performance. However, the criteria used to perform handoffs are not defined by the IEEE 802.11 standard and, thus, are dependent on specific vendor implementations. In this paper, we use data from a real large scale network and run experiments to demonstrate that such implementations are commonly deficient, resulting in high levels of association instability in dense environments. By analyzing the implementation used by the most common devices and conducting comparative tests in a real network, we were able to conclude that this instability, known as the “ping-pong effect”, results from the direct usage of RSSI samples which are highly variable. Also, we conclude that, despite being effective, common solutions for the ping-pong effect can cause additional delay to the handoff process, which is undesirable. Finally, we analyze the behavior of RSSI in indoor environments showing that its time series presents multimodal distribution. We argue that the findings presented in this study can help develop more stable handoff algorithms for dense wireless networks.

On the Performance of Cluster-Based MIMO-NOMA in Multi-Cell Dense Networks

This paper develops an analytical framework for exploring the benefits of applying cluster-based multi-antenna non-orthogonal multiple access (NOMA) in dense wireless networks. Using the tools of stochastic geometry, a new explicit expression and a tight approximation for per-cluster average data rates are derived in terms of relevant system parameters. Moreover, simulation results are provided to validate the accuracy of our analytical results and draw some essential system design insights. Based on the tractable expressions, we further consider the analysis and optimization of area spectral efficiency (ASE). It is analytically demonstrated that: 1) In terms of the per-cluster average data rate, there exists a wide range for the power allocation coefficient within a cluster where NOMA outperforms orthogonal multiple access (OMA); 2) The per-cluster average data rate of NOMA benefits more from the additional deployment of antennas at base stations (BSs) than that of OMA when the accuracy of channel state information at transmitter (CSIT) is fixed; 3) Regarding to ASE, there exists an optimal combination of the number of clusters and power allocation coefficient to maximize ASE. The performance gain of NOMA relative to OMA becomes marginal when the requirement of user fairness is stringent; 4) As for a special case that the power allocation coefficient is fixed, the analytical results indicate that ASE scales linearly with the number of antennas when the number of clusters is set optimally.

Physical layer security in shotgun cellular systems over correlated/independent shadow fading channels

The shotgun cellular systems (SCSs) (modeling, generally, a dense cellular or wireless data network deployment and non-ideal regular hexagonal grid with site-acquisition difficulties, variable traffic load) are wireless communication systems with randomly placed base stations (BSs) over the entire plane according to a stochastic (e.g., Poisson) point process in n dimensions (n = 1, 2, and 3). Due to the good performance and the importance of the SCSs in today’s communications, the physical layer security of these systems is practically of importance, specifically, in more realistic channel propagation models. In this paper, by assuming that the channel coefficients are known at the receiver, we analyze the performance of the physical layer security for the classic Wyner’s three-node model in the SCSs over correlated/independent log-normal shadow fading channels. For this purpose, first, we calculate the signal-to-interference-plus-noise ratio (SINR) performance at a mobile station (MS) in an SCS. Then, the average secrecy capacity (ASC), the probability of non-zero secrecy capacity (PNSC), the secure outage probability (SOP), and the lower bound of SOP (LSOP) are obtained, and also, their approximated closed-form expressions are derived. Finally, analytical results are validated numerically.

Energy-Efficient Distributed Transmission Scheme for MTC in Dense Wireless Networks: A Mean-Field Approach

In this article, we address the problem of long-term resource allocation for a 5G and beyond dense wireless network. Of particular interest within the considered framework are machine-type communications (MTCs). To establish an optimal operation for an energy-efficient MTC system, the power allocation policy should be designed by taking into consideration both channel and queue states of the devices. This problem formulation belongs to the category of complex stochastic optimization that can be recast as a Markov decision process (MDP) over high-dimensional state space. We circumvent the hurdle inherent to state space of a huge dimension by resorting to mean-field approximation on the MDP. More specifically, we demonstrate that the formulated MDP converges to a deterministic control problem provided that the number of devices approaches infinity. We develop a low-complexity power allocation scheme to efficiently solve the original high-dimensional state space stochastic optimization problem. Our proposed power allocation policy is drawn from the solution of the Hamilton–Jacobi–Bellman equation. It can be understood as a threshold-based policy and can be implemented in a decentralized manner that makes it very appealing for high scale networks. Finally, the simulations analyses are provided to establish the effectiveness of our proposed framework.

Delay and Power Tradeoff With Consideration of Caching Capabilities in Dense Wireless Networks

Enabling caching capabilities in dense small cell networks (DSCNs) has a direct impact on file delivery delay and power consumption. Most existing work studied these two performance metrics separately in cache-enabled DSCNs. However, file delivery delay and power consumption are coupled with each other and cannot be minimized simultaneously. In this paper, we investigate the optimal tradoff between these two performance metrics. Firstly, we formulate the joint file delivery delay and power consumption optimization (JDPO) problem where power control, user association and file placement are jointly considered. Then we convert it to a form that can be handled by Generalized Benders Decomposition (GDB). with GDB, we decompose the converted JDPO problem into two smaller problems, i.e., primal problem related to power control and master problem related to user association and file placement. An iterative algorithm is proposed and proved to be $\epsilon$-optimal, in which the primal problem and master problem are solved iteratively to approach the optimal solution. To further reduce the complexity of the master problem, an accelerated algorithm based on semi-definite relaxation is proposed. Finally, the simulation results demonstrate that the proposed algorithm can approach the optimal tradeoff between file delivery delay and power consumption.

Dense Wireless Network Inspired by the Nervous System

This work presents concepts of the use of algorithms inspired by the functions and properties of the nervous system in dense wireless networks. In particular, selected features of the brain consisting of a large number of nerve connections were analyzed, which is why they are a good model for a dense network. In addition, the action of a selected cells from the nervous system (such as neuron, microglia or astrocyte) as well as phenomena observed in it (e.g. neuroplasticity) are presented.

CA-CWA: Channel-Aware Contention Window Adaption in IEEE 802.11ah for Soft Real-Time Industrial Applications.

In 2016, the IEEE task group ah (TGah) released a new standard called IEEE 802.11ah, and industrial Internet of Things (IoT) is one of its typical use cases. The restricted access window (RAW) is one of the core MAC mechanisms of IEEE 802.11ah, which aims to address the collision problem in the dense wireless networks. However, in each RAW period, stations still need to contend for the channel by Distributed Coordination Function and Enhanced Distributed Channel Access (DCF/EDCA), which cannot meet the real-time requirements of most industrial applications. In this paper, we propose a channel-aware contention window adaption (CA-CWA) algorithm. The algorithm dynamically adapts the contention window based on the channel status with an external interference discrimination ability, and improves the real-time performance of the IEEE 802.11ah. To validate the real-time performance of CA-CWA, we compared CA-CWA with two other backoff algorithms with an NS-3 simulator. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11ah.

Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

One of the key applications for the Internet of Things (IoT) is the eHealth service that targets sustaining patient health information in digital environments, such as the Internet cloud with the help of advanced communication technologies. In eHealth systems, wireless networks, such as wireless local area networks (WLAN), wireless body sensor networks (WBSN), and wireless medical sensor networks (WMSNs), are prominent technologies for early diagnosis and effective cures. The next generation of these wireless networks for IoT-based eHealth services is expected to confront densely deployed sensor environments and radically new applications. To satisfy the diverse requirements of such dense IoT-based eHealth systems, WLANs will have to face the challenge of assisting medium access control (MAC) layer channel access in intelligent adaptive learning and decision-making. Machine learning (ML) offers services as a promising machine intelligence tool for wireless-enabled IoT devices. It is anticipated that upcoming IoT-based eHealth systems will independently access the most desired channel resources with the assistance of sophisticated wireless channel condition inference. Therefore, in this study, we briefly review the fundamental models of ML and discuss their employment in the persuasive applications of IoT-based systems. Furthermore, we propose Q-learning (QL) that is one of the reinforcement learning (RL) paradigms as the future ML paradigm for MAC layer channel access in next-generation dense WLANs for IoT-based eHealth systems. Our goal is to contribute to refining the motivation, problem formulation, and methodology of powerful ML algorithms for MAC layer channel access in the framework of future dense WLANs. This paper also presents a case study of next-generation WLAN IEEE 802.11ax that utilizes the QL algorithm for intelligent MAC layer channel access. The proposed QL-based algorithm optimizes the performance of WLAN, especially for densely deployed devices environment.

Spatial Deep Learning for Wireless Scheduling

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers, due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks, and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.

Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks

This paper presents developments in the area of brain-inspired wireless communications relied upon in dense wireless networks. Classic approaches to network design are complemented, firstly, by the neuroplasticity feature enabling to add the learning ability to the network. Secondly, the microglia ability enabling to repair a network with damaged neurons is considered. When combined, these two functionalities guarantee a certain level of fault-tolerance and self-repair of the network. This work is inspired primarily by observations of extremely energy efficient functions of the brain, and of the role that microglia cells play in the active immune defense system. The concept is verified by computer simulations, where messages are transferred through a dense wireless network based on the assumption of minimized energy consumption. Simulation encompasses three different network topologies which show the impact that the location of microglia nodes and their quantity exerts on network performance. Based on the results achieved, some algorithm improvements and potential future work directions have been identified.

A Radio Propagation Model for Dense Wireless Networks

This paper proposes a model of radio frequency signal propagation in the free space that can be used not only in the far field region of the transmitting antenna like the Friis propagation model for large scale fading, but it can also be employed when the antennas are very close to each other. The modeling starts from a transfer function of the exact two-port network model used to represent the coupling between two antennas in free space. The Z parameter network of the system is used to model the power attenuation as function of the distance between the antennas. Subsequently, a new mathematical model is proposed and compared with the Z parameter network model and shown to have small mean squared error. Such model has the advantage of replacing the Friis formula without the restriction to avoid measuring signals in the near field of the antenna. Furthermore, some practical experiments are carried out in an open field environment and the results are compared. The validity of the new model is demonstrated which can be used for any distance between the antennas and, therefore, it can also be employed to model dense wireless networks.

A Game-Theoretic Learning Approach for Anti-Jamming Dynamic Spectrum Access in Dense Wireless Networks

In this paper, we investigate the anti-jamming channel selection problem for interference mitigation (IM) based dense wireless networks in dynamic environment, in which the active user set is variable due to their specific traffic demands. We jointly consider the mutual interference among users and external jamming in IM-based dense wireless networks, and propose a generalized maximum protocol interference and jamming model to accurately capture the mutual interference and external jamming. Then, the anti-jamming channel selection problem is formulated as an anti-jamming dynamic game, and subsequently it is proved to be an exact potential game, which has at least one pure strategy Nash equilibrium (NE). Based on the stochastic learning theory, a distributed anti-jamming channel selection algorithm (DACSA) is proposed to find the NE solution. Moreover, the simulation results are presented to demonstrate the effectiveness of the proposed DACSA algorithm.

A Group Cooperative Coding Model for Dense Wireless Networks

Generally, node groups in dense wireless networks (WNs) often pose the problem of communication between the central node and the rest of the nodes in a group. Adaptive Network Coded Cooperation (ANCC) for wireless central node networks adapts precisely to an extensive and dense WN, at this level the random linear network coding (RLNC) scheme with Low-Density Parity-Check (LDPC) are used as the essential ANCC evolution. This paper suggests two phases effective technique then studies the influence of the randomly chosen number of coded symbols which are correctly received in the second phase on bit error rate (BER). The proposed technique also focuses on the role of dispersion impact related to the LDPC code generating matrix. The results of the simulation allow selecting the best compromise for the best BER.

Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks

Offloading computation to a mobile cloud is a promising solution to augment the computation capabilities of mobile devices. In this paper, we consider selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations through multiple access points or through the base station to a mobile cloud so as to minimize their computation costs. We provide a game theoretical analysis of the problem, prove the existence of pure strategy Nash equilibria, and provide an efficient decentralized algorithm for computing an equilibrium. For the case when the cloud computing resources scale with the number of mobile devices, we show that all improvement paths are finite. Furthermore, we provide an upper bound on the price of anarchy of the game, which serves as an upper bound on the approximation ratio of the proposed decentralized algorithms. We use simulations to evaluate the time complexity of computing Nash equilibria and to provide insights into the price of anarchy of the game under realistic scenarios. Our results show that the equilibrium cost may be close to optimal, and the convergence time is almost linear in the number of mobile devices.

Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.

Distributed Time and Carrier Frequency Synchronization for Dense Wireless Networks

Dense networks call for interconnectivity of a large number of devices, and in this context, the diversity of interconnected devices introduces challenges to be considered for management and coordination of the network. Synchronization of timing (TO) and carrier frequency offsets (CFO) are critical aspects to be considered to guarantee the proper network interconnectivity. This paper addresses a distributed synchronization method for dense (say $\geq$ 50 nodes), compact, and possibly strongly connected wireless network where the internode distance is small and propagation delay of the entire network is not negligible compared to the inverse of signal bandwidth. Signatures are periodically transmitted by every node to mark the start of the payload and to broadcast the synchronization state that is iteratively minimized by all nodes in aligning jointly the TO/CFO skew with the consensus-type algorithms. Here, the same signature is broadcasted by every node and it collides with those of the others to perform as a reference (compound) signature for receiving nodes that embeds the reference TO/CFO of the network for synchronization. In view to make distributed TO/CFO synchronization practicable, this paper aims to: first, compensate the propagation delays between nodes to mitigate their impact on the global TO/CFO synchronization of the network; second, design and validate chirplike signatures based on Zadoff–Chu sequences that enable the estimate of the TO and CFO mismatch on every node and decouple TO and CFO estimates; and finally, evaluate the mean-square convergence conditions for nodes employing realistic and unstable oscillators. Numerical validations are based on TO/CFO synchronization for orthogonal frequency division multiplexing (OFDM) system.

Gaussian message passing-based cooperative localization with node selection scheme in wireless networks

Abstract Cooperative localization is an attractive method to improve both the coverage and accuracy of the positioning systems in GNSS-challenged environments. However, as the number of agents increases, the computational complexity and communication overhead increase dramatically, which are two main bottlenecks of its application in a practical system. In this paper, we focus on reducing the system overhead of the message passing-based cooperative localization algorithm in dense wireless networks. Weighted samples are used to represent the salient characteristics of the local message, and a Gaussian parametric message passing rule is designed to reduce the burden of the network traffic. A relative spatial relationship between the target and its neighbor anchor nodes is proposed to concentrate the samples where the messages have significant mass. Based on the equivalent Fisher information matrix, a node selection scheme is put forward to refine the most contributing link combination for the position. Then, an efficient message calculation method which exploits the Taylor expansion to reduce the system overhead is deduced. The convergence property of the proposed algorithm is further analyzed. Simulation results show that the proposed algorithm leads to excellent performance at the communication overhead and computational complexity, with small losses in localization accuracy.

Optimizing MDS Coded Caching in Wireless Networks With Device-to-Device Communication

We consider the caching of content in the mobile devices in a dense wireless network using maximum distance separable (MDS) codes. We focus on an area, served by a base station (BS), where mobile devices move around according to a random mobility model. Users requesting a particular file download the coded packets from caching devices within a communication range using device-to-device communication. If additional packets are required to decode the file, these are downloaded from the BS. We analyze the device mobility and derive a good approximation of the distribution of caching devices within the communication range of mobile devices at any given time. We then optimize the MDS codes to minimize the network load under a cache size constraint and show that using optimized MDS codes results in significantly lower network load compared to when caching the most popular files. We further show, numerically, that caching coded packets of each file on all mobile devices, i.e., maximal spreading, is optimal.