Ultra-dense Wireless Networks Research Articles

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.

Age-Dependent Distributed MAC for Ultra-Dense Wireless Networks

We consider an ultra-dense wireless network with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$</tex-math> </inline-formula> channels and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M = N$</tex-math> </inline-formula> devices. Messages with fresh information are generated at each device according to a random process and need to be transmitted to an access point. The value of a message decreases as it ages, so each device searches for an idle channel to transmit the message as soon as it can. However, each channel probing is associated with a fixed cost (energy), so a device needs to adapt its probing rate based on the “age” of the message. At each device, the design of the optimal probing strategy can be formulated as an infinite horizon Markov Decision Process (MDP) where the devices compete with each other to find idle channels. While it is natural to view the system as a Bayesian game, it is often intractable to analyze such a system. Thus, we use the Mean Field Game (MFG) approach to analyze the system in a large-system regime, where the number of devices is very large, to understand the structure of the problem and to find efficient probing strategies. We present an analysis based on the MFG perspective. We begin by characterizing the space of valid policies and use this to show the existence of a Mean Field Nash Equilibrium (MFNE) in a constrained set for any general increasing cost functions with diminishing rewards. Further we provide an algorithm for computing the equilibrium for any given device, and the corresponding age-dependent channel probing policy.

Multi-UAV Placement and User Association in Uplink MIMO Ultra-Dense Wireless Networks

This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to the UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. The main goal of the paper is to jointly maximize the number of served terrestrial users and their scheduling. Moreover, the number of UAV servers and their 3D placement are optimized. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. The resulting problem is non-convex and intractable to solve. Therefore, we break the problem into two subproblems. We propose an efficient algorithm based on machine learning to solve the first subproblem, i.e., optimizing the number of UAVs and their 3D placements, and the user association. Different from existing literature, our proposed algorithm can achieve low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula> -norm concept to find a convex upper bound for the subproblem and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until the overall algorithm converges and a near-optimal solution is achieved for the optimization problem. Moreover, the computational complexity of the proposed scheme is analyzed. Finally, we evaluate the performance of our proposed algorithm via the simulation results. Regarding fast convergence and low computational complexity of the proposed algorithm, its superior performance is confirmed through numerical results.

Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

As the Internet of Things (IoT) advances, machine-type devices are densely deployed and massive networks such as ultra-dense networks (UDNs) are formed. Various devices attend to the network to transmit data using machine-type communication (MTC), whereby numerous, various are generated. MTC devices generally have resource constraints and use wireless communication. In this kind of network, data aggregation is a key function to provide transmission efficiency. It can reduce the number of transmitted data in the network, and this leads to energy saving and reducing transmission delays. In order to effectively operate data aggregation in UDNs, it is important to select an aggregation point well. The total number of transmitted data may vary, depending on the aggregation point to which the data are delivered. Therefore, in this paper, we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation scheme. In addition, we evaluate the proposed scheme with extensive computer simulations. Better performances in the proposed scheme are achieved compared to the conventional approach.

Hypergraph-Based Resource Allocation for Ultra-Dense Wireless Network in Industrial IoT

As next generation wireless communication and smart manufacturing evolve, Industrial Internet of Things (IIoT) has emerged. Due to the advantage in coverage and improved capacity of cellular networks, femtocells are densely deployed in macrocell of cellular networks contributing to Ultra-dense Wireless Network (UDWN). Nevertheless, it is faced with the challenge of how to reduce interference to improve spectrum efficiency and system performance for Non-orthogonal Multiple Access (NOMA) in IIoT. To this end, we identify and formulate the channel resource allocation issue as a sum-rate maximization problem under the guarantee of Quality of Service (QoS) and fairness. To solve the problem, we first construct a hypergraph by considering interference relationships. Thereafter, we convert the original problem into a maximum weighted clique set problem in hypergraph theory, based on which a heuristic resource allocation algorithm is proposed. Moreover, we analyse the property of the proposed algorithm and its complexity. Finally, simulation results show that the proposed method outperforms graph-based algorithm and other hypergraph-based algorithm in improving the system throughput.

Joint Interference Alignment and Subchannel Allocation in Ultra-Dense Networks

In this paper, we propose one joint interference alignment and subchannel allocation (JIARA) interference management scheme, aiming at maximizing the number of satisfactory users with different degrees of freedom (DoF) demand in ultra-dense wireless networks, while the existing works only consider the same DoF demands. The original JIARA scheme is equivalent to the maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -colorable subgraph (MKCS) problem in graph theory for a given <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -colors and the multicoloring demand at each vertex, hence NP-hard. Therefore, we propose one graph theory based low complexity sub-optimal JIARA scheme. Firstly, we propose one virtual allocation of subchannel based scheme to construct transformed the conflict graph, which is applicable for the different DoF demands and different size of the feasible IA group. Furthermore, we propose one greedy multi-coloring based algorithm to determine the maximum number of satisfactory users. Simulation results show that the proposed JIARA scheme outperforms the subchannel resource allocation only scheme.

Prospects and Challenges of THz Precoding

Terahertz (THz) communications are considered as very promising for the sixth-generation (6G) ultra-dense wireless networks. However, THz signals suffer from well-known severe path loss, which consequently shortens the coverage of THz communication systems. To deal with this issue, precoding technique is expected to be beneficial to extend the limited coverage by providing directional beams with ultra large number of antenna arrays. In this paper, we overview the state-of-the-art developments of THz precoding techniques such as reconfigurable intelligent surface based precoding, hybrid digital-analog precoding and delay-phase precoding. Based on the survey, we summarize several open issues remaining to be addressed, and discuss the prospects of a few potential research directions on THz precoding, such as one-bit precoding, precoding for hardware impairments and THz security precoding. This overview will be helpful for researchers to study innovative solutions of THz precoding in the future 6G wireless communications.

An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

With the emergence of 5G mobile multimedia services, end users’ demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communication mode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks. One of the important factors which affects the quality-of-service (QoS) of D2D communications is co-channel interference. In order to solve this problem of co-channel interference, this paper proposes a graph coloring based algorithm. The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource. The proposed algorithm also provides simpler power control. The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired. Simulation results show that the proposed algorithm effectively reduced the co-channel interference, power consumption and improved the system throughput as compared with existing algorithms.

Cell Traffic Prediction Based on Convolutional Neural Network for Software-Defined Ultra-Dense Visible Light Communication Networks

With the explosive growth of ubiquitous mobile services and the advent of the 5G era, ultra-dense wireless network (UDN) architectures have entered daily production and life. However, the massive access capacity provided by 5G networks and the dense deployment of micro base stations also bring challenges such as high energy consumption, high maintenance costs, and inflexibility. Fiber-based visible light communication (FVLC) has the advantages of large bandwidth and high speed, which provides an efficient connection option for UDN. Thus, in order to make up for the poor flexibility of UDN, we propose a new FVLC-UDN architecture based on software-defined networks (SDNs). Specifically, SDN decouples the data plane and the control plane of the device and centralizes the control of the LED in the cell through a unified control plane, which can not only improve the resource allocation ability of the network but also transmit the data only as the data plane, reducing the manufacturing and implementation costs of the LED. In order to get a better resource allocation scheme, this paper proposes a model for predicting cell traffic based on convolutional neural networks. By predicting the traffic of each cell in the control domain, the traffic trend and cells’ status in the future period of time in the control domain can be obtained, so that a much more efficient resource allocation scheme can be formulated proactively to reduce energy consumption and balance communication loads. The experimental results show that on the real cell traffic dataset, this method is better than the existing prediction methods when the size of training dataset is limited.

Coverage Analysis and Scaling Laws in Ultra-Dense Networks

In this paper, we develop an innovative approach to quantitatively characterize the performance of ultra-dense wireless networks in a plethora of propagation environments. The proposed framework has the potential of simplifying the cumbersome procedure of analyzing the coverage probability and allowing the unification of single- and multi-antenna networks through compact analytical representations. By harnessing this key feature, we develop a novel statistical machinery to study the scaling laws of wireless networks densification considering general channel power distributions including small-scale fading and shadowing as well as associated beamforming and array gains due to the use of multiple antenna. We further formulate the relationship between network density, antenna height, antenna array seize and carrier frequency showing how the coverage probability can be maintained with ultra-densification. From a system design perspective, we show that, if multiple antenna base stations are deployed at higher frequencies, monotonically increasing the coverage probability by means of ultra-densification is possible, and this without lowering the antenna height. Simulation results substantiate performance trends leveraging network densification and antenna deployment and configuration against path loss models and signal-to-noise plus interference thresholds.

Distributed Channel Assignment for Ultra-Dense Wireless Networks Using Belief Propagation

An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementation issues such as high computational complexity and control signaling issues. In an effort to cope with these challenging issues, we propose a distributed channel assignment algorithm that efficiently finds the optimal channel configuration by utilizing the concept of belief propagation. Based on a message-passing framework, the proposed distributed channel assignment algorithm maximizes the overall sum rate of the ultra-dense network with a low computational load for each network node. In addition, we design a network protocol and frame format to implement the proposed message-passing framework to real-world wireless networks. The main advantage of the proposed approach is that network nodes autonomously determine the optimal channel assignment and rapidly adapt to dynamic changes of the network. Simulation results confirm that the proposed distributed channel assignment algorithm outperforms conventional algorithms in terms of various network performance aspects, such as the sum rate, scalability, latency, and user mobility.

Deep Reinforcement Learning for Trustworthy and Time-Varying Connection Scheduling in a Coupled UAV-Based Femtocaching Architecture

The paper is motivated by the urgent need, imposed by the COVID-19 pandemic, for trustworthy access to secure communication systems with the highest achievable availability and minimum latency. In this regard, we focus on an ultra-dense wireless network consisting of Femto Access Points (FAPs) and Unmanned Aerial Vehicles (UAVs), known as caching nodes, where there are more than one possible caching node to handle user’s request. To efficiently cope with the dynamic topology of wireless networks and time-varying behavior of ground users, our focus is to develop an efficient connection scheduling framework, where ground users are autonomously trained to determine the optimal caching node, i.e., UAV or FAP. Our aim is to minimize users’ access delay by maintaining a trade-off between the energy consumption of UAVs and the occurrence of handovers. To achieve these objectives, we formulate a multi-objective optimization problem and propose the Convolutional Neural Network (CNN) and Q-Network-based Connection Scheduling (CQN-CS) framework. More specifically, to solve the constructed multi-objective connection scheduling problem, a deep Q-Network model is developed as an efficient Reinforcement Learning (RL) approach to train ground users to handle their requests in an optimal and trustworthy fashion within the coupled UAV-based femtocaching network. The effectiveness of the proposed CQN-CS framework is evaluated in terms of the cache-hit ratio, user’s access delay, energy consumption of UAVs, handover, lifetime of the network, and cumulative rewards. Based on the simulation results, the proposed CQN-CS framework illustrates significant performance improvements in companion to Q-learning and Deep Q-Network (DQN) schemes across all the aforementioned aspects.

A Mean Field Game Analysis of Distributed MAC in Ultra-Dense Multichannel Wireless Networks

This report analyzes the performance of distributed Medium Access Control (MAC) protocols in ultra-dense multichannel wireless networks, where $N$ frequency bands (or channels) are shared by $M=mN$ devices, and devices make decisions to probe and then transmit over available frequency bands. While such a system can be formulated as an $M$ -player Bayesian game, it is often infeasible to compute the Nash equilibria of a large-scale system due to the curse of dimensionality . In this report, we exploit the Mean Field Game (MFG) approach and analyze the system in the large population regime ( $N$ tends to $\infty $ and $m$ is a constant). We consider a distributed and low complexity MAC protocol where each device probes $d/k$ channels by following an exponential clock which ticks with rate $k$ when it has a message to transmit, and optimizes the probing strategy to balance throughput and probing cost. We present a comprehensive analysis from the MFG perspective, including the existence and uniqueness of and convergence to the Mean Field Nash Equilibrium and the price of anarchy with respect to the global optimal solution. Our analysis shows that the price of anarchy is at most one half, but is close to zero when the traffic load or the probing cost is low. Our numerical results confirm our analysis and show that the MFNE is a good approximation of the $M$ -player system. Further, this report demonstrates the novelty of MFG analysis, which can be used to study other distributed MAC protocols in ultra-dense wireless networks.

Terahertz band channel properties according to transmit power estimation

Over the last decade, short-range communications in the THz (Terahertz) band have been extensively studied as a technology-enabler for dense and ultra-dense wireless networks. The increasing demand for high data rates for wireless communication systems will inevitably lead to the extension of operation frequencies with larger bandwidths. THz range enables bandwidths on the order or hundreds of GHz. Thus, THz Band communication will alleviate the capacity limitations and spectrum scarcity of current wireless systems, and enable new classical networks and novel nanoscale networks applications. The main aim of this paper is to provide design guidelines for close proximity links with transmission capacity beyond 100 Gbit/s. We present the path loss, absorption loss, signal to noise ratio (SNR) and capacity of propagating electromagnetic waves at THz Band in different air medium types. The channel capacity and SNR properties are calculated according to transmit power estimation. In this paper, five air medium types are investigated which are average latitude—summer, high latitude—summer, average latitude—winter, high latitude—winter and tropics.

User Matching on Blockchain for Computation Offloading in Ultra-Dense Wireless Networks

The popularity of mobile devices has driven the occurrence of ultra-dense wireless networks (UDNs) to accommodate high volumes of data therein generated by mobile users (MUs) running various mobile applications, such as virtual reality/augmented reality (VR/AR) and online gaming. Since many such mobile applications are computation-intensive, mobile edge computing (MEC), a promising computing paradigm that can provide on-demand computation resources (i.e., virtual machines (VMs)), is considered to adopt for allowing MUs to offload their computation tasks to EgSvrs in the proximity. Previous works have developed many schemes to schedule computation tasks to be offloaded to edge servers (EgSvrs). However, these developed schemes are centralized, which are vulnerable to congested coordination operations, and have no trustworthiness guarantee as well. In this paper, we propose to design a decentralized coordination scheme to orchestrate MUs and EgSvrs for scheduling computation tasks to their right VMs by taking advantage of blockchain technology. Specifically, we develop an efficient task-VM matching algorithm that jointly considers task execution time and energy consumption. Particularly, we prove the stability of the task-VM matching achieved by the developed matching algorithm. Besides, we further implement the developed task-VM matching algorithm on the blockchain by developing a smart matching contract to perform task-VM matching on the blockchain without trusted third parties. Extensive simulation results demonstrate that our decentralized coordination scheme can improve the performance significantly and converge to a stable state very quickly.

Interference Management, Backhaul Routing &amp; Switching off in 5G Hetnets with Mmwave Backhaul Links: A Relative Perspective

Today most of the next generation wireless communication systems(5G) faces the challenge of supporting the demands for higher data rates growing exponentially and ensuring to provide a stable quality of service (QoS) throughout the network. Over the next 2 years meeting up these requirements need to maximize the capacity of the network by a factor which will multiply it by thousands. Moreover, the most important societal as well as economical concerns include the power usage of data and communication technology industry and pollution mostly energy-related. On 5G two contrary requirements of providing capacity to support higher cellular networks along with consuming less energy needs to be focused upon. Due to the scarcity of spectral sources, a wide consensus can only be achieved by increasing significantly the number of antennas operating per unit area. Here a heterogeneous network named HetNet is analysed, comprising a macro base station (BS) along with several antennas and an overlaid dense tier possessing SCAs(small cell access points)through a wireless backhaul to get data traffic. The SCAs mostly associate with static as well as low mobility user equipment whereas macro BS serve the medium-to-high mobility. This work analyses the methods of the ultra-dense wireless 5G heterogeneous networks considering the interference management along the mm-Wave backhaul links to utilize the spectrum and network densification to operate mm-Wave 5G HetNet. The study reviews several literature works, their drawbacks and developing a joint model by combining base station switch-off technique with interface mitigation. This study further throws light on the scope of managing the backhaul-aware interference which in turn enhances potential capacity of the system and depending on the suitable backhaul the capacity is improved. Millimetre wave(mm-Wave) has proved to be a remarkable candidate to overcome the issue of ensuring a broad bandwidth having secure link transmission. The ultra-dense heterogeneous networks are discussed along with discussing the role interference management algorithms to minimize energy consumption. The importance of interference management is discussed along with discussing issues related. The research problem is formulated following a comparative analysis.

User-Centric Joint Access-Backhaul Design for Full-Duplex Self-Backhauled Wireless Networks

Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user’s message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.

An adaptive grouping scheme in ultra-dense IEEE 802.11ax network using buffer state report based two-stage mechanism

IEEE 802.11ax, which is an emerging WLAN standard, aims at providing highly efficient communication in ultra-dense wireless networks. However, due to a large number of stations (STAs) in the ultra-dense device deployment scenarios, the potentially high packet collision rate significantly decreases the communication efficiency of WLAN. In this paper, we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report (BSR) based Two-stage Mechanism (BTM). In order to achieve the optimal efficiency of BSR delivery, we analyze the functional relationship between STA number in group and Resource Unit (RU) efficiency. Based on this analysis results, an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM. The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.

Asymptotic Analysis on Diversity Receptions Over Fading Channels With Correlated Shadowing

Ultra-dense wireless networks deploy multiple antennas within the decorrelation distance of lognormal shadowing, making it challenging to perform performance analysis. In this correspondence paper, we study the asymptotic performance of diversity receptions over composite lognormal-X fading channels, where the lognormal shadowing components are correlated. Closed-form asymptotic error rate and outage probability expressions of maximum-ratio combining, equal-gain combining, and selection combining are derived. It is revealed that the correlation between lognormal components only contributes a constant factor to the performance expressions, and does not change the diversity order.

A Non-Stationary Online Learning Approach to Mobility Management

Efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased node densities. We show that optimization-based mobility protocols cannot achieve long-term optimal performance, particularly for ultra-dense networks in a time-varying environment. To address the complex system dynamics, especially the possible change of statistics due to user movement and environment changes, we propose piece-wise stationary online-learning algorithms to learn the varying throughput distribution and solve the frequent handover problem. The proposed MMBD/MMBSW algorithms are proved to achieve sublinear regret performance in finite time horizon and a linear, non-trivial rigorous regret bound for infinite time horizon. We also study the robustness of the MMBD/MMBSW algorithms under delayed or missing feedback. The simulations show that the proposed algorithms can outperform 3GPP protocols with optimal thresholds. More importantly, they are more robust to system dynamics which are commonly present in practical ultra-dense wireless networks.