Edge Users Research Articles

Heterogeneous cyber-physical network coexistence through interference contribution rate and uplink power control algorithm (ICR-UPCA) in 6G edge cells

Optimizing power control for interference mitigation at the network cell edge is pivotal in enhancing capacity within a heterogeneous cyber-physical infrastructure, such as smart cities, manufacturing, healthcare, energy grids, transportation, and agriculture, among others. In this paper, we consider the intricate dynamics of Internet of Things (IoT) 5/6G edge users, with a particular focus on the Interference Contribution Rate (ICR), where macro and femtocells are critical network infrastructures. Existing approaches has drawbacks such as computational complexity, overhead, and co-channel interference, among others. However, to fully address interference challenges from the coexistence of diverse network hierarchies, preserving the Quality of Service for femtocell users is prioritized. The paper concurrently enhances the handoff mechanism of cell edge users in the macro cell network. A two-tier heterogeneous network (HetNet) is utilized to initially assess the contribution of edge user equipment (UE) to interference levels during its active state while quantifying it as ICR. Game theory is used to formulate a cohesive model for the coexistence of macro cell (MUE) and femtocell users (FUE). ICR-based uplink power control and reference signal received quality (RSRQ)-based handoff algorithms are deployed to regulate interference levels and enhance the Signal-to-Interference-Noise Ratio (SINR) of the MUE at the cell edge. This is achieved through coordinated transmit power adjustments by both user types. Results indicate a 6.67 % channel capacity loss (interference tolerance) by the FUE, leading to a 12.5 % improvement, translating to approximately 4 Mbps and 1 Mbps channel enhancements, respectively. The MUE and FUE can effectively coordinate power control with minimal overhead, accepting compromises in network channel quality. This approach facilitates improved MUE data access rates while ensuring the preservation of FUE. We show that interference is successfully mitigated through power control in heterogeneous networks with lower computational complexity.

Computation Offloading and Beamforming Optimization for Energy Minimization in Wireless-Powered IRS-Assisted MEC

Intelligent reflecting surface (IRS) has been recently exploited as a symbiotic radio (SR) technology to improve energy-and spectral-efficiencies in wireless systems. In this paper, we consider a symbiotic IRS-assisted mobile edge computing (MEC) system that allows edge users to first harvest RF power from a hybrid access point (HAP) and then offload its computational workload to the MEC server associated with the HAP. We aim to minimize the HAP’s energy consumption by jointly optimizing the users’ offloading schemes, the HAP’s active beamforming, and the IRS’s passive beamforming strategies. We propose an optimization-driven hierarchical deep deterministic policy gradient (OH-DDPG) framework to decompose the energy minimization problem into the optimization and the learning sub-problems, respectively. The outer-loop DDPG learning method adapts the IRS’s passive beamforming strategy, while the inner-loop optimization deals with the other control variables with reduced dimensionality. Moreover, to improve the learning efficiency, we extend OH-DDPG to the multi-agent scenario. In particular, the HAP first estimates the users’ offloading strategy by the inner-loop optimization and shares it with all user agents. Then, each user agent refines its offloading decision using the DDPG algorithm independently. This can avoid signaling overhead among users and improve the multi-user learning efficiency. Simulation results show that the proposed OH-DDPG and the multi-user extension can achieve significant performance gains compared to the conventional model-free learning algorithms.

Deep learning‐assisted reconfigurable intelligent surface for enhancing 6G mobile networks

AbstractThis article presents the role of Reconfigurable Intelligent Surface (RIS) using the Multi‐Input Multi‐Output (MIMO) technique as the enabling technology to boost the achievable data rate for mobile networks of the sixth generation (6G). The RIS has been adopted to mitigate the interference at the Cell Edge User (CEU) placed where two adjacent cells are separated. That is by reflecting the incident interference signal in the CEU direction out of phase with the basic interference signals coming from the interfering BS towards the CEU. This article adopts an efficient solution for designing the RIS redirecting (reflection) matrix with trivial training overhead using Deep Learning (DL) technology. Whereas a few of the reflecting elements in RIS are chosen to be active (attached to the baseband), whilst the majority are chosen to be passive, in which the active element's channels are known and used as medium indicators and indicate further the positions of the transmitter and receiver. This article illustrated the efficacy of the adopted framework with DL, by changing the training parameters regarding the data rates that can be achieved, the Spectral Energy Efficiency (SEE), and the Satisfaction Rate (SR). As a result, the proposed model improves the achievable data rate by a near average of 97% above the reference model. Furthermore, it enhances the achievable data rate above the baseline model that assumes no RIS used by an average of 115%. Therefore, The DL method demonstrated that the proposed model is promising for enhancing 6G Mobile Networks.

Enhancing QoS Performance for Cell Edge Users Through Adaptive Modulation and Coding in IEEE 802.11ac WLANs

Wireless communication networks, such as IEEE 802.11ac Wireless Local Area Networks (WLANs), often encounter challenges in providing consistent Quality of Service (QoS) to users situated at the cell edge. The inherent variations in channel conditions, particularly lower signal-to-noise ratios (SNRs) in these regions, lead to compromised data rates and reliability, resulting in significant degradation of throughput. This study presents an innovative solution in the form of an Adaptive Modulation and Coding Scheme (AMCS) algorithm tailored to enhance QoS performance for cell edge users. The primary objective of the AMCS algorithm is to optimize QoS by dynamically adjusting the transmission data rate based on the observed channel conditions, quantified using SNR as a channel state indicator. Conventional approaches might unilaterally select the lowest data rate in challenging conditions, prioritizing reliability at the expense of throughput. However, the proposed AMCS algorithm takes a distinct approach by intelligently determining the Modulation and Coding Scheme (MCS) that offers an optimal balance between throughput and reliability for the given SNR level. To achieve this, the algorithm utilizes real-time SNR measurements to select an MCS that ensures a stable connection while also maintaining an acceptable data rate. By adapting the MCS based on the current SNR, the algorithm aims to mitigate the adverse effects of poor channel conditions experienced by cell edge users. The innovation of the AMCS algorithm lies in its ability to make dynamic adjustments, allowing users to experience improved data rates without compromising connection stability. Through extensive simulations and evaluations, the proposed AMCS algorithm showcases its efficacy in enhancing QoS performance at the cell edge. The algorithm's adaptive approach successfully achieves higher data rates and improved reliability by selecting appropriate MCS configurations tailored to the observed SNR levels. This innovative technique provides a promising solution to the challenge of striking the right balance between throughput and reliability in wireless communication networks, ultimately leading to an improved user experience for those at the network's periphery.

Broadband Port-Selective Silicon Beam Scanning Device for Free-Space Optical Communication

Indoor free space optical (FSO) communication technology that provides high-speed connectivity to edge users is expected to be introduced in the near future mobile communication system, where the silicon photonics solid-state beam scanning device is a promising tool because of its low cost, long-term reliability, and other beneficial properties. However, the current two-dimensional beam scanning devices using grating coupler arrays have difficulty in increasing the transmission capacity because of bandwidth regulation. To solve the problem, we have introduced a broadband surface optical coupler, “elephant coupler,” which has great potential for combining wavelength and spatial division multiplexing technologies into the beam scanning device, as an alternative to grating couplers. The prototype port-selective silicon beam scanning device fabricated using a 300 mm CMOS pilot line achieved broadband optical beam emission with a 1 dB-loss bandwidth of 40 nm and demonstrated beam scanning using an imaging lens. The device has also exhibited free-space signal transmission of non-return-to-zero on-off-keying signals at 10 Gbps over a wide wavelength range of 60 nm. In this paper, we present an overview of the developed beam scanning device. Furthermore, the theoretical design guidelines for indoor mobile FSO communication are discussed.

Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.

EdgeMart : A Sustainable Networked OTT Economy on the Wireless Edge for Saving Multimedia IP Bandwidth

With the advent of 5G+ services, it has become increasingly convenient for mobile users to enjoy high-quality multimedia content from CDN driven streaming and catch-up TV services (Netflix, iPlayer) in the (post-) COVID over-the-top (OTT) content rush. To relieve ISP owned fixed-line networks from CDN streamed multimedia traffic, system ideas (e.g., Wi-Stitch in [ 45 ]) have been proposed to (a) leverage 5G services and enable consumers to share cached multimedia content at the edge, and (b) consequently, and more importantly, reduce IP traffic at the core network. Unfortunately, given that contemporary multimedia content might be a monetized asset, these ideas do not take this important fact into account for shared content. We present EdgeMart —a content provider (CP) federated, and computationally sustainable networked (graphical) market economy for paid-sharing of cached licensed (OTT) content with autonomous users of a wireless edge network (WEN). EdgeMart is a unique oligopoly multimedia market (economy) that comprises competing networked sub-markets of non-cooperative content sellers/buyers—each sub-market consisting of a single buyer connected (networked) to only a subset of sellers. We prove that for any WEN-supported supply-demand topology , a pure strategy EdgeMart equilibrium exists that is (a) nearly efficient (in a microeconomic sense) indicating economy sustainability, (b) robust to edge user entry/exit, and (c) can be reached in poly-time (indicating computational sustainability). In addition, we experimentally show that for physical WENs of varying densities, a rationally selfish EdgeMart economy induces similar orders of multimedia IP traffic savings when compared to the ideal (relatively less practical), altruistic , and non-monetized “economy” implemented atop the recently introduced Wi-Stitch WEN-based content trading architecture. Moreover, the EdgeMart concept helps envision a regulated edge economy of opportunistic (pay per licensed file) client services for commercial OTT platforms.

Secrecy Rate Maximization in THz-Aided Heterogeneous Networks: A Deep Reinforcement Learning Approach

Densely deployed femtocells in heterogeneous networks (HetNets) improve the quality-of-service (QoS) and quality-of-experience (QoE) for both next-generation networks and end users, respectively. However, users near the femtocell edge, i.e., femto edge users (FEUs), which are far away from the Femto base station (FBS) may receive a low signal-to-interference-plus-noise ratio (SINR). Moreover, due to the distributed and dynamic nature of HetNets, femtocells may not resist various eavesdropping attacks from different attackers. Thus, it is essential to ensure the wireless channel's secrecy through which FEUs communicate with FBS since they may face eavesdropping attacks and may have low SINR and achievable data rates. Aiming to enhance the secrecy rate of femtocells, in this article, we formulate a joint optimization problem of beamforming vectors, artificial noise (AN), and power control for terahertz (THz)-enabled femtocells. Addressing this non-convex optimization problem is challenging due to the system's complexity and dynamic nature of FEUs. Thus, we translate the optimization problem into a multi-agent reinforcement learning (MARL) problem using the Markov decision process (MDP). Then, to solve the MDP with continuous action space, two deep reinforcement learning (DRL) based schemes, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Deterministic Policy Gradient-based Secrecy Maximization (DDPG-SM)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Asynchronous-Advantage-Actor-Critic based Secrecy Maximization (A3C-SM)</i> are proposed to maximize the secrecy rate of femtocells. The secrecy rate performance of the proposed schemes is compared and analyzed with Advantage-Actor-Critic (A2C) and state-of-the-art Perfect Secrecy Rate Maximization (PSRM) schemes. Simulation results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A3C-SM</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDPG-SM</i> schemes achieve 37.73% and 22.64% better average secrecy rate in comparison to the PSRM scheme. Also, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A3C-SM</i> scheme outperforms all other <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDPG-SM</i> , A2C, and PSRM schemes in terms of average secrecy rate, received SINR, and energy-efficiency of femtocells.

Stochastic Geometry-Based Evaluation of Edge User QoS With Cooperative Allocation

Long Term Evolution (LTE) frequency bands are being considered for use in 5G due to their coverage advantage. However, LTE usage is expected to be non-negligible for a while, so the amount of spectrum resources for it should be carefully estimated. In this letter, we propose a criterion for evaluating the adequacy of LTE spectrum resources in terms of edge user Quality of Service (QoS). For resource allocation to the edge user, we consider cooperative schemes that reflects the joint resource utilization of the associated and neighbor base stations. Based on stochastic geometry model, we derive semi-closed expressions of lower and upper bounds for the ergodic spectral efficiency at the edge user and devise a QoS satisfaction evaluation method through the decision region determined from them. Numerical analysis validates our derivations and shows the effect of joint resource utilization and cooperative allocations on QoS satisfaction.

Massive MIMO NOMA: Double-Mode Model towards Green 5G Networks.

With the development of the Internet of Things (IoT), the number of devices will also increase tremendously. However, we need more wireless communication resources. It has been shown in the literature that non-orthogonal multiple access (NOMA) offers high multiplexing gains due to the simultaneous transfer of signals, and massive multiple-input-multiple-outputs (mMIMOs) offer high spectrum efficiency due to the high antenna gain and high multiplexing gains. Therefore, a downlink mMIMO NOMA cooperative system is considered in this paper. The users at the cell edge in 5G cellular system generally suffer from poor signal quality as they are far away from the BS and expend high battery power to decode the signals superimposed through NOMA. Thus, this paper uses a cooperative relay system and proposes the mMIMO NOMA double-mode model to reduce battery expenditure and increase the cell edge user's energy efficiency and sum rate. In the mMIMO NOMA double-mode model, two modes of operation are defined. Depending on the relay's battery level, these modes are chosen to utilize the system's energy efficiency. Comprehensive numerical results show the improvement in the proposed system's average sum rate and average energy efficiency compared with a conventional system. In a cooperative NOMA system, the base station (BS) transmits a signal to a relay, and the relay forwards the signal to a cluster of users. This cluster formation depends on the user positions and geographical restrictions concerning the relay equipment. Therefore, it is vital to form user clusters for efficient and simultaneous transmission. This paper also presents a novel method for efficient cluster formation.

Intelligent Video Streaming at Network Edge: An Attention-Based Multiagent Reinforcement Learning Solution

Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments.

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System.

The fifth generation (5G) marks an important advance in mobile network capabilities. When it comes to high data rates, capacity, spectrum efficiency, and availability, 5G mobile broadband goes above and beyond what was previously possible with standard mobile broadband. The construction of 5G networks is still in the planning stages. These 5G networks will create intelligent networked communication environments by connecting people, things, data, applications, and transport networks. Mobile networks have made it possible for customers' mobile devices (such as smartphones, tablets, laptops, and so on) to connect to the internet. A variety of distinct protocols will be required to take into consideration the various aspects that 5G possess. One of these is the transport protocol, which is intended to deliver extremely high data transfer rates up to 400 Gbps. The transmission control protocol (TCP) is one of the numerous protocols that are necessary for supporting 5G's many capabilities. Our work focuses on the detection and analysis, on the downlink (DL) side, of the congestion of the transport layer in single- and multicell environments. For the purpose of the analysis, the following metrics were analyzed: physical resource blocks (PRBs), user throughput, cell throughput, cell edge user throughput, and delay. The work emphasizes the activation of the TCP slow-start algorithm using file transfer protocol (FTP) model two according to 3GPP standards.

CoMP-Enhanced Flexible Functional Split for Mixed Services in Beyond 5G Wireless Networks

With explosively escalating service demands, beyond fifth generation (B5G) aims to realize various requirements for multi-service networks, i.e., higher performance of mixed enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services than 5G. To flexibly serve diverse traffic, various functional split options (FSOs) are specified by 5G protocols enabling different network functions. In order to improve signal qualities for edge users, we consider FSO-based coordinated multi-point (CoMP) transmission as a prominent technique capable of supporting high traffic demands. However, due to conventional confined hardware processing capability, a processor sharing (PS) model is introduced to deal with high latency for multi-service FSO-based networks. Therefore, it becomes essential to assign CoMP-enhanced functional split modes under PS model. A more tractable FSO-based network in terms of ergodic rate and reliability is derived by stochastic geometry approach. Moreover, we have proposed CoMP-enhanced functional split mode allocation (CFSMA) scheme to adaptively assign FSOs to provide enhanced mixed throughput and latency-aware services. The simulation results have validated analytical derivation and demonstrated that the proposed CFSMA scheme optimizes system spectrum efficiency while guaranteeing stringent latency requirement. The proposed CFSMA scheme with the designed PS FFS-CoMP system outperforms the benchmarks of conventional FCFS scheduling, non-FSO network, fixed FSOs, and limited available FSO selections in open literature.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%.

Computation bits maximization in data compression-assisted MEC networks

In this article, we study the weighted sum computation bits (WSCB) maximization for a multiuser mobile edge computing (MEC) system while considering data compression at each edge user (EU). Considering the partial offloading scheme and data compression at each EU, we formulate a WSCB maximization problem by jointly optimizing the compression time, the computing and compression frequencies of all the EUs as well as the transmit power and time of EUs when performing task offloading. Since the formulated problem is non-convex and difficult to solve, we first replace the original problem with an equivalent one by using the proof by contradiction. Subsequently we introduce auxiliary variables for transforming the equivalent problem into a convex one that can be optimally solved by using the existing convex tools. Further, we devise the closed-form expressions for the optimal transmit power, compression and computing frequencies at each EU by using Karush-Kuhn–Tucker (KKT) conditions. Simulation results verify that our proposed scheme is better than several baseline schemes from the WSCB perspective.

Improved almost blank subframe technology with cooperative interference suppression

AbstractAlmost blank subframe (ABS) is a classic and efficient technology to cancel interference in cellular networks and could be applied in future networks such as ultra‐density network. However, the existence of ABS will result in the deterioration of the communication capacity of the macro base station (BS), particularly for applications with large number of edge users. To solve this problem, an improved ABS technology with an interference cancelation scheme is proposed. For the considered scenario, the macro BS and the micro BS work in a cooperative manner, where the signal transmitted by the macro BS interferes with the communication between the micro BS and its edge terminals. At the terminal side, both the nonlinearity of the macro BS transmitter and the propagation model between the edge user and the macro BS are estimated using neural networks (NNs), which are then sent to the micro BS for interference elimination through the ABS. At the micro BS side, firstly, the nonlinearity and channel parameters from the ABS frame are used to generate a cancelation signal. Secondly, the cancelation signal is sent from the micro BS to the edge user to eliminate the disturbance from the macro BS. This enhanced technology can alleviate the disturbance for the edge user. Meanwhile, since it massively decreases the occupancy of ABS in the macro BS, the capacity of the macro cell can be improved.

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.

Social-Aware Graph-Based Collaborative Caching in Edge-User Networks

The soaring data traffic brings tremendous pressure on core networks. Collaborative caching among edge servers and user devices has been regarded as a promising technology for releasing backhaul pressure and reducing content download delay in the next-generation communication network. However, given the capacity constraint of edge servers and caching devices, a challenge is how to exploit these storage resources efficiently. In this paper, we introduce a three-tier collaborative caching architecture for edge-user networks and propose a social-aware graph-based collaborative caching (SGCC) strategy to minimize content download delay. Specifically, the small base station tier and user equipment tier are modeled as a one-tier directed graph. Moreover, we model the physical attributes and social attributes as the weight of the directed edges in the physical link graph and social graph, respectively. Then, we construct a social-aware graph by merging the two-tier topology of the physical link graph and social graph to simplify the caching decision. Further, we design SGCC to cache the most popular content according to the weighted content popularity. Simulation results demonstrate that the proposed SGCC can effectively reduce the average download delay and has a 26.8% lower average download delay, compared with the uniform popular caching baseline.

Efficient Radio Resource Management for Future 6G Mobile Networks: A Cell-Less Approach

Existing mobile communication systems are unable to support ultra high system capacity and high reliability for the edge users of future 6G systems, which are envisioned to guarantee the desired quality of experience. Recently, cell-less radio access networks (RAN) are exploited to boost the system capacity. Therefore, in this letter we propose a cell-less networking approach with an efficient radio resource optimization mechanism to improve the system capacity of the future 6G networks. The simulation results illustrate that the proposed cell-less NG-RAN design provides significant system capacity improvement over the legacy cellular solutions.

Impact of Multi-Connectivity on Channel Capacity and Outage Probability in Wireless Networks

Multi-connectivity facilitates higher throughput, shorter delay, and lower outage probability for a user in a wireless network. Considering these promises, a rationale policy for a network operator would be to implement multi-connectivity for all of its users. In this paper, we investigate whether the promises of multi-connectivity also hold in such a setting where all users of a network are connected through multiple links. In particular, we consider a network where every user connects to its k closest base stations. Using a framework of stochastic geometry and probability theory, we obtain analytic expressions for per-user throughput and outage probability of $k$-connectivity networks under several failure models. In contrast to the conclusions of previous research, our analysis shows that per-user throughput decreases with increasing k. However, multi-connected networks are more resilient against failures than single connected networks as reflected with lower outage probability and lead to higher fairness among the users. Consequently, we conclude that rather than implementing multi-connectivity for all users, a network operator should consider it for its users who would benefit from additional links the most, e.g., cell edge users.