Significant advances in wireless network technologies have given rise to use cases with unprecedented data rates, number of devices as well as low-latency constraints that thrive on such agility of networks. However, it also means that network management through appropriate resource allocation, coverage, and continuity of connection has become significantly more challenging compared to previous generations of networks. More recently, disaggregated network architectures have received increased attention through virtualization of important network functions and through the promise of significant value creation leveraging data-driven intelligent network control policies. In order to leverage the benefits of those versatile network technologies it is imperative that a fine balance be maintained between the resources allocated to a given set of users and the number of users that gain access to the network through intelligent admission control (AC). In this work, we propose and evaluate an admission control policy that leverages data-driven optimization to admit the maximum number of users while being acutely aware of quality of service (QoS) constraints of the users. This novel approach is achieved within the emerging O-RAN framework leveraging RAN Intelligent Controllers (RICs) to recommend appropriate policies enabling operators to maintain an efficient operating network. Our simulation results show that by intelligent tradeoff between the QoS and incoming UE request rejections, we can reduce the rejections by 2-3x in congested networks while maintaining acceptable QoS.

With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. This paper addresses this need by proposing an intelligent, demand-driven, elastic user-centric cloud radio access network (UCRAN) architecture capable of providing services to a diverse set of use cases ranging from augmented/virtual reality to high-speed rails to industrial robots to E-health applications, and more. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous throughput and latency requirements. Finally, numerical results are presented to validate the convergence and network adaptability of the proposed approach against the brute-force method.

The user-centric ultra-dense networks (UUDNs) confront the challenge of performance degradation because of the erroneous user equipment (UE), and data base station (DBS) positions estimated at the central controller (CC). This paper adopts the database aided approach to quantify the error impact on system-level key performance indicators (KPIs) under various configuration and optimization parameters (COPs). Although the performance fall is consistent with the increase in error radius of both UEs and DBS positions, its impact can be alleviated by extrapolating on the erroneous database and adopting to new COP values. To realize this, time-series (TS) forecasting is utilized to determine the extent of compensation and COP variations. Results compared for two TS based schemes show that a significant portion, more than 50%, of the decreased performance can be recovered by the suggested adoption in the COP values.

Abstract Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth‐generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase‐shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio‐frequency (RF) chains makes RIS an energy‐efficient and cost‐effective solution for future wireless networks. In this paper, the state‐of‐the‐art research on different aspects of RIS‐assisted communication is explored. Specifically, the fundamentals of RIS are first introduced, including the RIS's structure, operating principle, and deployment strategies. The emerging applications of RISs are then comprehensively discussed for 6G wireless networks. In addition, the crucial challenges for RIS‐assisted networks are elaborated, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase‐shift optimization, and resource allocation in RIS‐assisted networks are presented. Finally, to provide effective guidance for future research, important research directions for realizing RIS‐assisted network are highlighted.

Complexity is an inherent property in wireless heterogeneous networks (HetNets). In this paper, we investigate the application of the agent-based modeling (ABM) tool for optimization of complex and dynamic HetNets. The proposed framework contains a diversity of game-theoretic, machine learning, and rule-based algorithms within the same model. We present and analyze a HetNet ABM model that runs parallel reinforcement learning (RL) algorithms for spectrum deployment, interference management, resource allocation, and load balancing at both micro and macrocell levels. In our proposed model, two RL-based algorithms work jointly to manage the co-tier and cross-tier interferences. The macrocell runs the first algorithm to control the transmission power of the small cells. The second RL algorithm is run by small cells to assign the users to the sub-bands with less interference levels. Simultaneously, the user association is decided by the users depending on the available resources at the cells and user preferences. The model is then evaluated under various network load conditions to deduce relationships between the cell loads, aggregate bit rate, latency, and user association. Moreover, the system is assessed in a dynamic network scenario with moving users and is confirmed to possess the ability to attain convergence with sufficient performance levels.

It is well known that current reactive network management would be unable to support the exponential increase in complexity and rapidity of change in future cellular networks. Keeping this in perspective, the goal of this paper is to investigate applicability of machine learning and predictive models to assess cell-level user quality of experience (QoE) in real-time. For this purpose, we leverage a 5 week LTE metrics data collected at cell level granularity for a national LTE network operator. Domain knowledge is applied to assess user QoE with network key performance indicators (KPIs), namely scheduled user throughput, inter-frequency handover success rate and intra-frequency handover success rate. Results indicate that applying boosted trees model on a subset of carefully selected non-collinear features allows high accuracy threshold-based estimation of user throughput and inter-frequency handover success rate. We also exploit the periodic nature of cell data characteristics and apply a recently developed time series prediction model known as PROPHET for future QoE estimation. By employing machine learning and data analytics on network data within an end-to-end framework, network operators can proactively identify low performance cell sites along with the influential factors that impact the cell performance. Based on the root cause analysis, appropriate corrective measures may then be taken for low performance cell sites.

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes.

Ambitious targets for aggregate throughput, energy efficiency and ubiquitous user experience are propelling the advent of ultra- dense networks. Intercell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address the aforementioned challenges, in this paper, we propose a novel user-centric network orchestration solution for Cloud RAN based ultra-dense deployments. In this solution, a cluster (virtual disc) is created around users depending on their service priority. Within the cluster radius, only the best remote radio head (RRH) is activated to serve the user, thereby decreasing interference and saving energy. We follow a stochastic geometry based approach to quantify the area spectral efficiency (ASE) and RRH power consumption models to quantity energy(EE) efficiency of the proposed user-centric Cloud RAN (UCRAN). Through extensive analysis, we observe that the cluster sizes that yield optimal ASE and EE are quite different. Subsequently, we propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. A bargaining game is modeled to investigate the ASE-EE tradeoff through adjustment in the exponential efficiency weightage in the Nash bargaining solution (NBS). Results show that compared to current non user-centric network design, the proposed solution offers the flexibility to operate the network at multiple folds higher ASE or EE along with significant improvement in user experience.

User-centric network architectures are a key proponent to enable the uniform Quality of Experience (QoE) requirement for future dense heterogeneous network (HetNet) deployments. However, catering to spatio-temporally varying user service demands arising from the plethora of diverse mobile applications remains a challenge in such network architectures. In this paper, we propose a QoE-centric elastic framework for a dense multi-tier cellular network deployment. The framework leverages the control and data plane separation architecture (CDSA) for enabling selective data base station (DBS) activation within user equipment (UE)-centric virtual cells (also referred to as service zones). The allocation of these virtually elastic service zones around selected UEs is conducted via a central control base station (CBS) and modeled through two game techniques, namely evolutionary and auction games. Both the games are based on a utility minimization problem which is a function of weighted mean UE throughput and usage based UE service demands. To illustrate the trade-offs between the game models, network level performance is compared in terms of aggregate throughput, energy efficiency, algorithm convergence speed and mean UE scheduling probabilities.

With increasing cell density and the heterogeneity in the network, optimal user-cell association which is a well known open problem, will become an even more challenging issue. Contrary to the current studies that address user-cell association problem for convectional HetNets with massive MIMO deployments in HF (high frequencies) ranges, in this paper we investigate user-cell association problem for dense two-tier networks with massive MIMO deployment both at macro and femto-tier operating in HF and mmWave spectrum, respectively. We evaluate the performance of four user-cell association algorithms for massive MIMO deployment in a two-tier network under two different deployment scenarios: 1) HF-HF (both tiers operating in HF band); 2) HF-mmWave (MBSs operating in HF while FBSs in mmWave bands. To this end, we model the association problem in form of a convex network utility maximization problem as a function of the downlink user throughput. Contrary to the existing load aware association schemes that preclude the effect of bandwidth disparity in HF and mmWave bands, we propose a modified utility function that takes into account the effect of large bandwidth at mmWave bands. The problem is solvable through centralized as well as distributed or user centric load aware user association schemes.