DRL Enabled Coverage and Capacity Optimization in STAR-RIS-Assisted Networks

Ultra-dense Heterogeneous networks (UDHN) are emerging as the inevitable approach to cope with the imminent cellular network capacity crunch. However, load imbalance and widely disproportionate SINR distribution between macro and small cells, remains the key hurdle in harnessing the full potential of UDHN. In this paper we address this problem by proposing and analysing a novel load-aware user association methodology that offers a mechanism to simultaneously optimize network capacity, load distribution and coverage. The solution concurrently leverages the three key optimization parameters for Coverage and Capacity Optimization (CCO) and Load Balancing (LB) SON functions i.e. antenna tilts, transmit powers and cell individual offsets (CIOs). The method incorporates exponential-weighting based prioritization of CCO and LB SON functions within the user association process. The results suggest that the proposed approach offers a distribution of load between macro and small cells that yields more gain in terms of both network capacity and user quality of service than conventional max signal strength or max SINR based association methods.

This paper presents a framework for coverage and capacity optimization (CCO) in HCNs from stochastic geometry theory. Unlike conventional approaches, we propose a concept of effective capacity as t...

The management and operation of a Self-Organizing Network (SON)-enabled mobile network still requires considerable human effort. On the one hand, SON Functions need to be configured through low-level parameters in order to control the optimization of the network. On the other hand, an operator wants to steer the system with solely technical objectives, and the underlying network should be adapted accordingly. This opens up a gap in network management that is currently closed manually. This paper presents an approach that overcomes the manual gap between technical objectives and SON Functions by choosing the best values for the SON Functions' configurations automatically. Main advantage of this approach is that it allows to manage a system at a high level of abstraction and, at the same time, reduces manual effort. The approach is explained by applying it in a case study in the field of mobile networks with four SON Functions, namely Mobility Load Balancing (MLB), Coverage and Capacity Optimization (CCO), Energy Savings Management (ESM) and Mobility Robustness Optimization (MRO).

The deployment, operation and maintenance of complex cellular networks are managed autonomously by multiple concurrently executing Self-Organizing Network (SON) functions with dedicated objectives, that can often negatively impact the functioning of each other. It is essential to avoid the blinkered view to their individual targets and consider a holistic approach towards identifying the best possible coordination between them, in order to achieve desired overall network gains while ensuring stable and robust network operation. The designing of appropriate SON-coordination mechanisms is quite challenging as it requires comprehensive modelling of all the complementing and conflicting interactions among them. This article discusses the application of Machine Learning based online Recommender Systems to model the dynamics between SON functions. To evaluate its applicability, in this work, the focus is to jointly implement two intertwined SON functions - Inter Cell Interference Coordination (ICIC) and Coverage and Capacity Optimization (CCO), to implicitly handle their conflicts and achieve the desired trade-off between coverage and capacity by optimizing a joint objective. The proposed cooperative learning and distributed configuration enforcement based ICIC-CCO coordinated SON solution has been evaluated on a system-level LTE network simulator with varied traffic distributions. It has been observed that the outage situations in the network are significantly reduced while still achieving high Signal-to-Interference Ratios (SIRs), even with reduced transmit power settings on several occasions.

Millimetre wave (mm-Wave) bands and sub-6 GHz are key technologies in solving the spectrum critical situation in the fifth generation (5G) wireless networks in achieving high throughput with low transmission power. This paper studies the performance of dense small cells that involve a millimetre wave (mm-Wave) band and sub-6 GHz that operate in high frequency to support massive multiple-input-multiple-output systems (MIMO). In this paper, we analyse the propagation path loss and wireless powered transfer for a 5G wireless cellular system from both macro cells and femtocells in the sub-6 GHz (µWave) and mm-Wave tiers. This paper also analyses the tier heterogeneous in downlink for both mm-Wave and sub-6 GHz. It further proposes a novel distributed power to mitigate the inter-beam interference directors and achieve high throughput under game theory-based power constraints across the sub-6 GHz and mm-Wave interfaces. From the simulation results, the proposed distributed powers in femtocell suppresses inter-beam interference by minimising path loss to active users (UEs) and provides substantial power saving by controlling the distributed power algorithm to achieve high throughput.

In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges.

Optimizing the discriminator in Generative Adversarial Networks (GANs) to completion in the inner training loop is computationally prohibitive, and on finite datasets would result in overfitting. To address this, a common update strategy is to alternate between k optimization steps for the discriminator D and one optimization step for the generator G. This strategy is repeated in various GAN algorithms where k is selected empirically. In this paper, we show that this update strategy is not optimal in terms of accuracy and convergence speed, and propose a new update strategy for networks with Wasserstein GAN (WGAN) group related loss functions (e.g. WGAN, WGAN-GP, Deblur GAN, and Super resolution GAN). The proposed update strategy is based on a loss change ratio comparison of G and D. We demonstrate that the proposed strategy improves both convergence speed and accuracy.

Flexible interconnection, as a novel power system architecture, effectively enhances system reliability and economy. The problem of site selection and capacity optimization for flexible interconnection represents a significant component of flexible interconnection planning. Its objective is to identify the most advantageous locations and capacities for flexible interconnection devices while adhering to the constraints of system security and economic viability. This paper proposes a solution method based on an improved Crested Porcupine Optimizer (CPO) to address this problem. By introducing adaptive crossover strategies and elite strategies, the improved CPO enhances its global search ability and convergence speed. Furthermore, a novel fitness function is adopted to better measure the comprehensive benefits of flexible interconnection site selection and capacity optimization schemes. The results of the simulation indicate that this methodology is capable of effectively addressing the challenges associated with flexible interconnection site selection and capacity optimization. This approach has demonstrated remarkable performance, with an overall efficiency enhancement surpassing 40%.

In this paper we introduce a mathematical model in the framework of long term evolution (LTE) to solve the problem of coverage and capacity optimization by employing multi-level random Taguchi's Method. The optimization process runs in a centralized manner with no human intervention required and interacts with environment situation automatically. Under the mathematical model which combines coverage and capacity optimization by a coefficient factor, conventional Taguchi's Method transcends traditional trial-and-error approach in convergence speed and simple implementation, to offer even more search capacity, Gaussian shrink coefficient and random optimization offset are applied to the level-to-tilts mapping function. Antenna tilt is an effective interference reduction technique, which has been adopted as the tuning parameter. The simulation results turn out to be better than traditional Taguchi's Method and trial-and-error approach. The system model presented and evaluated also gives an insight to the mathematical model especially the trade-off between coverage and capacity in homogeneous scenario.

Multi-material thermomechanical topology optimization with applications to additive manufacturing: Design of main composite part and its support structure

Multi-objective capacity optimization of a hybrid energy system in two-stage stochastic programming framework

In the past few years, multi-objective optimization algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a set of Pareto-optimal solutions, one is often interested in capturing knowledge about the variation of variable values over the Pareto-optimal front. Recent innovization approaches for knowledge discovery from Pareto-optimal solutions remain as a major activity in this direction. In this article, a different data-fitting approach for continuous parameterization of the Pareto-optimal front is presented. Cubic B-spline basis functions are used for fitting the data returned by an EMO procedure in a continuous variable space. No prior knowledge about the order in the data is assumed. An automatic procedure for detecting gaps in the Pareto-optimal front is also implemented. The algorithm takes points returned by the EMO as input and returns the control points of the B-spline manifold representing the Pareto-optimal set. Results for several standard and engineering, bi-objective and tri-objective optimization problems demonstrate the usefulness of the proposed procedure.

This paper proposes a selection strategy for determining the optimal capacity and location of a static synchronous compensator (STATCOM) device under intermittent output variations of renewable energy sources using a multi-objective cuckoo search (MOCS) algorithm. The impact of seasonal output variations on the location and capacity of the STATCOM devices was analyzed based on the daily output variations of the wind turbines. Three objective functions were considered to determine the optimal location and capacity of the STATCOM devices: (1) minimizing the system line losses, (2) minimizing the transient voltage indicators, and (3) minimizing the total cost of the STATCOM capacity. MOCS was combined with the Pareto front to determine the non-dominated solution sets for the four seasons. The Manhattan distance method was used to select the most suitable installation locations and capacities within a specified time interval. Power flow calculations were performed using DIgSILENT Power Factory 2021 and MATLAB R2021b. To validate the proposed method, a test experiment was conducted using an IEEE 39 bus system.

Intelligent reflecting surface (IRS) is a revolutionizing approach for achieving low-cost yet spectral and energy efficient wireless communications. By properly tuning its massive reflecting elements, IRS is able to construct favorable channels and thereby significantly improve the wireless communication performance in various setups. In this paper, we consider the general wireless network consisting of multiple base stations (BSs), users and IRSs, and investigate their joint association optimization in the downlink communication. Specifically, each IRS assists in the communication from its associated BS to user and in the meanwhile randomly scatters the signals from the other non-associated BSs. As such, the joint BS-IRS-user association is more involved as compared to the BS-user association in conventional wireless networks without IRS. To address this new problem, we first derive the average signal-to-interference-plus-noise ratio (SINR) of each user in closed-form and then formulate the joint association problem to maximize the users' utility in the downlink communication. Both the optimal and low-complexity suboptimal solutions are proposed for the formulated problem. Numerical results demonstrate significant performance gains of the proposed solutions over benchmark schemes.

A ring-hierarchy-based evolutionary algorithm for multimodal multi-objective optimization