Simulation, modeling and analysis of the eICIC/ABS in H-CRAN

The wireless mobile communication systems have developed from the second generation (2G) through to the current fourth generation (4G) wireless system, transforming from simply telephony system to a network transporting rich multimedia contents including video conferencing, 3-D gaming and in-flight broadband connectivity (IFBC) where airline crew use augmented reality headsets to address passengers personally. However, there are still many challenges that are beyond the capabilities of the 4G as the demand for higher data rate, lower latency, and mobility requirement by new wireless applications sores leading to mixed content-centric communication service. The fifth generation (5G) wireless system has thus been suggested, and research is ongoing for its deployment beyond 2020. In this article, we investigate the various challenges of 4G and propose an indoor, outdoor segregated cellular architecture with cloud-based Radio Access Network (C-RAN) for 5G, we review some of its key emerging wireless technologies needed in meeting the new demands of users including massive multiple input multiple output (mMIMO) system, Device-to-Device (D2D), Visible Light Communication (VLC), Ultra-dense network, Spatial Modulation and Millimeter wave technology. It is also shown how the benefits of the emerging technologies can be optimized using the Software Defined Networks/Network Functions Virtualization (SDN/NFV) as a tool in C-RAN. We conclude that the new 5G wireless architecture will derive its strength from leveraging on the benefits of the emerging hardware technologies been managed by reconfigurable SDN/NFV via the C-RAN. This work will be of immense help to those who will engage in further research expedition and network operators in the search for a smooth evolution of the current state of the art networks toward 5G networks.

HetNet (heterogeneous networks) is studied intensively in 3GPP to meet with the explosive mobile traffic growth. HetNet is a cell structure in which small cells are deployed under a blanket coverage of macro cells. If macro cells and small cells are working within a common frequency band, eICIC (enhanced inter- cell interference coordination) can be used for mitigating the inter-cell interference between macro cells and small cells. To realize eICIC, signal transmission timing must be synchronized among base stations. We propose a novel base station synchronization scheme in this paper. Then we conduct a field experiment to show the accuracy of the proposed scheme. In the proposed scheme, GPS is used for accurate timing distribution. Then system frames are synchronized among base stations by using a control packet with a timestamp. In addition, we adjust the internal delay of each base station site, i.e. propagation delay of cables or processing delay of equipment. The result of the field experiment shows that it is possible to achieve the synchronization accuracy within 1μs. Also, we confirmed that the measured downlink throughput using eICIC was not degraded at all even under severe inter-cell interference.

Latency optimized C-RAN in optical backhaul and RF fronthaul architecture for beyond 5G network: A comprehensive survey

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.

An LTE-Advanced (LTE-A) Heterogeneous Network (HetNet) constitutes of a macro cell and small cells, where, the former acts as an overlay network and the latter as an underlay network. In HetNet, enabling Carrier Aggregation (CA) usually results in severe downlink interference at small cells from macro cell because of high powered transmissions from macro cell on over-lapping component carriers (co-channels). The intensity of downlink interference is much more profound in HetNet enabled with CA than compared with a single carrier HetNet and ultimately affects the Quality of Service (QoS) of users connected to small cells. Some frequency domain solutions like cross carrier scheduling, soft frequency reuse, and downlink power control have been proposed so far, but they do not provide complete orthogonal transmissions among macro and small cells, leading to adjacent channel interference. Also, a few time domain approaches like Almost Blank Subframe (ABS), dynamic ABS have been proposed in the literature. However, the resource utilization of cells decreases due to high number of ABS assignments in the dense deployment, which also results in low user throughput. In this paper, we propose a combined time domain and frequency domain novel idea for downlink interference control, which is via adaptive Discontinuous Reception (DRX) settings through Multi-Objective optimizations and an optimal carrier allocation scheme, respectively. The proposed idea mitigates the downlink co-channel interference by enabling intermittent data reception at the User Equipment (UE) through adaptive DRX settings, also it utilizes an effective carrier allocation scheme such that users are always allocated to carriers with least interference. The analytical model and simulation results reveal that the proposed idea achieves average gain of 3.5 times w.r.t cell throughput when compared with well-known downlink interference mitigation schemes.

Interference management is one of the most important issues in heterogeneous cellular networks with multiple macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. Therefore, the adaptive eICIC configuration problem is critical, which adjusts the parameters including the ratio of almost blank subframes (ABS) and the bias of cell range expansion (RE). This problem is challenging especially for the scenario with multiple coexisting network services, since different services have different user scheduling strategies and different evaluation metrics. By using a general service model, we formulate the eICIC configuration problem with multiple coexisting services as a general form consensus problem with regularization and solve the problem by proposing an efficient optimization algorithm based on the alternating direction method of multipliers. In particular, we perform local RE bias adaptation at service layer, local ABS ratio adaptation at BS layer, and coordination among local solutions for a global solution at a network layer. To provide the service scalability, we encapsulate the service details into the local RE bias adaptation subproblem, which is isolated from the other parts of the algorithm, and we also introduce some implementation examples of the subproblem for different services. The extensive simulation results demonstrate the efficiency of the proposed algorithm and verify the convergence property.

In this paper, a joint control method for the user association and almost blank sub-frame (ABSF) based interference coordination (IC) technique in heterogeneous cellular networks is proposed. The optimization objective is simultaneously improving system throughput and guaranteeing proportionally fairness. Usually, user and base station (UE-BS) association is performed before ABSF rate determination, which is suboptimal with respect to UE- BS association. We formulate the joint optimization of UE-BS association and ABSF rate as a combinatorial optimization problem. To deal with it, we proposed an iterative method to discover the ABSF rate, the optimal association and the optimal resource blocks(RBs) allocation that can maximum long-term average throughput. Besides, as we cannot know the interference before user-association and resource allocation, we proposed a graph-based BS clustering method to estimate the interference before user-association and resource allocation. Simulation results show the efficiency of our proposed scheme.

Enhanced Inter-Cell Interference Coordination (eICIC) is used in LTE heterogeneous networks to reduce interference in the small cell expansion region. Service areas of small cells are often expanded by a Cell Selection Offset (CSO) to increase their traffic uptake and offload macro cell traffic. eICIC relies on Almost Blank Subframes (ABS) or Reduced Power Almost Blank Subframes (RP-ABS) to manage interference between macro and small cellular layers. Gains achieved from deploying intra-carrier small cells are highly dependent on the optimization of eICIC parameters. This paper provides a novel optimization formulation to achieve optimal small cell CSOs and macro cell ABS or RP-ABS patterns. The performance of the outlined optimization framework is evaluated by state-of-the-art LTE system level simulations. Simulation results indicate an average network capacity gain of 19.4% from optimizing ABS-eICIC parameters, and a 22.8% gain from optimizing RP-ABS-eICIC parameters. Similarly, a 42.2% average gain in user data rates is achieved from optimizing ABS-eICIC parameters, and a 45.9% gain from optimizing RP-ABS-eICIC parameters.

Guest editorial IJSCN special issue on 3GPP NTN standards for future satellite communications

Summary form only given. Radio access technologies for mobile broadband have evolved effectively to provide connectivity to billions of subscribers and things. Current Radio Access Network architecture is undergoing a transformation to increase deployment flexibility and network dynamicity, so that networks will be able to meet the performance requirements demanded by applications such as extreme mobile broadband and long-range massive machine type communication. Next generation 5G networks will cater for a wide range of new business opportunities, some of which have yet to be conceptualized. They will provide support for advanced mobile broadband services such as massive media distribution. Applications like remote operation of machinery, tele-surgery, and smart metering all require connectivity, but with vastly different characteristics. The ability to provide customized connectivity will benefit many industries around the world, enabling them to bring new products and services to market rapidly, and adapt to fast changing demands, all while continuing to offer and expand existing services. To prevent total cost of ownership from soaring, the 5G architecture will be software configurable and split between general purpose and specialized hardware, in a way that enables optimal placement of networks functions.

As the demand for high-speed, low-latency, and energy-efficient mobile communications continues to surge with the proliferation of IoT, AR/VR, and ultra-reliable applications, traditional Distributed Radio Access Network (D-RAN) architectures face critical limitations. Cloud Radio Access Network (C-RAN) emerges as a promising alternative that centralizes baseband processing to improve scalability, resource utilization, and operational flexibility. This paper presents a comprehensive evaluation of C-RAN architecture, focusing on structural models, fronthaul technologies, and cloud-based service logic. A detailed mathematical modeling framework is developed to assess key performance indicators, including latency, spectral efficiency, energy efficiency, and fronthaul capacity. Extensive results demonstrate that C-RAN achieves up to 45% gains in energy efficiency, a 35% improvement in spectral efficiency, and latency reductions of over 40% compared to D-RAN. Additional results reveal enhanced handover success rates, better BBU pool utili-zation, and increased reliability, with packet loss rates reduced to under 0.5%. Despite increased fronthaul bandwidth requirements, optical solutions such as DWDM and PON mitigate the bottleneck effectively. The findings confirm that C-RAN offers a robust, scalable, and cost-efficient solution for 5G and future mobile networks, enabling dynamic resource allocation, advanced interference management, and centralized network intelligence. The paper also addresses implementation challenges, including fronthaul provisioning and security, and outlines future research directions such as virtualization, AI-driven orchestration, and edge-cloud integration to fully harness the potential of C-RAN in ultra-dense and heterogeneous network environments.

The enhanced Inter-Cell Interference Coordination (eICIC) techniques introduced for LTE-Advanced HetNets yield significant gains by offloading macro cell users to small cells. Interference management is enabled via Almost Blank Subframes (ABS) on which the high power macro cells mute. This creates two different interference patterns corresponding to non-ABS and ABS subframes, which makes analyzing resource allocation and throughput performance non-trivial. We introduce a utility model for the eICIC framework that enables us to study resource allocation with different interference patterns. We use a semi-analytical method using this model to study resource allocation of pico cell-center users on both non-ABS and ABS subframes. We show for proportional fairness, the pico cell-center users may need to be scheduled on ABS subframes. We also demonstrate that this model can be used to determine the optimal ABS configuration for a given deployment.

Traditional macro-cell networks are experienced an increase of data traffic and small-cells are deployed help to offload the traffic from macro-cells. The Energy Aware Traffic Offloading for Green Heterogeneous Networks (EATOG) approach is analyzed on grid power saving by offloading traffic for green heterogeneous networks to increase the efficient utilization of harvested energy for on-grid power saving while satisfying the Quality of Service (QoS) requirement. The EATOG is mainly intends to of energy-aware traffic offloading for HCN with multiple Small cell Base Station (SBS) powered by diverse energy sources which reduces the on-grid network power consumption while satisfying the QoS requirement in terms of rate outage probability. The performance of energy consumption is degraded due to the Overlapped Small Cell Based Stations. An optimal deploying low power node within macrocell coverage area is proposed in this paper to improve the system utility while minimizing the installation cost. The proposed Optimized Energy Aware Traffic Offloading for Green Heterogeneous Networks (OEATOG) approach is considers the inter-cell interference and the configuration of Almost Blank Sub-frames (ABS) when maximizing the system utility. The proposed paper deals with the placement of Pico Base Station within the macro cell in LTE (Long Term Evolution) heterogeneous networks. A Pico Cell is a small cellular Base Station (BS) which supports low power nodes and offers greater capacity and coverage areas. Furthermore the heuristic algorithm is introduced too efficiently solve the formulated problem and obtain the optimal picocell placement. The simulation results indicate that the proposed algorithm is to improve the utility of the network, especially in regions with high traffic density, while maintaining the installation cost at a reasonable level.

More data and information is being captured from systems, machines, and devices and made available to industrial information technology (IT) systems. The information is processed on-the-fly, enabling IT-based management systems to generate updated information for real-time control of the manufacturing processes. This data capturing and collection for IT systems is often referred to as the Internet of Things (IoT). When adopted to the industrial requirements, such as robustness, reliability, timeliness, and security, it is often termed as the industrial IoT (IIoT) [A1]. IIoT has attracted the attention of both the industry and academia since it is expected to enhance day-to-day activities, create new business models, products, and services, and as a broad source of research topics and ideas. Meanwhile, it is envisioned that the fifth-generation (5G) networks will be a cornerstone in future wireless industrial connectivity, and currently, there are multitude of ongoing research efforts in their design and optimization. Future industries willembrace use cases with numerous wireless-connected sensors and devices, and judging by the demand, massive machine-type communication and ultra-reliable low-latency communication (URLLC) in the literature and standardization activities, have been identified as two of the three main communication scenarios for 5G. These scenarios demand intelligent, scalable, and robust radio access techniques, network architectures, and deployment options to meet industrial demands [A2]. Therefore, more in-depth research is needed for IIoT and sensor networks in 5G-and-beyond wireless communication systems to address various challenges, including the following. 1)Transmit power control policy should be judiciously designed to improve both the spectrum efficiency and energy efficiency effectively; higher transmit powers can improve reliability but increase the interference and battery consumption. 2)Low-latency communication and computing is one of the significant challenges in 5G-and-beyond IIoT; uploading the device data to the cloud computing centers has high latency and resources waste issues in sensor networks. 3)Addressing privacy and security problems [A3] in the 5G-IIoT is fundamental to the further development and spread of 5G-IIoT. 4)Reliability and latency requirements of URLLC services, requiring less than 1-ms user plane latency and higher than 99.999% reliability, are demanding to meet, especially in time-varying industrial wireless channels. 5)Radio resource allocation, sharing, and isolation with performance guarantees under dynamic traffic conditions are critical issues for emerging IIoT applications requiring real-time support of massive connected devices. 6)5G-and-beyond IIoT networks must satisfy industrial-grade coverage, capacity, time-sensitive networking, and over-the-air time synchronization requirements [A4].

Unmanned aerial vehicles (UAVs) are projected to be utilized in a variety of unexpected applications, including agriculture, firefighting, emergency response, intelligent transportation, and so on. Wireless communication is one of the primary facilitators in bringing UAVs into a new phase in such applications. To realize the vision in fifth-generation (5G) networks, we propose a 5G-integration of the flexible multi-UAV system and the Open Radio Access Network (O-RAN) architecture, named U-ORAN. Although different studies have been proposed to optimize the UAV trajectory and resource allocation in the radio access network (RAN), our work is the first study to investigate the benefits of adopting UAVs in the O-RAN architecture. In U-ORAN, we consider a flying base station system and propose a joint optimization problem of multi-UAV trajectory and offloading tasks (UTOT) in which UTOT can optimize the routes from users to the core network as well as resource allocation to process offloading tasks. We decompose UTOT into two sub-problems and provide learning solutions based on the multi-agent reinforcement learning and online learning methodologies, both of which are well supported by the O-RAN architecture. Our intensive numerical simulations show that the proposed approaches outperform in a variety of settings and validation scenarios.