Enhanced mobile broadband (eMBB), characterized by its high data rates, and ultra-reliable low-latency communications (URLLC), known for its dependability, are transforming the digital landscape. Nevertheless, the eMBB-URLLC coexistence poses significant challenges in balancing high-speed data transmissions with real-time reliability, particularly considering the scarce wireless resources. In this paper, we explore dynamic time division duplex (TDD) pattern selection to enhance coexistence performance. Thus, we formulate an optimization problem to maximize eMBB throughput while ensuring fairness among them and meeting URLLC reliability requirements. To solve this problem, we develop a deep reinforcement learning framework using a double deep Q-network (DDQN). Results from our near-product 5G simulator show that our solution significantly improves eMBB throughput and reduces the throughput interquartile range by over 40% compared to competing baselines while meeting URLLC reliability requirements.

As industries become more digitized, they are entering a new era of connectivity based on 5G and beyond technology features. This transformation brings new challenges such as interference between public and non-public networks. In this study, we investigate the automatic configuration of non-public networks using a Machine Learning-based What-if Analysis technique to mitigate interference and enhance 5G NR networks coexistence. This proactive decision-making solution is designed for various coexistence scenarios, namely adjacent and co-channel. It provides a systematic approach for selecting the most suitable network parameters and configurations. We validate our approach through experimental analysis, with a particular focus on selecting and analyzing the TDD pattern and link robustness for 5G NR non-public network deployments. Our analysis is based on real-world experiments carried out at the 5G Industry Campus Europe in Aachen, Germany.

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.

Industry 4.0 imposes strict requirements on the fifth generation of wireless systems (5G), such as high reliability, high availability, and low latency. Guaranteeing such requirements implies that system failures should occur with an extremely low probability. However, some applications (e.g., training a reinforcement learning algorithm to operate in highly reliable systems or rare event simulations) require access to a broad range of observed failures and extreme values, preferably in a short time. In this paper, we propose IL-GAN, an alternative training framework for generative adversarial networks (GANs), which leverages incremental learning (IL) to enable the generation to learn the tail behavior of the distribution using only a few samples. We validate the proposed IL-GAN with data from 5G simulations on a factory automation scenario and real measurements gathered from various video streaming platforms. Our evaluations show that, compared to the state-of-the-art, our solution can significantly improve the learning and generation performance, not only for the tail distribution but also for the rest of the distribution.

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Violation of these requirements may result in faulty behavior of the system and cause significant economic losses. Although wireless communications enable mobility and easy maintenance to industrial networks, it introduces many challenges to high-performance control systems due to interference and harsh environments (e.g., vibrations and many metallic objects). Diversity techniques and power control are powerful approaches to reduce latency and enhance reliability at the expense of excessive resource usage due to redundant transmissions. In this paper, we adopt fundamental metrics from reliability literature to wireless communications and provide critical indicators to measure reliability key performance indicators (KPIs) of cyber-physical systems. Then, we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize our reliability KPIs. Our orchestrator enables near real-time control and can be implemented on the edge cloud. We implement our framework on 3GPP compliant simulator on a factory automation scenario. Our comprehensive experiments show that, compared to the state-of-the-art, our solution can substantially improve the performance, especially for 5th percentile availability.

Cyber-physical control applications impose strict requirements on the reliability and latency of the underlying communication system. Hence, they have been mostly implemented using wired channels where the communication service is highly predictable. Nevertheless, fulfilling such stringent demands is envisioned with the fifth generation of mobile networks (5G). The requirements of such applications are often defined on the application layer. However, cyber-physical control applications can usually tolerate sparse packet loss, and therefore it is not at all obvious what configurations and settings these application level requirements impose on the underlying wireless network. In this paper, we apply the fundamental metrics from reliability literature to wireless communications and derive a mapping function between application level requirements and network level parameters for those metrics under deterministic arrivals. Our mapping function enables network designers to realize the end-to-end performance (as the target application observes it). It provides insights to the network controller to either enable more reliability enhancement features (e.g., repetition), if the metrics are below requirements, or to enable features increasing network utilization, otherwise. We evaluate our theoretical results by realistic and detailed simulations of a factory automation scenario. Our simulation results confirm the viability of the theoretical framework under various burst error tolerance and load conditions.

A hybrid cloud radio access network (H-CRAN) architecture has been proposed to alleviate the midhaul capacity limitation in C-RAN. In this architecture, functional splitting is utilized to distribute the processing functions between a central cloud and edge clouds. The flexibility of selecting specific split point enables the H-CRAN designer to reduce midhaul bandwidth, reduce latency, save energy, or distribute the computation task depending on equipment availability. Meanwhile, techniques for caching are proposed to reduce content delivery latency and the required bandwidth. However, caching imposes new constraints on functional splitting. In this study, considering H-CRAN, a constraint programming problem is formulated to minimize the overall power consumption by selecting the optimal functional split point and content placement, taking into account the content access delay constraint. We also investigate the trade-off between the overall power consumption and occupied midhaul bandwidth in the network. Our results demonstrate that functional splitting together with enabling caching at edge clouds reduces not only content access delays but also fronthaul bandwidth consumption and saves energy finding a compromise between these performance metrics.

Today's modern communication technologies such as cloud radio access and software defined networks are key candidate technologies for enabling 5G networks as they incorporate intelligence for data-driven networks. Traditional content caching in the last mile access point has shown a reduction in the core network traffic. However, the radio access network still does not fully leverage such solution. Transmitting duplicate copies of contents to mobile users consumes valuable radio spectrum resources and unnecessary base station energy. To overcome these challenges, we propose huMan mObility-based cOntent Distribution (MOOD) system. MOOD exploits urban scale users' mobility to allocate radio resources spatially and temporally for content delivery. Our approach uses the broadcast nature of wireless communication to reduce the number of duplicated transmissions of contents in the radio access network for conserving radio resources and energy. Furthermore, a human activity model is presented and statistically analyzed for simulating people daily routines. The proposed approach is evaluated via simulations and compared with a generic broadcast strategy in an actual existing deployment of base stations as well as a smaller cells environment, which is a trending deployment strategy in future 5G networks. MOOD achieves 15.2 and 25.4 percent of performance improvement in the actual and small-cell deployment, respectively.

The inevitable deployment of 5G and the Internet of Things (IoT) sheds the light on the importance of the energy efficiency (EE) performance of Device-to- Device (DD) communication systems. In this work, we address a potential IoT application, where different prioritized DD system, i.e., Low-Priority (LP) and High-Priority (HP) systems, co-exist and share the spectrum. We maximize the EE of each system by proposing two schemes. The first scheme optimizes the individual transmission power and the spatial density of each system. The second scheme optimizes the transmission power ratio of both systems and the spatial density of each one. We also construct and analytically solve a multi- objective optimization problem that combines and jointly maximizes both HP and LP EE performance. Unique structures of the addressed problems are verified. Via numerical results we show that the system which dominates the overall EE (combined EEs of both HP and LP) is the system corresponding to the lowest power for low/high power ratio (between HP and LP systems). However, if the power ratio is close to one, the dominating EE corresponds to the system with higher weight.

Centralized Radio Access Network (C-RAN) has been recently proposed to increase network capacity, reduce energy consumption, and improve scalability. However, C-RAN requires an extensive modification to the current infrastructure, which results in a considerable deployment cost. In this paper, we conduct a techno-economic study to evaluate the migration cost of C-RAN, and we propose a methodology for cost and energy efficient C-RAN deployment. We exploit the concept of total cost of ownership, defined as the sum of capital and operational expenditures. We formulate a Digital Unit (DU) pool placement optimization problem as Mixed Integer Linear Programming (MILP), which minimizes the total cost of ownership. We compare the total cost of ownership of C-RAN to that of the existing infrastructure, under different deployment scenarios such as greenfield and brownfield deployment of fiber and DU pool, and different cell sizes. The results show that the optical infrastructure plays a determinant role in the migration cost of C-RAN. If greenfield fiber is assumed, the migration cost cannot be compensated in a reasonable amount of time. If brownfield fiber is assumed, the migration cost is considerably reduced, and a more feasible C-RAN deployment is achieved.

Cloud radio access network (CRAN) has been proposed as a potential energy saving architecture and a scalable solution to increase the capacity and performance of radio networks. The original CRAN decouples the digital unit (DU) from radio unit (RU) and centralizes the DUs. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN, i.e. the network segment connecting RUs and DUs. In this study, we propose a modified CRAN architecture, namely hybrid cloud RAN (H-CRAN), where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions, i.e., central cloud level and edge cloud level, connected by a transport layer called “midhaul”. We study the interplay of energy efficiency and midhaul bandwidth consumption when baseband functions are centralized at the edge cloud vs central cloud. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying the network constraints. The addressed problem with the associated constrains are modeled as a mixed integer constraint optimization problem. Numerical results show the compromise between energy and bandwidth consumption, with the optimal placement of baseband processing functions in H-CRAN architecture.

Today's mobile service providers aim at ensuring end-to-end performance guarantees. Hence, ensuring an efficient content delivery to end users is highly required. Currently, transmitting popular contents in modern mobile networks rely on unicast transmission. This result into a huge underutilization of the wireless bandwidth. The urban scale mobility of users is beneficial for mobile networks to allocate radio resources spatially and temporally for broadcasting contents. In this paper, we conduct a comprehensive analysis on a human activity/mobility model and the content broadcasting system in 5G mobile networks. The objective of this work is to describe how human daily activities could improve the content broadcasting efficiency. We achieve the objective by analyzing the transition probabilities of a user traveling over several places according to the change of states of daily human activities. Using a real-life simulation, we demonstrate the relationship between the human mobility and the optimization objective of the content broadcasting system.

As a potential candidate architecture for 5G systems, cloud radio access network (CRAN) enhances the system's capacity by centralizing the processing and coordination at the central cloud. However, this centralization imposes stringent bandwidth and delay requirements on the fronthaul segment of the network that connects the centralized baseband processing units (BBUs) to the radio units (RUs). Hence, hybrid CRAN is proposed to alleviate the fronthaul bandwidth requirement. The concept of hybrid CRAN supports the proposal of splitting/virtualizing the BBU functions processing between the central cloud (central office that has large processing capacity and efficiency) and the edge cloud (an aggregation node which is closer to the user, but usually has less efficiency in processing). In our previous work, we have studied the impact of different split points on the system's energy and fronthaul bandwidth consumption. In this study, we analyze the delay performance of the end user's request. We propose an end-to-end (from the central cloud to the end user) delay model (per user's request) for different function split points. In this model, different delay requirements enforce different function splits, hence affect the system's energy consumption. Therefore, we propose several research directions to incorporate the proposed delay model in the problem of minimizing energy and bandwidth consumption in the network. We found that the required function split decision, to achieve minimum delay, is significantly affected by the processing power efficiency ratio between processing units of edge cloud and central cloud. High processing efficiency ratio (≈1) leads to significant delay improvement when processing more base band functions at the edge cloud.

In this paper, we propose a cross-layer design, which optimizes the energy efficiency of a potential future 5G spectrum-sharing environment, in two sharing scenarios. In the first scenario, underlying sharing is considered. We propose and minimize a modified energy per good bit (MEPG) metric, with respect to the spectrum sharing user's transmission power and media access frame length. The cellular users, legacy users, are protected by an outage probability constraint. To optimize the non-convex targeted problem, we utilize the generalized convexity theory and verify the problem's strictly pseudoconvex structure. We also derive analytical expressions of the optimal resources. In the second scenario, we minimize a generalized MEPG function while considering a probabilistic activity of cellular users and its impact on the MEPG performance of the spectrum sharing users. Finally, we derive the associated optimal resource allocation of this problem. Selected numerical results show the improvement of the proposed system compared with other systems.

We propose a cross layer design that optimizes the energy efficiency of spectrum sharing systems. The energy per good bit (EPG) is considered as an energy efficiency metric. We optimize the secondary user's transmission power and media access frame length to minimize the EPG metric. We protect the primary user transmission via an outage probability constraint. The non-convex targeted problem is optimized by utilizing the generalized convexity theory and verifying the strictly pseudo-convex structure of the problem. Analytical results of the optimal power and frame length are derived. We also used these results in proposing an algorithm, which guarantees the existence of a global optimal solution. Selected numerical results show the improvement of the proposed system compared to other systems.

In this paper, we consider a cognitive radio system in which a block-fading channel is assumed. Each transmission frame consists of M blocks and each block undergoes a different channel gain. Instantaneous channel state information about the interference links remains unknown to the primary and secondary users. We minimize the secondary user's targeted outage probability over the block-fading channels. To protect the primary user, a statistical constraint on its targeted outage probability is enforced. The secondary user's targeted outage region and the corresponding optimal power are derived. We also propose two sub-optimal power strategies and derive compact expressions for the corresponding outage probabilities. These probabilities are shown to be asymptotic lower and upper bounds on the outage probability. Utilizing these bounds, we derive the exact diversity order of the secondary user outage probability. Selected numerical results are presented to characterize the system's behavior.

In this paper, an energy efficient cognitive radio system is proposed. The proposed design optimizes the secondary user transmission power and the sensing duration combined with soft-sensing information to minimize the energy per goodbit. Due to the non-convex nature of the problem we prove its pseudo-convexity to guarantee the optimal solution. Furthermore, a quantization scheme, that discretize the soft-sensing information, is proposed and analyzed to reduce the overload of the continuously adapted power. Numerical results show that the energy per goodbit performance of the proposed system outperforms the benchmark systems. The impact of the quantization levels and other system parameters is evaluated in the numerical results.

In this paper, two resource allocation schemes for energy efficient cognitive radio systems are proposed. Our design considers resource allocation approaches that adopt spectrum sharing combined with soft-sensing information, adaptive sensing thresholds, and adaptive power to achieve an energy efficient system. An energy per good-bit metric is considered as an energy efficient objective function. A multi-carrier system, such as, orthogonal frequency division multiplexing, is considered in the framework. The proposed resource allocation schemes, using different approaches, are designated as sub-optimal and optimal. The sub-optimal approach is attained by optimizing over a channel inversion power policy. The optimal approach utilizes the calculus of variation theory to optimize a problem of instantaneous objective function subject to average and instantaneous constraints with respect to functional optimization variables. In addition to the analytical results, selected numerical results are provided to quantify the impact of soft-sensing information and the optimal adaptive sensing threshold on the system performance.

In this paper we consider a cognitive radio multi-input multi-output environment in which we adapt our beamformer to maximize both energy efficiency and signal to interference plus noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with the sensing information to achieve an optimal energy efficient system. The proposed schemes maximize the energy efficiency and SINR metrics subject to cognitive radio and quality of service constraints. Since the optimization of energy efficiency problem is not a convex problem, we transform it into a standard semi-definite programming (SDP) form to guarantee a global optimal solution. Analytical solution is provided for one scheme, while the other scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.

In this paper we propose an energy efficient cognitive radio system. Our design considers an underlaying resource allocation combined with soft sensing information to achieve a sub-optimum energy efficient system. The sub-optimality is achieved by optimizing over a channel inversion power policy instead of considering a water-filling power policy. We consider an Energy per Goodbit (EPG) metric to express the energy efficient objective function of the system and as an evaluation metric to our system performance. Since our optimization problem is not a known convex problem, we prove its convexity to guarantee its feasibility. We evaluate the proposed scheme comparing to a benchmark system through both analytical and numerical results.

In this paper, we consider a cognitive radio multi-input-multi-output environment, in which we adapt our beamformer to maximize both energy efficiency (EE) and signal-to-interference-plus-noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with sensing information to achieve optimal energy-efficient systems. The proposed schemes maximize EE and SINR metrics subject to cognitive radio and quality-of-service constraints. The analysis of the proposed schemes is classified into two categories based on knowledge of the secondary-transmitter-to-primary-receiver channel. Since the optimizations of EE and SINR problems are not convex problems, we transform them into a standard semidefinite programming (SDP) form to guarantee that the optimal solutions are global. An analytical solution is provided for one scheme, while the second scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.