Analysis of MAC-level throughput in LTE systems with link rate adaptation and HARQ protocols

LTE is rapidly gaining momentum for building future 4G cellular systems, and real operational networks are under deployment worldwide. To achieve high throughput performance, in addition to an advanced physical layer design LTE exploits a combination of sophisticated mechanisms at the radio resource management layer. Clearly, this makes difficult to develop analytical tools to accurately assess and optimise the user perceived throughput under realistic channel assumptions. Thus, most existing studies focus only on link-layer throughput or consider individual mechanisms in isolation. The main contribution of this paper is a unified modelling framework of the MAC-level downlink throughput of a sigle LTE cell, which caters for wideband CQI feedback schemes, AMC and HARQ protocols as defined in the LTE standard. We have validated the accuracy of the proposed model through detailed LTE simulations carried out with the ns-3 simulator extended with the LENA module for LTE

On the optimal user grouping in NOMA system technology

This paper provides a state-of-art analysis of the most relevant studies on optimal user-aggregation strategies for non-orthogonal multiple access (NOMA) technology. The main ideas behind are i) to highlight how, in addition to the adoption of an optimal power allocation scheme, an optimal user-aggregation strategy represents an important key factor for improving NOMA system performance, and ii) to provide an exhaustive survey of the most relevant studies which can serve as useful starting point for the definition of new channel state-aware user-aggregation strategies for NOMA systems which, at the time of writing, represents a research field that still remains to be investigated more in depth. A detailed and complete analysis, which permits to point out the need to guarantee a certain relationship between users’ channel gain, is provided for each cited work

Promoting STEM via UMI: an Ecological Framing of CoPs in Networking and Networked Robotics

Ubiquitous Computing, Mobile Computing and Internet of Things (UMI) technologies, are widely diffused in the everyday life. In addition to their primary usage (e.g., supporting the implementation of the future 5G network),these technologies can be used in the context of Science Education.According to this perspective, the innovative psycho-pedagogical approach here presented has been ad-hoc developed for the Horizon 2020 Project “Exploiting Ubiquitous Computing, Mobile Computing and the Internet of Things to promote Science Education” (Umi-Sci-Ed). The aim of the project is to enhance knowledge and skills of Science, Technology, Engineering and Mathematics (STEM) and to promote positive attitudes towards these disciplines. In order to reach this goal, the UMI technologies, framed in the Community of Practices (CoPs) paradigm, will be introduced in the learning process of secondary schools’ students (i.e., 9thand 10thgrade). Specifically, the students will attend to innovative learning activities, such as hands-on activities, concerning with Networking and networked Robotics. In the present contribution, the theoretical framework that constitutes the rationale for the Umi-Sci-Ed projectwill be described. In particular, the “bottom-up” socio-constructionist perspective will be presented, aswell as the main technological tools (e.g., UDOO) that would be used to implement an integrated STEM learning environment. The expected results of the project will be discussed

Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead

The concept of digital twin (DT) is constantly revealing as a key enabling technology for the deployment of mobile communication services envisaged for the sixth-generation (6G) Internet-of-Things (IoT). This paper aims at providing a comprehensive review of the current state-of-the-art DT-enabled 6G oriented network services. The main characteristics of this new key enabling technology and its critical aspects are highlighted. An overview of the 6G network requirements for the deployment of its innovative envisioned services is firstly provided, emphasizing how the DT concept represents a complementary key enabling technology for them. This is followed by a brief introduction of the DT technology. Subsequently, a comprehensive classification and analysis of the research advancements on DT-enabled 6G services currently available in literature is provided. This paper is concluded by highlighting the most representative challenges and future directions necessary for the deployment of this promising and innovative technology

Jointly power allocation and phase shift optimization for RIS empowered downlink cellular networks

Reconfigurable Intelligent Surfaces (RIS) have been highlighted by the research community as a key enabling technology for the enhancement of next-generation wireless network performance, including energy efficiency, spectral efficiency, and network throughput. This paper investigates how RIS-assisted communication can effectively maximize the downlink throughput of a cellular network. Specifically, the paper considers a communication scenario where a single base station serves multiple ground users with the aid of an RIS placed on a building facade. For such a communication scenario, we considered an optimization problem aimed at maximizing the overall downlink throughput by jointly optimizing power allocation at the base station and phase shift of RIS reflecting elements, subject to power consumption and quality-of-service constraints. To address its non-convex nature, the original optimization problem has been divided into two subproblems. The first one, for power control with fixed phase shift values, is a convex problem that can be easily solved. Subsequently, a phase shift searching procedure to solve the non-convex problem of RIS phase shift optimization has been adopted. The results from numerical simulations show that the proposed method outperforms other conventional methods proposed in the literature. In addition, computational complexity analysis has been conducted to prove the low complexity of the proposed method

Offloading through Opportunistic Networks with Dynamic Content Requests

Offloading is gaining momentum as a technique to overcome the cellular capacity crunch due to the surge of mobile data traffic demand. Multiple offloading techniques are currently under investigation, from modifications inside the cellular network architecture, to integration of multiple wireless broadband infrastructures, to exploiting direct communications between mobile devices. In this paper we focus on the latter type of offloading, and specifically on offloading through opportunistic networks. As opposed to most of the literature looking at this type of offloading, in this paper we consider the case where requests for content are non-synchronised, i.e. users request content at random points in time. We support this scenario through a very simple offloading scheme, whereby no epidemic dissemination occurs in the opportunistic network. Thus our scheme is minimally invasive for users’ mobile devices, as it uses only minimally their resources. Then, we provide an analysis on the efficiency of our offloading mechanism (in terms of percentage of offloaded traffic) in representative vehicular settings, where content needs to be delivered to (subsets of the) users in specific geographical areas. Depending on various parameters, we show that a simple and resource-savvy offloading scheme can nevertheless offload a very large fraction of the traffic (up to more than 90%, and always more than 20%). We also highlight configurations where such a technique is less effective, and therefore a more aggressive use of mobile nodes resources would be needed

Robust Adaptive Modulation and Coding (AMC) Selection in LTE Systems using Reinforcement Learning

Adaptive Modulation and Coding (AMC) in LTE networks is commonly employed to improve system throughput by ensuring more reliable transmissions. Most of existing AMC methods select the modulation and coding scheme (MCS) using pre-computed mappings between MCS indexes and channel quality indicator (CQI) feedbacks that are periodically sent by the receivers. However, the effectiveness of this approach heavily depends on the assumed channel model. In addition CQI feedback delays may cause throughput losses. In this paper we design a new AMC scheme that exploits a reinforcement learning algorithm to adjust at run-time the MCS selection rules based on the knowledge of the effect of previous AMC decisions. The salient features of our proposed solution are: i) the lowdimensional space that the learner has to explore, and ii) the use of direct link throughput measurements to guide the decision process. Simulation results obtained using ns3 demonstrate the robustness of our AMC scheme that is capable of discovering the best MCS even if the CQI feedback provides a poor prediction of the channel performance