Energy-Efficient and Overhead-Aware Cooperative Communications

Due to the rapid growth of energy-hungry wireless multimedia services, telecom energy consumption is increasing at an extraordinary rate. Besides negative environmental impacts and higher energy bills for operators, it also affects user experience as improvements in battery technologies have not kept up with increasing mobile energy demands. Therefore, how to increase the energy efficiency (EE) of wireless communications has gained a lot of attention recently. Cooperative communication, where relays cooperatively retransmit the received data from the source to the destination, is seen as a promising technique to increases EE. Nevertheless, it requires more overhead than direct communication that needs to be taken into account for practical wireless cooperative networks. In order to achieve potential energy savings promised by cooperative communications in practical systems, overhead-aware cooperative relaying schemes with low overhead are imperative. For the case that not all relays can hear each other, i.e., hidden relays exist, an energy-efficient and a low-overhead cooperative relaying scheme is proposed. This scheme selects a subset of relays before data transmission, through the proactive participation of available relays using their local timers. Theoretical analysis of average EE under maximum transmission power constraint, using practical data packet length, and taking account of the overhead for obtaining channel state information (CSI), relay selection, and cooperative beamforming, is performed and a closed-form approximate expression for the optimal position of relays is derived. Furthermore, the overhead of the proposed scheme and the impact of data packet lengths on EE, are analysed. The analytical and simulation results reveal that the proposed scheme is significantly more energy-efficient than direct transmission, best relay selection, all relay selection, and a state-of-the-art existing cooperative relaying scheme. Moreover, the proposed scheme reduces the overhead and achieves higher energy savings for larger data packets. The conventional cooperative beamforming schemes rely on the feedback of CSIs of the best relays from the destination, which cause extra energy consumption and are prone to quantization errors in practical systems. In the case of clustered relays with location awareness and timer-based relay selection, where relays can overhear the transmission and know the location of each other, an energy-efficient overhead-aware cooperative relaying scheme is proposed, making CSI feedback from the destination dispensable. In order to avoid possible collisions between relay transmissions during best relays selection, a distributed mechanism for the selected relays to appropriately insert guard intervals before their transmissions is proposed. Average EE of the proposed scheme considering the related overhead is analysed. Moreover, the impact of the number of available relays, the number of selected relays and the location of relay cluster on EE is studied. The simulation results indicate that the proposed cooperative relaying scheme achieves higher EE than direct communication, best relay selection, and all relay selection for relay clusters located close to the source. Independent of the relay cluster location, the proposed scheme exhibits significantly higher EE than an existing cooperative relaying scheme. Device-to-device (D2D) communication in cellular networks that enable direct transmissions between user equipments (UEs) is seen as a promising way to improve both EE and spectral efficiency (SE). If the source UE (SUE) and the destination UE (DUE) are far away from each other or if the channel between them is too weak for direct transmission, then two-hop D2D communications, where relay UEs (RUEs) forward the SUE's data packets to the DUE, can be used. An energy- and spectral-efficient optimal adaptive forwarding strategy (OAFS) for two-hop D2D communications is proposed. In a distributed manner, the OAFS adaptively chooses between the best relay forwarding (BRF) and the cooperative relay beamforming (CRB) with the optimal number of selected RUEs, depending on which of them provides the higher instantaneous EE. In order to reduce the computational complexity of relay selection, a low-complexity sub-optimal adaptive forwarding strategy (SAFS) is proposed that selects between the BRF and the CRB with two RUEs by comparing their instantaneous EE. Theoretical analysis of the average EE and SE for the proposed adaptive forwarding strategies is performed considering maximum transmission power constraints, circuit power consumption and the overhead for the acquisition of CSI, forwarding mode selection and cooperative beamforming. The theoretical and simulation results show that the proposed OAFS and SAFS exhibit significantly higher EE and SE than the BRF, CRB, direct D2D communications and conventional cellular communications. For short to moderate SUE-to-DUE distances, SAFS is almost as energy- and spectral-efficient as OAFS

A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond

The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft

A model for predicting the probability of code beauty

Software maintenance is one of the most expensive phases of the software development life cycle. This cost increases more if maintenance is performed on poorly written code (less aesthetic). There exist a set of code writing patterns that developers need to follow to write good looking code. However, coding conforms ‘rules’ is not always possible. During software evolution, code goes through different changes, which are the main reasons for breaking rules of beautiful code. In this paper, we propose an AI (artificial intelligence) based model which will measure the beauty of a written code. The model is built on a set of code- based features that are used to assign the probability of being a beautiful code