Performance comparison of interference alignment algorithms in an energy harvesting scenario

Proceeding of: 12th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP), 20-22, July 2020, (Online).Energy-efficient interference alignment (IA) algorithms that simultaneously satisfy continuous coverage and green communications requirements are an open problem in 5G cellular networks. IA is one of the most promising techniques to eliminate interference. However, a recent assumption in green communications is to utilize interference signals as an energy supply for electronic devices. In this scenario, simultaneous wireless information and power transfer (SWIPT) schemes are a common technique to harvest energy from wireless signals. This paper addresses a performance comparison of different IA algorithms to guarantee the best trade-off between sum-rate and the amount of harvested energy, with an in-depth analysis.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391. Also, this work has been supported in part by the Spanish National Project TERESA-ADA, funded by (MINECO/AEI/FEDER, UE) under grant TEC2017-90093-C3-2-R

Hardware Evaluation of Interference Alignment Algorithms Using USRPs for Beyond 5G Networks

Proceedings of the 20th IEEE Region 8 EUROCON Conference, EUROCON 2023, 6-8 July 2023, Turín, ItalyNetwork densification is a key technology to achieve the spectral efficiency (SE) expected in 5G wireless networks and beyond. However, the proximity between transmitters and receivers increases the interference levels, becoming a major drawback. To overcome this problem, several interference management techniques have been proposed to increase the signal-to-interference-plus-noise ratio (SINR). Interference alignment (IA) algorithms have been extensively studied due to their capability to achieve optimal degrees of freedom (DoFs) in interference channels (ICs). Nevertheless, most of the works are limited to a purely theoretical analysis based on non-realistic assumptions such as perfect channel state information (CSI) and the synchronization of all nodes in the network. To the best of our knowledge, only a few articles address the IA implementation using reconfigurable hardware. To cover this lack, this paper proposes a practical design of the IA algorithm based on the SINR maximization, known as MAX-SINR, considering a multi-user IC. Each transmitter and receiver is implemented on the National Instruments USRP-2942. A practical solution for the channel estimation and synchronization stages in an IC, that are usually omitted in theoretical works, is developed. The performance of the proposed implementation is shown in terms of the SINR gain, SE, and bit error rate (BER). Unlike previous works, all the results are based on real measurements providing valuable insights into the performance of IA algorithms.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391. Also, this work has been partially funded by the Spanish National project IRENE-EARTH (PID2020- 115323RB-C33 / AEI / 10.13039/501100011033

Optimal sensing policy for energy harvesting cognitive radio systems

Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391. Also, this work has been supported in part by the Spanish National Project TERESA-ADA, funded by (MINECO/AEI/FEDER, UE) under grant TEC2017-90093-C3-2-R

Deployment of clustered-based small cells in interference-limited dense scenarios: analysis, design and trade-offs

Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In this scenario, most users experience significant interference levels from neigh-bouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely pro-posed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification lev-els envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered-IA systems relied on oversimplified channel models and/or enforced single-stream transmission. In this paper, we explore an ultradense deployment of small-cells (SCs) to pro-vide coverage in 5G/B5G wireless networks. A novel cluster design based on size-restricted k-means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial mul-tiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade-offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral-efficiency degradation due to the inter-cluster interference in ultra-dense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both, intra-cluster and inter-cluster interference. Simulation results provide key insights for the deployment of small cells in interference-limited dense scenarios.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391. We also acknowledge the Ministerio de Ciencia, Innovación y Universidades (MCIU), the Agencia Estatal de Investigacion (AEI) and the European Regional Development Funds (ERDF) for its support to the Spanish National Project TERESA (subprojects TEC2017-90093-C3-2-R and TEC2017-90093-C3-3-R).Publicad

MOOC as a Way of Dissemination, Training and Learning of Telecommunication Engineering

In this chapter, the use of massive open online courses (MOOCs) for the dissemination, training capabilities and learning of telecommunication engineering is described taking as example the successful MOOC ‘Ultra- Dense Networks for 5G and its Evolution’ developed under the European innovative training network (ITN) TeamUp5G. MOOCs are usually understood as a way of teaching or learning for massive potential students. Indeed, this is the main goal of any MOOC. However, we also propose its use for training and dissemination. The ITN TeamUp5G is a training network for 15 PhD students of seven different institutions (universities and companies) where the students make research on different interconnected topics for the common goal of Ultra dense networks for 5G. At the same time they researched, they prepared a MOOC to disseminate their most recent advances and their challenges. For the MOOC, they needed to collect their thoughts, organizse their knowledge and establish a common vision of the whole system. The cooperative work, the cross-related meetings and, the preparation of all the materials for the MOOC were very interesting and useful in their training process. The whole experience of designing and creating the MOOC is described in detail along with the challenges and lessons learned.info:eu-repo/semantics/acceptedVersio

MOOC on "Ultra-dense networks for 5G and its evolution": challenges and lessons learned

Proceeding of: 31st Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE 2022), Coimbra, Portugal, 26 June-1 July 2022Many of the new mobile communication devices will be things that power and monitor our homes, city infrastructure and transport. Controlling drones thousands of miles away, performing remote surgeries or being immersed in video with no latency will also be a huge game changer. Those are some of the few things that make the fifth generation (5G) a revolution expected to be a thrust to the economy. To that end, the design and density of deployment of new networks is also changing becoming more dense, what introduces new challenges into play. What else will it add to previous generations? The MOOC about Ultra-dense networks for 5G and its evolution has been prepared by the researchers of an European MSCA ITN, named TeamUp5G, and introduces the most important technologies that support 5G mobile communications, with an emphasis on increasing capacity and reducing power. The content spans from aspects of communication technologies to use cases, prototyping and the future ahead, not forgetting issues like interference management, energy efficiency or spectrum management. The aim of the MOOC is to fill the gap in graduation and post-graduation learning on content related to emerging 5G technologies and its applications, including the future 6G. The target audience involves engineers, researchers, practitioners and students. This paper describes the content and the learning outcomes of the MOOC, the main tasks and resources involved in its creation, the joint contributions from the academic and non-academic sector, and aspects like copyright compliance, quality assurance, testing and details on communication and enrollment, followed by the discussion of the lessons learned.This work has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391

Spectral Efficiency of Precoded 5G-NR in Single and Multi-User Scenarios under Imperfect Channel Knowledge: A Comprehensive Guide for Implementation

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off between performance, complexity, and signal overhead. This paper aims to evaluate the performance bounds in SE achieved by the 5G-NR precoding matrices in single-user (SU) and multi-user (MU) MIMO systems, namely Type I and Type II, respectively. The implementation of these codebooks is covered providing a comprehensive guide with a detailed analysis. The performance of the 5G-NR precoder is compared with theoretical precoding techniques such as singular value decomposition (SVD) and block-diagonalization to quantify the margin of improvement of the standardized methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors on the system performance is analyzed in both SU and MU-MIMO cases. For a realistic framework, the SE values are obtained for a practical deployment based on a clustered delay line (CDL) channel model. These results provide valuable insights for system designers about the implementation and performance of the 5G-NR precoding matrices.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391. Also, this work has been partially funded by the Spanish National project IRENE-EARTH (PID2020-115323RB-C33/AEI/10.13039/501100011033)

Performance bounds with precoding matrices compliant with standardized 5G-NR for MIMO transmission

Proceeding of: IEEE Conference on Standards for Communications and Networking (CSCN 2022), 28-30 November 2022, Thessaloniki, GreeceAdvanced multiple-input multiple-output (MIMO) beamforming techniques are crucial in 5G New Radio (NR) to achieve the expected data rate values. Therefore, the 3rd Generation Partnership Project (3GPP) has proposed a codebook-based MIMO precoding strategy to provide high diversity, array gain, and spatial multiplexing. The main goal is to obtain a tradeoff between performance, signal overhead, and complexity. The precoding matrix is selected from a set of predefined codebooks based on the knowledge that the 5G-NR base station (gNB) acquires about the channel status. In this work, a detailed study of the precoding matrix design is provided following the guidelines reported in the technical specifications 38-211 and 38-214 of the 3GPP. An analysis of the performance in terms of spectral efficiency (SE) achieved by the 5G-NR precoding matrices is illustrated for a single-user MIMO scenario. These results are contrasted against the optimal singular value decomposition (SVD) solution in order to explore the gap between the standardized precoding proposal and the optimal one. Several values of signal-to-noise ratio (SNR) and different antenna array configurations are considered. Moreover, the multiplexing gain for a different number of parallel data streams is evaluated. Numerical results show the SE bounds that can be obtained with the 5G-NR precoding matrices. These insights are of key importance for practical implementation of precoding strategies in 5G-NR systems and beyond

Spectral Efficiency of Precoded 5G-NR in Single and Multi-User Scenarios under Imperfect Channel Knowledge: A Comprehensive Guide for Implementation

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off between performance, complexity, and signal overhead. This paper aims to evaluate the performance bounds in SE achieved by the 5G-NR precoding matrices in single-user (SU) and multi-user (MU) MIMO systems, namely Type I and Type II, respectively. The implementation of these codebooks is covered providing a comprehensive guide with a detailed analysis. The performance of the 5G-NR precoder is compared with theoretical precoding techniques such as singular value decomposition (SVD) and block-diagonalization to quantify the margin of improvement of the standardized methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors on the system performance is analyzed in both SU and MU-MIMO cases. For a realistic framework, the SE values are obtained for a practical deployment based on a clustered delay line (CDL) channel model. These results provide valuable insights for system designers about the implementation and performance of the 5G-NR precoding matrices