Compressive Sensing Based Grant-Free Communication

Grant-free communication, where each user can transmit data without following the strict access grant process, is a promising technique to reduce latency and support massive users. In this thesis, compressive sensing (CS), which exploits signal sparsity to recover data from a small sample, is investigated for user activity detection (UAD), channel estimation, and signal detection in grant-free communication, in order to extract information from the signals received by base station (BS). First, CS aided UAD is investigated by utilizing the property of quasi-time-invariant channel tap delays as the prior information for the burst users in internet of things (IoT). Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. In particular, g-TIDCS and m-TIDCS do not require any prior knowledge of the number of active users like the existing approaches and therefore are more practical. Second, periodic communication as one of the salient features of IoT is considered. Two schemes, namely periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), are proposed to exploit the non-continuous temporal correlation of the received signal for joint UAD, channel estimation, and signal detection. The theoretical analysis and simulation results show that the PBOMP and PBSBL outperform the existing schemes in terms of the success rate of UAD, bit error rate (BER), and accuracy in period estimation and channel estimation. Third, UAD and channel estimation for grant-free communication in the presence of massive users that are actively connected to the BS is studied. An iteratively UAD and signal detection approach for the burst users is proposed, where the interference of the connected users on the burst users is reduced by applying a preconditioning matrix to the received signals at the BS. The proposed approach is capable of providing significant performance gains over the existing algorithms in terms of the success of UAD and BER. Last but not least, since the physical layer security becomes a critical issue for grant-free communication, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The proposed EA-pilots based approach possesses a high level of security by scrambling the eavesdropper’s normalized mean square error performance of channel estimation

Review of Recent Trends

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

Optimum Averaging of Superimposed Training Schemes in OFDM under Realistic Time-Variant Channels

The current global bandwidth shortage in orthogonal frequency division multiplexing (OFDM)-based systems motivates the use of more spectrally efficient techniques. Superimposed training (ST) is a candidate in this regard because it exhibits no information rate loss. Additionally, it is very flexible to deploy and it requires low computational cost. However, data symbols sent together with training sequences cause an intrinsic interference. Previous studies, based on an oversimplified channel (a quasi-static channel model) have solved this interference by averaging the received signal over the coherence time. In this paper, the mean square error (MSE) of the channel estimation is minimized in a realistic time-variant scenario. The optimization problem is stated and theoretical derivations are presented to attain the optimum amount of OFDM symbols to be averaged. The derived optimal value for averaging is dependent on the signal-to-noise ratio (SNR) and it provides a better MSE, of up to two orders of magnitude, than the amount given by the coherence time. Moreover, in most cases, the optimal number of OFDM symbols for averaging is much shorter, about 90% reduction of the coherence time, thus it provides a decrease of the system delay. Therefore, these results match the goal of improving performance in terms of channel estimation error while getting even better energy efficiency, and reducing delays.This work was supported by the Spanish National Project Hybrid Terrestrial/Satellite Air Interface for 5G and Beyond - Areas of Dif-cult Access (TERESA-ADA) [Ministerio de Economía y Competitividad (MINECO)/Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2017-90093-C3-2-R

Meta-learning applications for machine-type wireless communications

Abstract. Machine Type Communication (MTC) emerged as a key enabling technology for 5G wireless networks and beyond towards the 6G networks. MTC provides two service modes. Massive MTC (mMTC) provides connectivity to a huge number of users. Ultra-Reliable Low Latency Communication (URLLC) achieves stringent reliability and latency requirements to enable industrial and interactive applications. Recently, data-driven learning-based approaches have been proposed to optimize the operation of various MTC applications and allow for obtaining the desired strict performance metrics. In our work, we propose implementing meta-learning alongside other deep-learning models in MTC applications. First, we analyze the model-agnostic meta-learning algorithm (MAML) and its convergence for regression and reinforcement learning (RL) problems. Then, we discuss uncrewed aerial vehicles (UAVs) trajectory planning as a case study in mMTC and RL, illustrating the system model and the main challenges. Hence, we propose the MAML-RL formulation to solve the UAV path learning problem. Moreover, we address the MAML-based few-pilot demodulation problem in massive IoT deployments. Finally, we extend the problem to include the interference cancellation with Non-Orthogonal Multiple Access (NOMA) as a paradigm shift towards non-orthogonal communication thanks to its potential to scale well in massive deployments. We propose a novel, data-driven, meta-learning-aided NOMA uplink model that minimizes the channel estimation overhead and does not require perfect channel knowledge. Unlike conventional deep learning successive interference cancellation (SICNet), Meta-Learning aided SIC (meta-SICNet) can share experiences across different devices, facilitating learning for new incoming devices while reducing training over- head. Our results show the superiority of MAML performance in addressing many problems compared to other deep learning schemes. The simulations also prove that MAML can successfully solve the few-pilot demodulation problem and achieve better performance in terms of symbol error rates (SERs) and convergence latency. Moreover, the analysis confirms that the proposed meta-SICNet outperforms classical SIC and conventional SICNet as it can achieve a lower SER with fewer pilots

A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond

Today's wireless networks allocate radio resources to users based on the orthogonal multiple access (OMA) principle. However, as the number of users increases, OMA based approaches may not meet the stringent emerging requirements including very high spectral efficiency, very low latency, and massive device connectivity. Nonorthogonal multiple access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. In this tutorial style paper, we target providing a unified model for NOMA, including uplink and downlink transmissions, along with the extensions tomultiple inputmultiple output and cooperative communication scenarios. Through numerical examples, we compare the performances of OMA and NOMA networks. Implementation aspects and open issues are also detailed.Comment: 25 pages, 10 figure

An interference-reducing precoding for SCMA multicast design based on complementary sequences

In a multi-group multicast sparse code multiple access (SCMA) system, one base station multicasts common messages to multiple multicast groups via different code books. To accommodate more user terminals (UTs), traditional multicast systems have multiple transmitters, each of which works in one-to-many mode. In this way, each UT is subject to inter-transmitter interference. Considering the high degrees of freedom for transmitting and receiving, it is difficult to separate the desired signal from interference signals. Therefore, an interference-reducing precoding scheme is required to ensure the reliability of SCMA multicast communication system. For the SCMA multicast system design, we present three necessary conditions that the interference-reducing matrix should satisfy. Then, the precoding matrix satisfying the three necessary conditions simultaneously is designed by utilizing the complementary sequences (CS) and complete complementary sequences (CCS). In this context, we consider two scenarios with different transmission modes (single-cell and multiple-cell) and different precoding schemes (based on CS and CCS). Simulation results show that proposed transmission schemes can significantly reduce the bit error rate of multicast groups while ensuring the communication throughput, and behave a superior performance over other alternatives. Moreover, theoretical and simulation results also prove that the proposed precoding vectors have perfect average power radiation and omnidirectional coverage performance

Spectrally efficient Non-Orthogonal Multiple Access (NOMA) techniques for future generation mobile systems

With the expectation of over a 1000-fold increase in the number of connected devices by 2020, efficient utilization of the limited bandwidth has become ever more important in the design of mobile wireless systems. Furthermore, the ever-increasing demand for higher data rates has made it necessary for a new waveform design that satisfies not only throughput demands, but network capacity as well. One such technique recently proposed is the non-orthogonal multiple access (NOMA) which utilizes the distance-dependent power domain multiplexing, based on the principles of signal superposition. In this thesis, new spectrally efficient non-orthogonal signal techniques are proposed. The goal of the schemes is to allow simultaneous utilization of the same time frequency network resources. This is achieved by designing component signals in both power and phase domain such that users are precoded or preformed to form a single and uniquely decodable composite signal. The design criteria are based on maximizing either the sum rate or spectral efficiency, minimizing multi-user interference and detection ambiguity, and maximizing the minimum Euclidean distance between the composite constellation points. The design principles are applied in uplink, downlink and coordinated multipoint (CoMP) scenarios. We assume ideal channel state with perfect estimation, low mobility and synchronization scenarios so as to prove the concept and serve as a bound for any future work in non-ideal conditions. Extensive simulations and numerical analysis are carried to show the superiority and compatibility of the schemes. First, a new NOMA signal design called uplink NOMA with constellation precoding is proposed. The precoding weights are generated at the eNB based on the number of users to be superposed. The eNB signals the precoding weights to be employed by the users to adjust their transmission. The adjustments utilize the channel state information estimated from common periodic pilots broadcasted by the eNB. The weights ensure the composite received signal at the eNB belongs to the pre-known constellation. Furthermore, the users precode to the eNB antenna that requires the least total transmit power from all the users. At the eNB, joint maximum likelihood (JML) detection is employed to recover the component signals. As the composite constellation is as that of a single user transmitting that same constellation, multiple access interference can be viewed as absent, which allows multiple users to transmit at their full rates. Furthermore, the power gain achieved by the sum of the component signals maximizes the sum rate. Secondly, the constellation design principle is employed in the downlink scenario. In the scheme, called downlink NOMA with constellation preforming, the eNB preforms the users signal with power and phase weights prior to transmission. The preforming ensures multi-user interference is eliminated and the spectral efficiency maximized. The preformed composite constellation is broadcasted by the eNB which is received by all users. Subsequently, the users perform JML detection with the designed constellation to extract their individual component signals. Furthermore, improved signal reliability is achieved in transmit and receive diversity scenarios in the schemes called distributed transmit and receive diversity combining, respectively. Thirdly, the constellation preforming on the downlink is extended to MIMO spatial multiplexing scenarios. The first MIMO scheme, called downlink NOMA with constellation preforming, each eNB antenna transmits a preformed composite signal composed of a set of multiple users’ streams. This achieves spatial multiplexing with diversity with less transmit antennas, reducing costs associated with multiple RF chains, while still maximizing the sum rate. In the second MIMO scheme, a highly spectrally efficient MIMO preforming scheme is proposed. The scheme, called group layer MIMO with constellation preforming, the eNB preforms to a specific group of users on each transmit antenna. In all the schemes, the users perform JML detection to recover their signals. Finally, the adaptability of the constellation design is shown in CoMP. The scheme, called CoMP with joint constellation processing, the additional degrees of freedom, in form of interfering eNBs, are utilized to enable spatial multiplexing to a user with a single receive antenna. This is achieved by precoding each stream from the coordinating eNB with weights signalled by a central eNB. Consequently, the inter-cell interference is eliminated and the sum-rate maximized. To reduce the total power spent on precoding, an active cell selection scheme is proposed where the precoding is employed on the highest interferers to the user. Furthermore, a power control scheme is applied the design principle, where the objective is to reduce cross-layer interference by adapting the transmission power to the mean channel gain

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed