Meeting IMT 2030 Performance Targets: The Potential of OTFDM Waveform and Structural MIMO Technologies

The white paper focuses on several candidate technologies that could play a crucial role in the development of 6G systems. Two of the key technologies explored in detail are Orthogonal Time Frequency Division Multiplexing (OTFDM) waveform and Structural MIMO (S-MIMO)

Filtered Multicarrier Transmission

Orthogonal frequency‐division multiplexing (OFDM) has been adopted as the waveform of choice in the existing and emerging broadband wireless communication systems for a number of advantages it can offer. Nevertheless, investigations of more advanced multicarrier transmission schemes have continued with the aim of eliminating or mitigating its essential limitations. This article discusses multicarrier schemes with enhanced spectrum localization, which manage to reduce the spectral sidelobes of plain OFDM that are problematic in various advanced communication scenarios. These include schemes for enhancing the OFDM waveform characteristics through additional signal processing as well as filter‐bank multicarrier (FBMC) waveforms utilizing frequency‐selective filter banks instead of plain (inverse) discrete Fourier transform processing for waveform generation and demodulation.acceptedVersionPeer reviewe

Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View

The next-generation wireless technologies, commonly referred to as the sixth generation (6G), are envisioned to support extreme communications capacity and in particular disruption in the network sensing capabilities. The terahertz (THz) band is one potential enabler for those due to the enormous unused frequency bands and the high spatial resolution enabled by both short wavelengths and bandwidths. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of the advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurements for THz band. The transceiver requirements, architectures, technological challenges, and approaches together with means to compensate for the high propagation losses by appropriate antenna and beamforming solutions. We survey also several system technologies required by or beneficial for THz systems. The synergistic design of sensing and communications is explored with depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the issues and challenges that are open for further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications Surveys & Tutorial

Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-powered Receiver Design for Terahertz Integrated Sensing and Communication Systems

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the $10^{-3}$ BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise, and multi-target estimation

On the feasibility and applications of in-band full-duplex radios for future wireless networks

Due to the continuous increase of the demands for the wireless network’s capacity, in-band full-duplex (IBFD) has recently become a key research topic due to its potential to double spectral efficiency, reduce latency, enhance emerging applications, etc., by transmitting and receiving simultaneously over the same channel. Meanwhile, many studies in the literature experimentally demonstrated the feasibility of IBFD radios, which leads to the belief that it is possible to introduce IBFD in the standard of the next-generation networks. Therefore, in this thesis, we timely study the feasibility of IBFD and investigate its advantages for emerging applications in future networks. In the first part, we investigate the interference suppression methods to maximize the IBFD gain by minimizing the effects of self-interference (SI) and co-channel interference (CCI). To this end, we first study a 3-step self-interference cancellation (SIC) scheme. We focus on the time domain-based analog canceller and nonlinear digital canceller, explaining their rationale, demonstrating their effectiveness, and finding the optimal design by minimizing the residual effects. To break the limitation of conventional electrical radio frequency (RF) cancellers, we study the photonic-assisted canceller (PAC) and propose a new design, namely a fiber array-based canceller. We propose a new low-complexity tuning algorithm for the PAC. The effectiveness of the proposed fiber array canceller is demonstrated via simulations. Furthermore, we construct a prototype of the fiber array canceller with two taps and carry out experiments in real-world environments. Results show that the 3-step cancellation scheme can bring the SI close to the receiver's noise floor. Then, we consider the multiple-input multiple-output (MIMO) scenarios, proposing to employ hybrid RF-digital beamforming to reduce the implementation cost and studying its effects on the SIC design. Additionally, we propose a user allocation algorithm to reduce the CCI from the physical layer. A heterogeneous industrial Internet of Things (IIoT) scenario is considered, while the proposed algorithm can be generalized by modifying the parameters to fit any other network. In the second part, we study the beamforming schemes for IBFD multi-cell multi-user (IBFD-MCMU) networks. The transceiver hardware impairments (HWIs) and channel uncertainty are considered for robustness. We first enhance zero-forcing (ZF) and maximum ratio transmission and combining (MRTC) beamforming to be compatible with IBFD-MCMU networks in the presence of multi-antenna users. Then, we study beamforming for SIC, which is challenging for MCMU networks due to the limited antennas but complex interference. We propose a minimum mean-squared error (MMSE)-based scheme to enhance the SIC performance while minimizing its effects on the sum rate. Furthermore, we investigate a robust joint power allocation and beamforming (JPABF) scheme, which approaches the performance of existing optimal designs with reduced complexity. Their performance is evaluated and compared through 3GPP-based simulations. In the third part, we investigate the advantages of applying IBFD radios for physical layer security (PLS). We focus on a channel frequency response (CFR)-based secret key generation (SKG) scheme in MIMO systems. We formulate the intrinsic imperfections of IBFD radios (e.g., SIC overheads and noise due to imperfect SIC) and derive their effects on the probing errors. Then we derive closed-form expressions for the secret key capacity (SKC) of the SKG scheme in the presence of a passive eavesdropper. We analyze the asymptotic behavior of the SKC in the high-SNR regime and reveal the fundamental limits for IBFD and half-duplex (HD) radios. Based on the asymptotic SKC, numerical results illustrate that effective analog self-interference cancellation (ASIC) is the basis for IBFD to gain benefits over HD. Additionally, we investigate essential processing for the CFR-based SKG scheme and verify its effectiveness via simulations and the National Institute of Standards and Technology (NIST) test. In the fourth part, we consider a typical application of IBFD radios: integrated sensing and communication (ISAC). To provide reliable services in high-mobility scenarios, we introduce orthogonal time frequency space (OTFS) modulation and develop a novel framework for OTFS-ISAC. We give the channel representation in different domains and reveal the limitations and disadvantages of existing ISAC frameworks for OTFS waveforms and propose a novel radar sensing method, including a conventional MUSIC algorithm for angle estimation and a delay-time domain-based range and velocity estimator. Additionally, we study the communication design based on the estimated radar sensing parameters. To enable reliable IBFD radios in high-mobility scenarios, a SIC scheme compatible with OTFS and rapidly-changing channels is proposed, which is lacking in the literature. Numerical results demonstrate that the proposed ISAC waveform and associated estimation algorithm can provide both reliable communications and accurate radar sensing with reduced latency, improved spectral efficiency, etc

6G wireless communications networks: a comprehensive survey

The commercial fifth-generation (5G) wireless communications networks have already been deployed with the aim of providing high data rates. However, the rapid growth in the number of smart devices and the emergence of the Internet of Everything (IoE) applications, which require an ultra-reliable and low-latency communication, will result in a substantial burden on the 5G wireless networks. As such, the data rate that could be supplied by 5G networks will unlikely sustain the enormous ongoing data traffic explosion. This has motivated research into continuing to advance the existing wireless networks toward the future generation of cellular systems, known as sixth generation (6G). Therefore, it is essential to provide a prospective vision of the 6G and the key enabling technologies for realizing future networks. To this end, this paper presents a comprehensive review/survey of the future evolution of 6G networks. Specifically, the objective of the paper is to provide a comprehensive review/survey about the key enabling technologies for 6G networks, which include a discussion about the main operation principles of each technology, envisioned potential applications, current state-of-the-art research, and the related technical challenges. Overall, this paper provides useful information for industries and academic researchers and discusses the potentials for opening up new research directions

TRANSMISSION PERFORMANCE OPTIMIZATION IN FIBER-WIRELESS ACCESS NETWORKS USING MACHINE LEARNING TECHNIQUES

The objective of this dissertation is to enhance the transmission performance in the fiber-wireless access network through mitigating the vital system limitations of both analog radio over fiber (A-RoF) and digital radio over fiber (D-RoF), with machine learning techniques being systematically implemented. The first thrust is improving the spectral efficiency for the optical transmission in the D-RoF to support the delivery of the massive number of bits from digitized radio signals. Advanced digital modulation schemes like PAM8, discrete multi-tone (DMT), and probabilistic shaping are investigated and implemented, while they may introduce severe nonlinear impairments on the low-cost optical intensity-modulation-direct-detection (IMDD) based D-RoF link with a limited dynamic range. An efficient deep neural network (DNN) equalizer/decoder to mitigate the nonlinear degradation is therefore designed and experimentally verified. Besides, we design a neural network based digital predistortion (DPD) to mitigate the nonlinear impairments from the whole link, which can be integrated into a transmitter with more processing resources and power than a receiver in an access network. Another thrust is to proactively mitigate the complex interferences in radio access networks (RANs). The composition of signals from different licensed systems and unlicensed transmitters creates an unprecedently complex interference environment that cannot be solved by conventional pre-defined network planning. In response to the challenges, a proactive interference avoidance scheme using reinforcement learning is proposed and experimentally verified in a mmWave-over-fiber platform. Except for the external sources, the interference may arise internally from a local transmitter as the self-interference (SI) that occupies the same time and frequency block as the signal of interest (SOI). Different from the conventional subtraction-based SI cancellation scheme, we design an efficient dual-inputs DNN (DI-DNN) based canceller which simultaneously cancels the SI and recovers the SOI.Ph.D