The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. Phase noise (PN) is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the modest output power of the power amplifier demands limits on peak to average power ratio (PAPR) signal design. In this work, we design a PN-robust, low PAPR single-carrier (SC) waveform by geometrically shaping the constellation and adapting the pulse shaping filter pair under practical PN modelling and adjacent channel leakage ratio (ACLR) constraints for a given excess bandwidth. We optimize the waveforms under conventional and state-of-the-art PN-aware demappers. Moreover, we introduce a neural-network (NN) demapper enhancing transceiver adaptability.We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a design that is numerically robust to PN, while adhering to PAPR and ACLR constraints. The results substantiate the efficacy of the method, yielding up to 2.5 dB in the requiredEb/N0under stronger PN along with a PAPR reduction of 0.5 dB. Moreover, PAPR reductions up to 1.2 dB are possible with competitive BLER and SE performance in both low and high PN conditions.

The large untapped spectrum in the sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. One of the challenges in radio communications in sub-THz is the hardware impairments. Specifically, phase noise is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the moderate output power of the sub-THz power amplifier demands limits on peak to average power ratio (PAPR) signal design. Single carrier frequency domain equalization (SC-FDE) has been identified as a suitable candidate for sub-THz, although some challenges such as phase noise and PAPR still remain to be tackled. In this work, we design a phase noise robust, modest PAPR SC waveform by geometrically shaping the constellation under practical conditions. We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a constellation design that is numerically robust to phase noise, while maintaining a relatively low PAPR compared to the conventional waveforms.

Turbo-AI is an iterative Machine Learning (ML) based channel estimator, processed through frequency, time and spatial domains consecutively with low complexity. Within the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communications, we embed Turbo-AI into Demodulation Reference Signal (DMRS) processing in this paper. The extension is not technically obvious, comparing to our previous Turbo-AI paper series, because the main challenges come from not only estimating the channel response exactly for the DMRS pilot resource elements, but also precisely interpolating the channel response for the data resource elements, especially when the user mobility or equivalently the sparsity of the pilot structure significantly increases. Throughout this paper, we propose a universal interpolation approach, named Firecracker Algorithm, which is jointly introduced to DMRS-Turbo-AI. Numerical results show that DMRS-Turbo-AI can deliver high quality channel estimation for the users with high mobility, and demonstrate that Firecracker Algorithm is even capable of tracking the channel of a user, who moves at a supersonic speed.

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complexity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3rd generation partnership project (3GPP) calibrated channels, showing the validity of our proposal.

Targeting to potential evolution of future wireless systems, precise channel estimation is regarded as one of the fundamental prerequisites. In this paper, we focus on Machine Learning (ML) based channel estimation with Turbo-AI, which is an iterative training approach, and can monotonically reduce the post-processing noise variance of Gaussian inputs after each iteration, by updating the Neural Network (NN) models with re-training. After discussing about the initial results of Turbo-AI as Part I in our introductory paper, we will now deploy the same principle in a multicarrier system from a more practical view point. Multi-dimensional Turbo-AI will estimate the channel in an iterative manner through frequency, time and spatial domain cooperatively. Since the complexity of traditional channel estimation method will be extremely high due to the multi-dimensional data structure, Turbo-AI can be regarded as a complementary solution to balance the performance and complexity. Throughout this paper, we exploit 5G compliant link level simulator to show that genie-aided upper bound can be approached by Turbo-AI. Challenges of realizing Turbo-AI based channel estimation in practical systems for future wireless communication towards Beyond 5G (B5G) are focused on.

Channel estimation belongs to one of the potential applications, that can exploit Artificial Intelligence (AI) and Machine Learning (ML) to enhance Physical Layer (PHY) performance in the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communication systems. In this paper, we focus on the ML-based channel estimation for 2-Dimensional (2D) antenna arrays. Due to the extremely high computational requirement for 2D massive arrays with Conventional Training, we exploit the 2D Kronecker covariance model to perform Subspace Training for the vertical and horizontal spatial domains independently, which achieves a complexity cost saving factor O(M4N4)/O(MN4+ NM4) for an M × N 2D-array. Furthermore, we propose an iterative training approach, referred to as Turbo-AI. Along with Subspace Training, the new approach can monotonically reduce the effective variance of additive noise of the observation, by updating the Neural Network (NN) models with re-training. Furthermore, we propose a concept, named Universal Training. It allows to use one NN for a wide range of Signal-to-Noise-Ratio (SNR) operation points and spatial angles, which can greatly simplify Turbo-AI usage. Numerical results exhibit that Turbo-AI can tightly approach the genie-aided channel estimation bound, especially at low SNR.

The strict latency and reliability requirements of ultra-reliable low-latency communications (URLLC) use cases are among the main drivers in fifth generation (5G) network design. Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission. This prediction is exploited in the LA context to maximize the spectral efficiency while guaranteeing reliability at an arbitrary level. Numerical results are compared with state of the art interference prediction techniques for LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.

Vehicular networking belongs to one of the most attractive techniques under the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communication system. It opens diverse new concepts and applications, e.g. Vehicular-to-Vehicular (V2V), Vehicular-to-Infrastructure (V2I). One of the key components to support autonomous driving is to allow vehicles to share information about their awareness of the environment, i.e. perception layer of neighbor vehicles. In this paper, we focus on low overhead, low latency MAC layer design, enabling perception layer sharing in V2V environment, and PHY layer design, enabling Multi-User Detection (MUD) with V2V environment by means of Non-Orthogonal Multiple Access (NOMA), which is one of the key physical layer technologies of 5G New Radio (NR). Numerical results of link level simulation exhibit that our solution generates and exchanges the NOMA user signature flexibly, i.e. supports both synchronous and asynchronous traffic with low bit error rate. Furthermore, it enables small packet communication with low computational complexity, thus low latency.

This paper extends the weak secrecy results of Liu et al. for broadcast channels with two confidential messages to strong secrecy. Our results are based on an extension of the techniques developed by Hou and Kramer on bounding Kullback-Leibler divergence in the context of resolvability and effective secrecy.

This paper deals with the challenge of providing information-theoretic secrecy in a two-hop wireless channel with multiple untrusted relay nodes. Instead of perfect secrecy (in the information-theoretic sense), of interest here is a cross-layer approach where only parts of relayed information are protected at the physical layer against eavesdropping at relay nodes. For both decode-and-forward and amplify-and-forward relaying strategies, we present a novel framework for designing secrecy schemes for two-hop relay channels that ensure some predefined level of partial secrecy. The paper is concluded by some numerical simulations to show the interplay between different system parameters such as the the rate, secrecy factor, the number of relays and antennas.

In this paper, we design the optimal precoding matrices for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay networks. Specifically, we consider a dual-hop relay network and minimize the total power consumed by source and relay under predetermined quality of service (QoS) constraints, i.e., mean square error (MSE) constraints. By using majorization theory, we simplify the matrix-valued problem into a scalar-valued one. Since the problem is non-convex, we then propose two convex suboptimal problems that provide the upper and lower bound of the original objectives. Numerical results demonstrate that the lower bound and the upper bound are tight in high signal-to-noise ratio (SNR).