A High Altitude Platform-Based 3D Geometrical Channel Model for Beamforming Characterization in Future 6G Flying Ad-Hoc Networks

Growing requirements of future wireless communication systems, such as high data rates, high reliability, and low latency, make the active usage of Non-Terrestrial Networks (NTN) an inevitable necessity. In this regard, High Altitude Platforms (HAPs) have drawn great attention in recent years due to their unique characteristics such as high coverage, long operational durability, and ad-hoc movement. However, for the active usage of HAPs, channel models for their various usage scenarios must be well-defined, especially in those cases where the sophisticated multiple-input multiple-output (MIMO) techniques, such as beamforming, are utilized to increase the data rate. Therefore, in this study, an air-to-air (A2A) three dimensional (3D) geometrical channel model is proposed to characterize the beamforming capabilities of non-stationary HAP networks operating at millimeter wave (mmWave) frequency band. In this regard, the 3D geometry of the two HAPs in the air is analyzed, and the effect of Doppler due to the movement of HAPs is interrogated as well as its effect on the signal-to-noise ratio (SNR). The final outputs of this study show that the proposed A2A channel model is applicable to characterize the future sixth generation (6G) HAP networks when the mmWave is used to utilize beamforming with a large number of antennas.

Fast Network Recovery from Large-Scale Disasters: A Resilient and Self-Organizing RAN Framework

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed.

OFDM-RSMA: Robust Transmission under Inter-Carrier Interference

Rate-splitting multiple access (RSMA) is a multiple access scheme to mitigate the effects of the multi-user interference (MUI) in multi-antenna systems. In this study, we leverage the interference management capabilities of RSMA to tackle the issue of inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) waveform. We formulate a sum-rate maximization problem to find the optimal subcarrier and power allocation for downlink transmission in a two-user system using RSMA and OFDM. A weighted minimum mean-square error (WMMSE)-based algorithm is proposed to obtain a solution for the formulated non-convex problem. We show that the marriage of rate-splitting (RS) with OFDM provides complementary strengths to cope with peculiar characteristic of wireless medium and its performance-limiting challenges including inter-symbol interference (ISI), MUI, ICI, and inter-numerology interference (INI). The sum-rate performance of the proposed OFDM-RSMA scheme is numerically compared with that of conventional orthogonal frequency division multiple access (OFDMA) and OFDM-non-orthogonal multiple access (NOMA). It is shown that the proposed OFDM-RSMA outperforms OFDM-NOMA and OFDMA in diverse propagation channel conditions owing to its flexible structure and robust interference management capabilities.

Performance Analysis of OTSM under Hardware Impairments in Millimeter-Wave Vehicular Communication Networks

Orthogonal time sequency multiplexing (OTSM) has been recently proposed as a single-carrier (SC) waveform offering similar bit error rate (BER) to multi-carrier orthogonal time frequency space (OTFS) modulation in doubly-spread channels under high mobilities; however, with much lower complexity making OTSM a promising candidate for low-power millimeter-wave (mmWave) vehicular communications in 6G wireless networks. In this paper, the performance of OTSM-based homodyne transceiver is explored under hardware impairments (HIs) including in-phase and quadrature imbalance (IQI), direct current offset (DCO), phase noise, power amplifier non-linearity, carrier frequency offset, and synchronization timing offset. First, the discrete-time baseband signal model is obtained in vector form under the mentioned HIs. Then, the system input-output relations are derived in time, delay-time, and delay-sequency (DS) domains in which the parameters of HIs are incorporated. Analytical studies demonstrate that noise stays white Gaussian and effective channel matrix is sparse in the DS domain under HIs. Also, DCO appears as a DC signal at receiver interfering with only the zero sequency over all delay taps in the DS domain; however, IQI redounds to self-conjugated fully-overlapping sequency interference. Simulation results reveal the fact that with no HI compensation (HIC), not only OTSM outperforms plain SC waveform but it performs close to uncompensated OTFS system; however, HIC is essentially needed for OTSM systems operating in mmWave and beyond frequency bands.

Network-Independent and User-Controlled RIS: An Experimental Perspective

The march towards 6G is accelerating and future wireless network architectures require enhanced performance along with significant coverage especially, to combat impairments on account of the wireless channel. Reconfigurable intelligent surface (RIS) technology is a promising solution, that has recently been considered as a research topic in standards, to help manipulate the channel in favor of users needs. Generally, in experimental RIS systems, the RIS is either connected to the transmitter (Tx) or receiver (Rx) through a physical backhaul link and it is controlled by the network and requires significant computation at the RIS for codebook (CB) designs. In this paper, we propose a practical user-controlled RIS system that is isolated from the network to enhance communication performance and provide coverage to the user based on its location and preference. Furthermore, a low-complexity algorithm is proposed to aid in CB selection for the user, which is performed through the wireless cloud to enable a passive and energy efficient RIS. Extensive experimental test-bed measurements demonstrate the enhanced performance of the proposed system while both results match and validate each other.

Quasisynchronous LoRa for LEO Nanosatellite Communications

Perfect synchronization in LoRa communications between Low Earth Orbit (LEO) satellites and ground base stations is still challenging, despite the potential use of atomic clocks in LEO satellites, which offer high precision. Even by incorporating atomic clocks in LEO satellites, their inherent precision can be leveraged to enhance the overall synchronization process, perfect synchronization is infeasible due to a combination of factors such as signal propagation delay, Doppler effects, clock drift and atmospheric effects. These challenges require the development of advanced synchronization techniques and algorithms to mitigate their effects and ensure reliable communication from / to LEO satellites. However, maintaining acceptable levels of synchronization rather than striving for perfection, quasisynchronous (QS) communication can be adopted which maintains communication reliability, improves resource utilization, reduces power consumption, and ensures scalability as more devices join the communication. Overall, QS communication offers a practical, adaptive, and robust solution that enables LEO satellite communications to support the growing demands of IoT applications and global connectivity. In our investigation, we explore different chip waveforms such as rectangular and raised cosine. Furthermore, for the first time, we study the Symbol Error Rate (SER) performance of QS LoRa communication, for different spreading factors (SF), over Additive White Gaussian Noise (AWGN) channels.

Time-Frequency Warped Waveforms for Well-Contained Massive Machine Type Communications

This paper proposes a novel time-frequency warped waveform for short symbols, massive machine-type communication (mMTC), and internet of things (IoT) applications. The waveform is composed of asymmetric raised cosine (RC) pulses to increase the signal containment in time and frequency domains. The waveform has low power tails in the time domain, hence better performance in the presence of delay spread and time offsets. The time-axis warping unitary transform is applied to control the waveform occupancy in time-frequency space and to compensate for the usage of high roll-off factor pulses at the symbol edges. The paper explains a step-by-step analysis for determining the roll-off factors profile and the warping functions. Gains are presented over the conventional Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM), and Cyclic prefix (CP) DFT-s-OFDM schemes in the simulations section.

Multicarrier Rate-Splitting Multiple Access: Superiority of OFDM-RSMA over OFDMA and OFDM-NOMA

Rate-splitting multiple access (RSMA) is a multiple access technique generalizing conventional techniques, such as, space-division multiple access (SDMA), non-orthogonal multiple access (NOMA), and physical layer multi-casting, which aims to address multi-user interference (MUI) in multiple-input multiple-output (MIMO) systems. In this study, we leverage the interference management capabilities of RSMA to tackle the issue of inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) waveform. We formulate a problem to find the optimal subcarrier and power allocation for downlink transmission in a two-user system using RSMA and OFDM and propose a weighted minimum mean-square error (WMMSE)-based algorithm to obtain a solution. The sum-rate performance of the proposed OFDM-RSMA scheme is compared with that of conventional orthogonal frequency division multiple access (OFDMA) and OFDM-NOMA by numerical results. It is shown that the proposed OFDM-RSMA outperforms OFDM-NOMA and OFDMA under ICI in diverse propagation channel conditions owing to its flexible structure and robust interference management capabilities.

Centralized and Decentralized ML-Enabled Integrated Terrestrial and Non-Terrestrial Networks

Non-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short. However, the integration of these networks with the terrestrial network is laden with obstacles. The dynamic nature of NTN communication scenarios and numerous variables render conventional model-based solutions computationally costly and impracticable for resource allocation, parameter optimization, and other problems. Machine learning (ML)-based solutions, thus, can perform a pivotal role due to their inherent ability to uncover the hidden patterns in time-varying, multi-dimensional data with superior performance and less complexity. Centralized ML (CML) and decentralized ML (DML), named so based on the distribution of the data and computational load, are two classes of ML that are being studied as solutions for the various complications of terrestrial and non-terrestrial networks (TNTN) integration. Both have their benefits and drawbacks under different circumstances, and it is integral to choose the appropriate ML approach for each TNTN integration issue. To this end, this paper goes over the TNTN integration architectures as given in the 3rd generation partnership project standard releases, proposing possible scenarios. Then, the capabilities and challenges of CML and DML are explored from the vantage point of these scenarios.

Identification of Distorted RF Components via Deep Multi-Task Learning

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.