In the physical layer (PHY) of modern cellular systems, information is transmitted as a sequence of resource blocks (RBs) across various domains with each resource block limited to a certain time and frequency duration. In the PHY of 4G/5G systems, data is transmitted in the unit of transport block (TB) across a fixed number of physical RBs based on resource allocation decisions. Using sharp band-limiting in the frequency domain can provide good separation between different resource allocations without wasting resources in guard bands. However, using sharp filters comes at the cost of elongating the overall system impulse response which can accentuate inter-symbol interference (ISI). In a multi-user setup, such as in Machine Type Communication (MTC), different users are allocated resources across time and frequency, and operate at different power levels. If strict band-limiting separation is used, high power user signals can leak in time into low power user allocations. The ISI extent, i.e., the number of neighboring symbols that contribute to the interference, depends both on the channel delay spread and the spectral concentration properties of the signaling waveforms. We hypothesize that using a precoder that effectively transforms an OFDM waveform basis into a basis comprised of discrete prolate spheroidal sequences (DPSS) can minimize the ISI extent when strictly confined frequency allocations are used. Analytical expressions for upper bounds on ISI are derived. In addition, simulation results support our hypothesis.

Millimeter-wave (mmWave) technology represents a promising avenue in integrated sensing and communication (ISAC), leveraging wide bandwidth to accommodate growing demands for high data-rate communication and high-resolution radar sensing. However, in non-line-of-sight (NLOS) scenarios, mmWave signals suffer from severe attenuation, and integrating precise radar sensing into bandwidth-limited communication systems remains an open problem. To address this, we propose an NLOS-ISAC system that jointly provides accurate target positioning and robust communication. Our approach synthesizes many narrowband signals into a virtual wideband radar via stepped-frequency techniques, eliminating the need for additional hardware. By fusing time-of-flight (ToF) measurements of multipath reflections with environmental maps, the system achieves submeter positioning accuracy for NLOS targets. Building on these position estimates, we then employ position-aware beamforming to significantly enhance the NLOS communication throughput. Extensive experimental results demonstrate that the proposed NLOS-ISAC system reliably achieves high-accuracy localization while improving data rates in challenging NLOS environments.

Deep learning is making a profound impact in the physical layer of wireless communications. Despite exhibiting outstanding empirical performance in tasks such as MIMO receive processing, the reasons behind the demonstrated superior performance improvement remain largely unclear. In this work, we advance the field of Explainable AI (xAI) in the physical layer of wireless communications utilizing signal processing principles. Specifically, we focus on the task of MIMO-OFDM receive processing (e.g., symbol detection) using reservoir computing (RC), a framework within recurrent neural networks (RNNs), which outperforms both conventional and other learning-based MIMO detectors. Our analysis provides a signal processing-based, first-principles understanding of the corresponding operation of the RC. Building on this fundamental understanding, we are able to systematically incorporate the domain knowledge of wireless systems (e.g., channel statistics) into the design of the underlying RNN by directly configuring the untrained RNN weights for MIMO-OFDM symbol detection. The introduced RNN weight configuration has been validated through extensive simulations demonstrating significant performance improvements. This establishes a foundation for explainable RC-based architectures in MIMO-OFDM receive processing and provides a roadmap for incorporating domain knowledge into the design of neural networks for NextG systems.

Physical layer key generation techniques aim to extract secret keys from the information contained in wireless channels. However, existing key generation schemes often rely on time-frequency domain waveforms for channel estimation, which not only makes secret extraction less reliable but may also compromise the confidentiality of the extracted secret information. This paper presents physical layer key generation methods relying on the Orthogonal Time Frequency and Space (OTFS) waveform. We present analysis showing that the delay-Doppler domain channel estimates obtained using OTFS are conducive to more secure and reliable secret extraction than time-frequency domain channel estimates obtained using the prevalent Orthogonal Frequency Division Multiplexing (OFDM). This analysis provides theoretical guarantees under certain simple assumptions. We then relax those assumptions in extensive time-division duplex (TDD) simulations and show that under realistic settings, OTFS offers the expected benefits to reliability and security. Our simulations show that the introduced OTFS schemes can reliably extract secret keys from channel estimates in scenarios where time-frequency domain methods deteriorate.

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only the limited over-the-air (OTA) pilot symbols are utilized for training. However, the previous RC-based approach does not design the RC architecture based on the properties of the OTFS system to fully unlock the potential of RC. This paper introduces a novel two-dimensional RC (2D-RC) approach for online symbol detection on a subframe basis in the OTFS system. The 2D-RC is designed to have a two-dimensional (2D) filtering structure to equalize the 2D circular channel effect in the delay-Doppler (DD) domain of the OTFS system. With the introduced architecture, the 2D-RC can operate in the DD domain with only a single neural network, unlike our previous work which requires multiple RCs to track channel variations in the time domain. Experimental results demonstrate the advantages of the 2D-RC approach over the previous RC-based approach and the compared model-based methods across different modulation orders.

As the development of 6G technologies progresses, there is a focused effort by international bodies and regulatory agencies to enhance worldwide connectivity, paying special attention to the needs of high-mobility users and networks, such as Mobile Ad-Hoc Networks (MANETs) and Vehicular Ad-Hoc Networks(VANETS) . These advanced systems face significant challenges, particularly the increased demand for rapid channel state information (CSI) feedback due to the fast-changing nature of channel conditions. In environments where traditional time-frequency domain approaches struggle, the adoption of delay-Doppler domain representations, like those used in OTFS (Orthogonal Time Frequency Space) modulation, shows promise for improved stability in mobile scenarios. This paper introduces a novel expression for predicting OTFS channel variations over time and proposes an efficient technique for dynamically updating MIMO (Multiple Input Multiple Output) precoders, enhancing the utility of outdated CSI while minimized signaling overhead and computational complexity. Subsequently, we conduct Monte Carlo simulations to evaluate the performance of OFDM and OTFS MIMO systems within high-mobility environments. These simulations aim to rigorously assess the robustness and efficiency of both modulation techniques under scenarios characterized by rapid user movement and fluctuating channel conditions.

The paper proposes a new architecture for Distributed MIMO (D-MIMO) in which the base station (BS) jointly transmits with wireless mobile nodes to serve users (UEs) within a cell for 6G communication systems. The novelty of the architecture lies in the wireless mobile nodes participating in joint D-MIMO transmission with the BS (referred to as D-MIMO nodes), which are themselves users on the network. The D-MIMO nodes establish wireless connections with the BS, are generally near the BS, and ideally benefit from higher SNR links and better connections with edge-located UEs. These D-MIMO nodes can be existing handset UEs, Unmanned Aerial Vehicles (UAVs), or Vehicular UEs. Since the D-MIMO nodes are users sharing the access channel, the proposed architecture operates in two phases. First, the BS communicates with the D-MIMO nodes to forward data for the joint transmission, and then the BS and D-MIMO nodes jointly serve the UEs through coherent D-MIMO operation. Capacity analysis of this architecture is studied based on realistic 3GPP channel models, and the paper demonstrates that despite the two-phase operation, the proposed architecture enhances the system’s capacity compared to the baseline where the BS communicates directly with the UEs.

It is well known that index (discrete-time)-limited sampled sequences leak outside the support set when a band-limiting operation is applied. Similarly, a fractional shift causes an index-limited sequence to be infinite in extent due to the inherent band-limiting. Index-limited versions of discrete prolate spheroidal sequences (DPSS) are known to experience minimum leakage after band-limiting. In this work, we consider the effect of a half-sample shift and provide upper bounds on the resulting leakage energy for arbitrary sequences. Furthermore, we find an orthonormal basis, derived from DPSS, whose members are ordered according to energy concentrationafter half sample shifts; the primary (first) member being the global optimum.

Orthogonal time frequency space (OTFS) is a promising modulation scheme for wireless communication in high-mobility scenarios. Recently, a reservoir computing (RC) based approach has been introduced for online subframe-based symbol detection in the OTFS system, where only a limited number of over-the-air (OTA) pilot symbols are utilized for training. However, this approach does not leverage the domain knowledge specific to the OTFS system to fully unlock the potential of RC. This paper introduces a novel two-dimensional RC (2D-RC) method that incorporates the domain knowledge of the OTFS system into the design for symbol detection in an online subframe-based manner. Specifically, as the channel interaction in the delay-Doppler (DD) domain is a two-dimensional (2D) circular operation, the 2D-RC is designed to have the 2D circular padding procedure and the 2D filtering structure to embed this knowledge. With the introduced architecture, 2D-RC can operate in the DD domain with only a single neural network, instead of necessitating multiple RCs to track channel variations in the time domain as in previous work. Numerical experiments demonstrate the advantages of the 2D-RC approach over the previous RC-based approach and compared model-based methods across different OTFS system variants and modulation orders.

In 5G operating scenarios, mobility-induced Doppler spread becomes prominent and when compounded with equally significant delay spread results in a doubly dispersive channel. In such channel conditions, orthogonal frequency division multiplexing-based signaling loses the property of being a universal diagonalizer for the equivalent channel matrix and thus also the opportunity for low complexity equalization. Diagonalizing a doubly dispersive channel being too restrictive, a non-Gabor basis made up of discrete prolate spheroidal sequences is investigated for its benefit of compacting the bandedness of the equivalent channel matrix. While equivalent channel matrices obtained from Gabor frames using pulse shapes of duration equal to the block period can lead to a banded structure, the out-of-band elements are only slowly decaying. However, for discrete prolate spheroidal sequences a comparatively high out-of-band decay rate is obtained, translating to a higher rate of reduction of error when the channel matrix is approximated by a strictly banded version. In spite of the higher modulation complexity in discrete prolate spheroidal signaling, due to not being amenable to Fast Fourier Transform processing like Gabor bases, the resulting higher out-of-band decay rate property enables banded approximations with lower band width and lower equalization complexity; the latter dominates complexity at typical symbol error rate (SER) levels, such as 10−3.