In this paper, we propose a hybrid precoding/combining framework for communication-centric integrated sensing and full-duplex (FD) communication operating at mmWave bands. The designed precoders and combiners enable multiuser (MU) FD communication while simultaneously supporting monostatic sensing in a frequency-selective setting. The joint design of precoders and combiners involves the mitigation of self-interference (SI) caused by simultaneous transmission and reception at the FD base station (BS). Additionally, MU interference needs to be handled by the precoder/combiner design. The resulting optimization problem involves non-convex constraints since hybrid analog/digital architectures utilize networks of phase shifters. To solve the proposed problem, we separate the optimization of each precoder/combiner, and design each one of them while fixing the others. The precoders at the FD BS are designed by reformulating the communication and sensing constraints as signal-to-leakage-plus-noise ratio (SLNR) maximization problems that consider SI and MU interference as leakage. Furthermore, we design the frequency-flat analog combiner such that the residual SI at the FD BS is minimized under communication and sensing gain constraints. Finally, we design an interference-aware digital combining stage that separates MU signals and target reflections. The communication performance and sensing results show that the proposed framework efficiently supports both functionalities simultaneously.

This paper introduces a novel hybrid analog/digital transceiver design for full-duplex (FD) integrated sensing and communication (ISAC) systems operating at mmWave band. The proposed scheme simultaneously supports downlink (DL) and uplink (UL) multiuser communication along with monostatic sensing, while suppressing the self-interference (SI). Considering that high SI levels may lead to saturation at low-noise amplifiers (LNAs), our design incorporates two SI mitigation constraints: one imposed at the receiver (RX) antennas before LNAs and another after the analog combining stage before analog-to-digital converters (ADCs). By formulating optimization problems that balance the trade-offs between spectral efficiency and beam-pattern error, we leverage a projected gradient ascent (PGA) algorithm with penalty-based methods to iteratively design hybrid precoders and combiners. Simulation results show that the proposed architecture strikes a balance between communication and sensing performance while effectively mitigating SI.

Accurate downlink (DL) channel state information (CSI) is crucial for effective precoding in massive multiple-input multiple-output (MIMO) systems. Operating in time-division duplexing (TDD) mode enables both uplink (UL) and DL CSI to be utilized for DL precoding, while each alternative presents its own unique drawbacks. UL CSI experiences degradation for cell edge users due to their low signal-to-noise ratio (SNR), while DL CSI feedback suffers from low frequency resolution. Moreover, channel aging in high mobility scenarios necessitates frequent CSI updates. Although channel or precoder prediction is a promising solution, the inherent shortcomings of UL CSI and DL CSI feedback make accurate prediction challenging. In this paper, we propose artificial intelligence (AI)-based precoder prediction solutions that jointly exploit UL CSI and codebook-based precoder matrix indicator (PMI) feedback to address the aforementioned challenges. In our first solution, a PMI-based precoder sequence for a subband (SB) that covers many subcarriers is used as input to an AI model that generates a context vector. This context vector is then combined with multiple precoders obtained from noisy UL CSI, with each precoder corresponding to a different subset of subcarriers within the SB at the last time step, thereby yielding a precoder prediction with high frequency resolution. Although we study a recurrent neural network (RNN)-based approach, our framework can also be implemented with other architectures such as Transformers. Additionally, we introduce a UL CSI denoising stage to enhance performance and propose a multi-resolution prediction network that leverages both PMI and UL CSI-based precoder sequences with arbitrary time-frequency resolution.

In this paper, we propose a novel indoor localization algorithm that exploits the angle and delay information of the sparse channel paths at mmWave. We consider that the user and the access point (AP) are not perfectly synchronized, which results in an unknown clock offset for the estimated delays. The proposed algorithm comprises two stages where the initial stage is to estimate the unknown clock offset and the locations of the users by leveraging the properties of the indoor environment. Then, the initial location estimates of users are collected and used to learn the virtual anchor locations. Finally, we propose a one-shot anchor-based localization algorithm that outperforms the initial one.

In the context of integrated sensing and communication (ISAC), a full-duplex (FD) transceiver can operate as a monostatic radar while maintaining communication capabilities. This paper investigates the design of precoders and combiners for a joint radar and communication (JRC) system at mmWave frequencies. The primary goal of the design is to guarantee certain performance in terms of some sensing and communication metrics while minimizing the self-interference (SI) caused by FD operation and taking into account the hardware limitations coming from a hybrid MIMO architecture. Specifically, we introduce a generalized eigenvalue-based precoder design that considers the downlink user rate, the radar gain, and the SI suppression. Since the hybrid analog/digital architecture degrades the SI mitigation capability of the precoder, we further enhance SI suppression with the analog combiner. Our numerical results demonstrate that the proposed architecture achieves the required radar gain and SI mitigation while incurring a small loss in downlink spectral efficiency. Additionally, the numerical experiments also show that the use of orthogonal frequency division multiplexing (OFDM) radar with the proposed beamforming architecture results in highly accurate range and velocity estimates for the detected targets.

Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.

Reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) wireless systems offer robustness to blockage and enhanced coverage. In this paper, we develop an algorithmic solution that shows how RISs can also enhance the positioning performance in a joint localization and communication setting, even when hardware impairments are considered. We propose a realistic system architecture that considers the clock offset between the transmitter and the receiver, impairments at transmit and receive arrays, and mutual coupling between the RIS elements. We formulate the estimation of the composite channel in a RIS-aided mmWave system as a multidimensional orthogonal matching pursuit problem, which can be solved with high accuracy and low complexity, even when operating with large antenna arrays as required at mmWave. In addition, we introduce a dictionary learning stage to calibrate the hardware impairments at the user array. To complete our design, we devise a localization scheme that exploits the estimated composite channel while accounting for the clock offset between the transmitter and the receiver. Numerical results show how RIS-aided mmWave systems can significantly improve the localization accuracy in a realistic 3D indoor scenario simulated by ray tracing.

Massive MIMO and non-orthogonal multiple access (NOMA) are key technologies for next generation wireless systems due to their distinct advantages. We previously proposed a general framework on unification of code-domain NOMA and joint spatial division and multiplexing (JSDM) which is a spatial user-grouping based hybrid beamforming method for massive MIMO. In this paper, we propose a constrained analog beam-former design for JSDM based systems. Moreover, we propose joint group processing for the digital beamformer in order to reduce the interference introduced by constrained beamformers. We provide a comprehensive performance evaluation for different operational modes of JSDM with code-domain NOMA when constrained analog beamformers are utilized.