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.

Low-altitude uncrewed aerial vehicles (UAVs) can pose growing risks to airspace safety, security, and privacy. Cellular infrastructure can passively sense them without dedicated radar hardware by exploiting integrated sensing and communication (ISAC) technology. Most prior work exploits monostatic sensing or bistatic/multistatic configurations based on downlink measurements. To the best of our knowledge, this paper presents the first uplink framework, where multiple user equipments (UEs) transmit sounding reference signal (SRS) pilots and the base station (BS) receives the UAV-scattered echoes. Sensing from uplink SRS, however, introduces new challenges. Each UE has its own oscillator and timing loop, so the channel estimate at the BS carries residual timing, frequency, and amplitude impairments that corrupt the UAV delay and Doppler. Moreover, the UAV echo is weaker than both the line-of-sight (LOS) path and urban clutter, so detection from a single UE transmission is not reliable. We address these challenges by designing a LOS-referenced synchronization scheme and a joint detector. The synchronization reuses the existing timing advance (TA) command and an adjacent-occasion conjugate product to remove the residuals without additional signaling. Then the detector searches a shared 3D state space and accumulates evidence across UEs. It leverages a normalized contrast that exploits the bistatic geometry. We evaluate the framework in a cluttered urban scene at frequency range 1 (FR1) with four pedestrian UEs and a 100 MHz 5G New Radio (NR) waveform. The proposed pipeline achieves sub-nanosecond synchronization and a 4.84 m median 3D position error.

This paper investigates a full-duplex (FD) multiple-input multiple-output (MIMO) setting for integrated sensing and communication (ISAC), which enables simultaneous monostatic sensing and communication with a single base station (BS). We consider frequency bands that exhibit sparse propagation characteristics, such as mmWave bands. A key challenge for these systems is the design of hybrid precoders and combiners that facilitate ISAC functionalities while mitigating self-interference (SI) that is caused by concurrent transmission and reception. While prior work has focused on hybrid precoder and combiner design for FD ISAC with prior communication channel and target parameter knowledge, the problem of joint channel and target parameter estimation remains unexplored. We address this gap by designing SI-aware hybrid training precoders and combiners that form a beam codebook optimized for sparse channel estimation. Our design minimizes the mutual coherence, a key metric in compressed sensing, while effectively suppressing the SI to enable accurate joint estimation. We evaluate our approach in terms of estimation accuracy, as well as SI mitigation performance.

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.

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.

Networks exploiting distributed integrated sensing and communication (DISAC) nodes can provide enhanced localization and sensing performance, further emphasized when operating with large arrays and bandwidths available in the upper mid-band (also known as FR3). In this paper, we consider a DISAC system operating at FR3 where a single base station (BS) acts as the transmitter and several vehicular user equipments (UEs) act as the receivers. We tackle the design of the signal processing chain at the UE side to enable joint UE positioning and target localization. The system model exploits a multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) waveform, and incorporates practical effects such as inter-node timing offsets (TOs), extended targets, dense multipath, and realistic uniform planar arrays (UPAs) at both ends. The proposed design includes a multipath estimation stage at each UE, clutter removal, a novel clustering and association scheme, and a final joint estimator of UE positions and target locations. The estimator solves a weighted least squares (WLS) problem to jointly compute clock offsets and localize UEs and targets. Numerical results considering two UEs and two targets show that for 80% of the cases the target localization error is below 32cm, while the UE positioning error is below 44cm.

Interference from terrestrial networks can reduce the communication rate for low Earth orbit (LEO) satellites in the upper mid-band frequency range from 7-24 GHz. To coexist in frequency, MIMO precoding can be used to reduce the signal that impinges on the LEO satellite. We present a beamforming algorithm designed for the hybrid architecture that incorporates a satellite interference penalty while optimizing the analog and digital precoders. Our algorithm optimizes the precoding at the base station (BS) within the set of precoders that null the interference to the satellite. Simulations demonstrate that our algorithm reduces the interference at the satellites and lowers the probability of violating prescribed LEO satellite protection thresholds, outperforming prior hybrid nulling algorithms. Results indicate that the algorithm maintains sum-rate within 3% of the existing hybrid solutions, while effectively improving interference to noise power by 22.4 dB.

A full duplex (FD) multiple-input multiple-output (MIMO) communication system can also provide monostatic sensing capabilities by processing the echoes of the downlink (DL) signal or by dedicating specific spatial resources to sensing. This integrated sensing and communication (ISAC) setting requires the design of precoders and combiners at the FD base station (BS) to balance sensing and communication performance while suppressing the self-interference (SI) caused by simultaneous transmission and reception. Although this subject has been studied for conventional arrays that operate in the far-field (FF), nearfield (NF) FD-ISAC is still in its nascent stages. Given that the extremely large arrays to be used in high frequency bands and in the upper mid-band will lead to operation in the NF for many users, it is essential to design techniques for NF FD-ISAC. In this paper, we tackle the design of the precoders and combiners that enable NF FD-ISAC while exploiting dynamic metasurface antennas (DMAs) at the BS. We evaluate our design considering sensing and communication metrics, in addition to its SI mitigation capability.

In this paper, we propose a design for hybrid precoding and combining at mmWave that enables simultaneous monostatic sensing and full-duplex (FD) communication. This joint design mitigates the self-interference (SI) caused by FD operation at the base station (BS). Precoders at the FD BS are designed to maximize signal-to-leakage-plus-noise ratio (SLNR) for downlink (DL) communication and sensing, treating the SI as leakage. The analog combiner minimizes the residual SI at the FD BS under uplink (UL) communication and sensing gain constraints. Moreover, an interference-aware digital combiner separates the target reflections from the UL signals, followed by orthogonal frequency division multiplexing (OFDM) radar for target parameter estimation. Numerical results demonstrate the effectiveness of the proposed design to simultaneously support FD communication and sensing at mmWave.

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.

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.

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.

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.

User localization and tracking in the upcoming generation of wireless networks have the potential to be revolutionized by technologies such as Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches rely on assumptions about relatively dominant Line-of-Sight (LoS) paths or may require pilot transmission sequences whose length is comparable to the number of DMA elements, thus leading to limited effectiveness and considerable measurement overheads in blocked LoS and dynamic multipath environments. Therefore, this paper proposes a two-stage machine-learning-based approach for user tracking, specifically designed for non-LoS multipath settings. A newly proposed Attention-based neural network is first trained to map noisy channel responses to potential user positions regardless of user-mobility patterns. This architecture constitutes a modification of the prominent Vision Transformer, specifically modified for extracting information from high-dimensional frequency response signals. As a second stage, its predictions for the past user positions are passed through a learnable autoregressive model to exploit the time-correlated information and obtain the final position predictions; thus the problems of localization and tracking are decomposed. The channel estimation procedure leverages a DMA architecture with partially-connected Radio Frequency Chains (RFCs), which results to reduced numbers of pilot signals. The numerical evaluation over an outdoor ray-tracing scenario illustrates that despite LoS blockage, this methodology is capable of achieving high position accuracy across various multipath settings.

In this study, the effects of combining multiple models to increase the accuracy of Click-Through Rate (CTR) prediction, which is a critical task in online advertising, product marketing, and recommendation systems, have been examined. Traditional CTR prediction methods use a single model developed for this purpose and therefore cannot capture some complex relationships. In this study, the aim is to increase the accuracy of CTR prediction in terms of different metrics by combining multiple models using the ranx library. The experimental results show that the proposed method achieve better results than CTR prediction models based on a single model used in previous studies. These results indicate that the development of different and new combination methods could also be beneficial.

This paper proposes an analog beam codebook for full-duplex joint sensing and communication (JSAC) systems at millimeter wave (mmWave) bands. The codebook design is formulated as the minimization of the self-interference (SI) caused by full-duplex operation, while also considering constraints associated to the beam gains and the implementation based on phase shifters. It is shown that the proposed codebook is suitable for joint initial access and target detection in mmWave communication systems that leverage a full-duplex circuit. The simulation results reveal that the proposed design outperforms the benchmarks in terms of communication and sensing metrics, while operating with a practical analog beamformer.

Wireless networks are incorporating higher frequency bands and higher bandwidths by exploiting MIMO technology with large arrays. These large arrays and bandwidths enable high resolution estimates of the angles and delays associated to the different multipath components of the MIMO channel. Given the sparse nature of the millimeter wave (mmWave) channel, sparse recovery algorithms can extract the path parameters with reasonable accuracy. Moreover, channel sparsity also facilitates the association of these multipath components to the geometry of the environment, providing sufficient information to determine the user position. In this paper, we address the problem of designing the set of training precoders and combiners that, while providing a high accuracy channel and position estimate, also result in a reduced training overhead with respect to standardized beam training strategies. As performance metric, we consider the mutual coherence between the training hybrid precoders/combiners and the overcomplete dictionary used to represent the channel. The proposed scheme significantly reduces overhead and outperforms previous designs in terms of the accuracy of the channel estimate, which results in a higher localization accuracy and a higher spectral efficiency.

RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We consider sparse reconstruction algorithms to obtain high resolution channel estimates that are mapped to position information. In RIS-aided mmWave systems, the complexity of sparse recovery becomes a bottleneck, given the large number of elements of the RIS and the large communication arrays. We propose to exploit a multidimensional orthogonal matching pursuit strategy for compressive channel estimation in a RIS-aided millimeter wave system. We show how this algorithm, based on computing the projections on a set of independent dictionaries instead of a single large dictionary, enables high accuracy channel estimation at reduced complexity. We also combine this strategy with a localization approach which does not rely on the absolute time of arrival of the LoS path. Localization results in a realistic 3D indoor scenario show that RIS-aided wireless system can also benefit from a significant improvement in localization accuracy.

In this letter, we provide a practical framework to resolve whether code-domain NOMA (CD-NOMA) is beneficial when integrated with massive MIMO systems. In order to realize this integration, first, we develop a novel code-beamspace wideband signal model for uplink CD-NOMA in mmWave hybrid massive MIMO systems employing single-carrier (SC) transmission. Then, we apply a state-of-the-art SC frequency domain equalization (SC-FDE) based iterative receiver where the number of radio frequency (RF) chains is limited. Simulation results verify the effectiveness of the proposed architecture in overloaded scenarios. Furthermore, we show that CD-NOMA can enhance the performance of mmWave hybrid beamforming based massive MIMO systems by effectively decreasing the correlation between closely separated user channels in joint code-beamspace.

Low-complexity beamformer design with practical constraints is an attractive research area for hybrid analog/digital systems in mm-wave massive multiple-input multiple-output (MIMO). This paper investigates interference-aware pre-beamformer (analog beamformer) design for joint spatial division and multiplexing (JSDM) which is a user-grouping based two-stage beamforming method. Single-carrier frequency domain equalization (SC-FDE) is employed in uplink frequency-selective channels. First, unconstrained slowly changing statistical analog beamformer of each group, namely, generalized eigenbeamformer (GEB) which has strong interference suppression capability is designed by maximizing the mutual information in reduced dimension. Then, constant-modulus constrained approximations of unconstrained beamformer are obtained by utilizing alternating minimization algorithms for fully connected arrays and fixed subarrays. In addition, a dynamic subarray algorithm is proposed where the connections between radio frequency (RF) chains and antennas are changed with changing channel statistics. Convergence of the proposed alternating minimization-based algorithms is provided along with their complexity analysis. It is observed that the additional complexity of proposed algorithms is insignificant for the overall system design. Although most of the interference is suppressed with the help of proposed constrained beamformers, there may be some residual interference after analog beamforming stage. Thus, minimum mean square error (MMSE) criterion based iterative block decision feedback equalization (IB-DFE) method, which takes the residual interference in reduced dimension into account, is promoted for digital beamforming stage. Simulation results verify the superiority of the proposed interference-aware constrained design over existing approaches in terms of beampattern, spectral efficiency, outage capacity, bit-error rate (BER), and channel estimation accuracy.