Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths available in the higher bands have inspired much work regarding sensing in the mmWave and sub-THz bands; however, the sub-6 GHz cellular bands will still be the main provider of wide cellular coverage due to the more favorable propagation conditions. In this paper, we present a measurement system and results of sub-6 GHz distributed MIMO measurements performed in an industrial environment. From the measurements, we evaluated the diversity for both large-scale and small-scale fading and characterized the link reliability. We also analyzed the possibility of multistatic sensing and positioning of users in the environment, with the initial results showing a mean-square error below 20 cm on the estimated position. Further, the results clearly showed that new channel models are needed that are spatially consistent and deal with the nonstationary channel properties among the antennas.
Orthogonal time frequency space (OTFS) modulation has the potential to enable robust communications in highly-mobile scenarios. Estimating the channels for OTFS systems, however, is associated with high pilot signaling overhead that scales with the maximum delay and Doppler spreads. This becomes particularly challenging for massive MIMO systems where the overhead also scales with the number of antennas. An important observation however is that the delay, Doppler, and angle of departure/arrival information are directly related to the distance, velocity, and direction information of the mobile u…
One of the trends that is gaining more and more importance in the field of beyond-5G and 6G wireless communication systems is the investigation on systems that jointly perform communication and sensing of the environment. This paper proposes to use a base station (BS), that we call \textit{radar-BS}, equipped with a large-scale antenna array to execute, using the same frequency range, communication with mobile users and sensing/surveillance of the surrounding environment through radar scanning. The massive antenna array can indeed both operate as a MIMO radar with co-located antennas -- transm…
Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex correlation characteristics across time, frequency, and space from channel tensors formed from the measurements, followed by a neural network that learns the relationship between the input features and o…
The sixth-generation (6G) network is envisioned to integrate communication and sensing functions, so as to improve the spectrum efficiency (SE) and support explosive novel applications. Although the similarities of wireless communication and radio sensing lay the foundation for their combination, there is still considerable incompatible interest between them. To simultaneously guarantee the communication capacity and the sensing accuracy, the multiple-input and multiple-output (MIMO) technique plays an important role due to its unique capability of spatial beamforming and waveform shaping. How…
Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communication systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth. However, the growing size of transmission bandwidth and antenna array results in the beam squint effect, which hampers the communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitive for practical systems. In this paper, instead of alleviating the wideband beam squint effect, we …
In colocated compressive sensing MIMO radar, the measurement matrix is specified by antenna placement. To guarantee an acceptable recovery performance, this measurement matrix should satisfy certain properties, e.g., a small coherence. Prior work in the literature often employs randomized placement algorithms which optimize the prior distribution of antenna locations. The performance of these algorithms is suboptimal, as they can be easily enhanced via expurgation. In this paper, we suggest an iterative antenna placement algorithm which determines the antenna locations deterministically. The p…
This paper studies a joint communication and sensing (JCAS) system with downlink communication and multi-static sensing for single-target detection in a cloud radio access network architecture. A centralized operation of cell-free massive MIMO is considered for communication and sensing purposes. The JCAS transmit access points (APs) jointly serve the user equipments (UEs) and optionally steer a beam towards the target. A maximum a posteriori ratio test detector is derived to detect the target using signals received at distributed APs. We propose a power allocation algorithm to maximize the se…
In this work, the uplink channel estimation problem is considered for a millimeter wave (mmWave) multi-input multi-output (MIMO) system. It is well known that pilot overhead and computation complexity in estimating the channel increases with the number of antennas and the bandwidth. To overcome this, the proposed approach allows the channel estimation at the base station to be aided by the sensing information. The sensing information contains an estimate of scatterers locations in an environment. A simultaneous weighting orthogonal matching pursuit (SWOMP) - sparse Bayesian learning (SBL) algo…
Mobile communication networks were designed to mainly support ubiquitous wireless communications, yet they are also expected to achieve radio sensing capabilities in the near future. However, most prior studies on radio sensing usually rely on far-field assumption with uniform plane wave (UPW) models. With the ever-increasing antenna size, together with the growing demands to sense nearby targets, the conventional far-field UPW assumption may become invalid. Therefore, this paper studies near-field radio sensing with extremely large-scale (XL) antenna arrays, where the more general uniform sph…
In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method…
The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for…
Wireless channel sensing is one of the key enablers for integrated sensing and communication (ISAC) which helps communication networks understand the surrounding environment. In this work, we consider MIMO-OFDM systems and aim to design optimal and robust waveforms for accurate channel parameter estimation given allocated OFDM resources. The Fisher information matrix (FIM) is derived first, and the waveform design problem is formulated by maximizing the log determinant of the FIM. We then consider the uncertainty in the parameters and state the stochastic optimization problem for a robust desi…
The space-air-ground-sea integrated network (SAGSIN) plays an important role in offering global coverage. To improve the efficient utilization of spectral and hardware resources in the SAGSIN, integrated sensing and communications (ISAC) has drawn extensive attention. Most existing ISAC works focus on terrestrial networks and can not be straightforwardly applied in satellite systems due to the significantly different electromagnetic wave propagation properties. In this work, we investigate the application of ISAC in massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite …
Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation in RIS-aided system is still a critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensat…
A novel compressive-sensing based signal multiplexing scheme is proposed in this paper to further improve the multiplexing gain for multiple input multiple output (MIMO) system. At the transmitter side, a Gaussian random measurement matrix in compressive sensing is employed before the traditional spatial multiplexing in order to carry more data streams on the available spatial multiplexing streams of the underlying MIMO system. At the receiver side, it is proposed to reformulate the detection of the multiplexing signal into two steps. In the first step, the traditional MIMO equalization can be…
To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data communication, the integrated sensing and communication (ISAC) technique has broken the barriers between data collection and delivery in the physical layer. By exploiting the analog-wave addition in a multi-access channel, over-the-air computation (AirComp) enables function computation via transmissions in the physical layer. The promising perfo…
Spatial channel covariance information can replace full knowledge of the entire channel matrix for designing analog precoders in hybrid multiple-input-multiple-output (MIMO) architecture. Spatial channel covariance estimation, however, is challenging for the hybrid MIMO architecture because the estimator operating at baseband can only obtain a lower dimensional pre-combined signal through fewer radio frequency (RF) chains than antennas. In this paper, we propose two approaches for covariance estimation based on compressive sensing techniques. One is to apply a time-varying sensing matrix, and …
Massive multiple-input multiple-output (MIMO) is becoming a key technology for future 5G wireless communications. Channel feedback for massive MIMO is challenging due to the substantially increased dimension of MIMO channel matrix. In this letter, we propose a compressive sensing (CS) based differential channel feedback scheme to reduce the feedback overhead. Specifically, the temporal correlation of time-varying channels is exploited to generate the differential channel impulse response (CIR) between two CIRs in neighboring time slots, which enjoys a much stronger sparsity than the original s…
Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) a…
A new approach is proposed, namely CSSF MIMO radar, which applies the technique of step frequency (SF) to compressive sensing (CS) based multi-input multi-output (MIMO) radar. The proposed approach enables high resolution range, angle and Doppler estimation, while transmitting narrowband pulses. The problem of joint angle-Doppler-range estimation is first formulated to fit the CS framework, i.e., as an L1 optimization problem. Direct solution of this problem entails high complexity as it employs a basis matrix whose construction requires discretization of the angle-Doppler-range space. Since h…
This article analyzes the performance of sensing and communications (S\&C) achieved by a multiple-input multiple-output downlink integrated S\&C (ISAC) system. Three ISAC scenarios are analyzed, including the sensing-centric design, communications-centric design, and Pareto optimal design. For each scenario, diversity orders and high signal-to-noise ratio slopes of the sensing rate (SR) and communication rate (CR) are derived to gain further insights. Numerical results reveal that \romannumeral1) ISAC achieves the same diversity order as existing frequency-division S\&C (FDSAC) techniques; \ro…
Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, we propose a spectrum-efficient superimposed pilot design fo…
This paper deals with spectrum sensing for cognitive radio scenarios where the decision fusion center (DFC) exploits array processing. More specifically, we explore the impact of user cooperation and orthogonal transmissions among secondary users (SUs) on the reporting channel. To this aim four protocols are considered: (i) non-orthogonal and non-cooperative; (ii) orthogonal and non-cooperative; (iii) non-orthogonal and cooperative; (iv) orthogonal and cooperative. The DFC employs maximum ratio combining (MRC) rule and performance are evaluated in terms of complementary receiver operating char…
The paper considers a scenario where a base station (BS), equipped with a large-scale antenna array, execute, using the same frequency range, both communication with mobile users and radar surveillance of the surrounding environment, relying on the ability of the massive MIMO array to synthesize multiple narrow beams. Based on an OFDM signaling format for both communication and surveillance tasks, a lower bound to the system achievable downlink rate is provided, along with a GLRT detection rule that does not require any knowledge about the target parameters. Then, a power allocation strategy i…
Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is ta…
In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as measurement matrix. The samples are subsequently forwarded to a fusion center, where an L1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits the target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with many fewer measurements. The measurement matrix is vital for CS recovery performance. This …
