The use of extremely large aperture arrays (ELAAs) in upper-mid frequencies (FR3) extends the Fraunhofer distance to several hundreds of meters. This necessitates wireless channels to be modeled via spherical wavefronts, and performance metrics to be derived in the near-field. This paper hence focuses on evaluating the near-field performance of cell-free massive multiple-input multiple-output (CF-mMIMO)-aided integrated sensing and communication (ISAC) systems. The achievable user rates, Cramér Rao bound, and signal-to-clutter-plus-noise ratio (SCNR) are derived by modeling near-field spatial correlation, imperfect channel estimation, and multiple clutters sources. A transmit power allocation algorithm is also proposed to maximize the weakest user’s rate while satisfying a SCNR threshold. Our numerical results confirm the necessity for near-field analysis of CF-mMIMO-aided ISAC with ELAAs in FR3 band.

The recently acquired mid-band frequency range (FR3) for 6G necessitates adopting extremely large aperture arrays (ELAAs) to leverage higher array gains and spatial multiplexing gains to compensate for larger path-losses compared to sub-6 GHz band and reduction of bandwidth availability compared to millimeter-waves, respectively. However, the nearfield of ELAAs may extend hundreds of meters depending on the aperture size and operating frequency. Hence, the planarwave based far-field channel models must be replaced by spherical-wave based near-field counterparts. To this end, we analyze the near-field performance of ELAA-based integrated sensing and communications (ISAC). This analysis captures the near-field spatial correlation, partial visibility due to spatially non-wide sense stationarities, erroneous channel estimates, an extended target, and clutter sources. A computationally-efficient conjugate precoding-based superimposed ISAC waveform is used at ELAAs. This waveform is further optimized via transmit power allocation to maximize the minimum achievable rate of the weakest communication user, while satisfying a sensing threshold for target detection. The achievable user rates and a target detector are derived. Our results demonstrate the potential of ELAA-based ISAC to improve the trade-off between the communication and sensing performance metrics.

The transition to millimeter-wave and sub-THz frequency bands necessitates that the base-stations (BSs) utilize extra-large antenna arrays (ELAA) to compensate for the associated huge path-losses. However, when higher frequencies and shorter transmission distances are utilized, the spherical wave curvature can no longer be neglected. Hence, the ELAAbased wireless systems tend to operate primarily in the near-field. Thus, the far-field channel models used for near-field users may detrimentally affect wireless system designs and performance gains. To this end, we investigate the impact of mismatches between far-field and near-field channel models/precoders on the performance of ELAA-based integrated sensing and communication (ISAC). To this end, the achievable user rates are derived for the near-field. Two detectors for sensing a target are designed based on known/unknown BS/target channels. The performance of these detectors are investigated by deriving the probability of detection and probability of false-alarm. A transmit power optimization procedure is also proposed to maximize the minimum achievable user rate, while ensuring a power threshold for sensing. Numerical results are used to study the fundamental trade-off between the probability of detection and achievable rates for near-field ELAA-based ISAC. We unveil that ELAAs can be leveraged to improve the ISAC performance trade-offs.

The performance of cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) is investigated. Each transmit access point (AP) sends a superimposed ISAC waveform from which the users are able to decode data, while the reflected echos off a target are used at the receive APs to perform sensing functionalities. Each transmit AP adopts a local conjugate precoder, which is designed based on the locally acquired channel state information (CSI) via user pilots. This approach reduces the implementation complexity as it does not necessitate CSI exchanges. An efficient transmit power optimization is also proposed to construct the superimposed ISAC waveform. The performance is evaluated by deriving the achievable user rates and quantifying the two-dimensional MUltiple SIgnal Classification (MUSIC) spectrum function at the receive APs. Our performance analysis captures practical impairments, including erroneously estimated CSI, spatially correlated Rician fading, and clutter interference. Our analytical and numerical results demonstrate the potential of our proposed cell-free massive MIMO aided ISAC systems.

The performance of integrated sensing and communications (ISAC) empowered intelligent reflecting surface (IRS)aided massive multiple-input multiple-output (MIMO) systems operating over spatially correlated Rician fading is investigated. Computationally-efficient linear precoders are used to construct the ISAC signal by invoking the maximal ratio transmission (MRT) criterion into the composite channels containing both direct and IRS reflected channels. The uplink communication channels are estimated based on the linear minimum mean square error criterion and used to construct user precoders. The IRS phase-shifts are optimized based on the statistical channel knowledge to maximize the minimum average power gains of the composite communication channels subject to an average power threshold for the reflected sensing channel. The communication performance is evaluated by deriving the achievable user rates, while the sensing performance is studies by locating the target via the 2D MUltiple SIgnal Classification (MUSIC) algorithm. Our numerical results are used to study the trade-off between the communication and sensing performance metrics in IRS-aided massive MIMO systems with MRT-based linear precoders.

The phase-shift control of intelligent reflective surface (IRS)-aided massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) systems requires pilot-intensive instantaneous estimation of cascaded channels. As a remedy, in this paper, statistical composite channel state information (CSI)-based techniques for SWIPT in IRS-aided massive MIMO are explored. Two phase-shift/power optimization strategies are proposed to guarantee system-wide user-fairness in terms of the average harvested energy and achievable rates, while keeping the pilot overhead and computational complexity significantly lower than the current state-of-the-art counterparts. By considering a hybrid time-switching/power-splitting protocol with a non-linear energy harvesting model, the average harvested energy and achievable rates are quantified. This analysis considers spatially correlated fading, imperfectly estimated composite channels, and optimization of IRS phase-shifts and transmit power control based on statistical composite CSI. The design insights and performance gains of the proposed techniques are presented through numerical results. Monte-Carlo simulations are used to validate our analysis.

The active pilot contamination attacks in intelligent reflecting surface (IRS) aided massive multiple-input multiple-output systems are investigated. By proposing a statistical channel state information based IRS phase-shift optimization technique, an achievable secrecy rate is derived in the presence of practical impediments, including erroneously estimated composite channels via linear minimum mean square error estimation criterion, residual interference due to active pilot contamination, artificial noise (AN) generation, and spatially correlated fading at the base-station antennas and IRS elements. A transmit power allocation technique is also proposed. Two active pilot attack detectors are designed based on the Neyman-Pearson and generalized likelihood ratio test criteria. The performance of these detectors is investigated by deriving the probability of detection, probability of false alarm, and receiver operating characteristics. Our secrecy rate analysis reveals that the rate leaked into the eavesdroppers by active pilot contamination attacks can be considerably high. The proposed power allocation algorithm jointly assigns transmit powers for the legitimate signals and AN sequences for maximizing the minimum secrecy rate of the weakest legitimate user to ensure user-fairness. The proposed detectors of active pilot attacks may be useful in designing remedial techniques to mitigate detrimental effects of active eavesdropping.