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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 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 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 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. 

Spatial non wide-sense stationarities cause partial visibility regions (VRs), and it is a unique propagation characteristic of emerging extra-large aperture arrays (ELAAs). Thus, classification of VRs is a necessity for accurate estimation of channels and efficient design of VR-aware precoders for ELAAs. In this paper, a deep learning framework is proposed to classify VRs in ELAAs. Our objective is to boost the accuracy of classifying VRs based on the uplink pilots received at the ELAAs. Consequently, we focus on guaranteeing user-fairness in the presence of wholly/partial VRs and improving the achievable rates by adopting VR-aware channel estimation and precoding. We propose a hybrid deep learning architecture comprising one dimensional convolutional neural networks and long-short term memory to classify VRs of each user at the ELAA. To achieve a higher accuracy, we generate a diverse dataset through Monte-Carlo simulations that captures numerous combinations of VRs at the ELAA. A transmit power allocation algorithm is also proposed to achieve a common downlink rate for all users irrespective of the different VRs, and its computational complexity is discussed. A set of numerical results is presented to evaluate the performance of our proposed framework. It is efficient and accurate in classifying VRs. Thus, it can be used to enhance the estimation accuracy of ELAA channels with VRs and thereby to design VR-aware precoders to boost spectral/energy efficiency of the next-generation wireless systems.