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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. 

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.