Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed Farouk .

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Boris Come, Nadia Khaled IMEC vzw., DESICS, WISE AEISTRACI Channel reciprocity is needed in SDMA or MIMO downlink pre-filtering when the channel knowledge is acquired in the n link We first show analytically that the non-reci roeity ofthe . &e station anal hardware which is part of the channel, introduces a v e q x g h level ok multiuser interference and quantify the effect of the nonreciproeit by means of simulations We then p r o p a novel caliiratinn technique at the base station that enables to compensate for the non-reciprocity and reduce the M U to a negligible value while having a low implementation c a t I. INTRODUIXIONOFDM-SDMA is an attractive technique to enhance the capacity of future wireless LANs since it allows mitigating the frequency selective channel fading (OFWM) and increasing the spectral efficiency by accommodating several users in the same time-frequency slots (SDMA). In the downlink, pre-filtering at the base station (BS) side allows to pre-compensate the phase (and amplitude) of the channel in such a manner that all simultaneous users receive their own signal free of MLn 01, [4l.It is in fact this interference Cancellation property that makes SDMA possible in the downlink. Since the terminals have only one antenna, they have no means IO mitigate the spatial MUI.When the channel is estimated in the uplink, the downlink channel matrix is just the transpose of the uplink matrix, assuming the channel is reciprocal. However, the "channel" is actually made up of the propagation channel (the medium between the antennas), the antennas and the transceiver RF, IF and baseband circuits at both sides of the link. The transceiver circuits are usually not reciprml (the TX and RX frequency responses are different) and this can jeopardise the performance of the SDMA system.In this paper, we depart from the reciprocity assumption, analyse the impact of channel non-reciprocity and propose a mitigation method. The document is organised as fdlows. Section 2 introduces the OFDM-SDMA Uplink and Downlink model, including the impact of the composite channel. In section 3, the effects of non-reciprocities are estimated by simulations. Seaion 4 proposes a simple yet effective calibration method with minimal additional hardware requirement. Then, the conclusions are drawn. SYSTEM MODELWe consider an OFDWSDMA system with a multi-antenna base station and single antenna terminals. Kapeldreef 75, B-3M)l Leuven A. UPLINKIn the uplink, U mobile user terminals (MT) transmit simultaneously to a BS using A antennas. Each user U employs conventional OFWM modulation Lz1. The following linear frequency domain model results on each sub-camer n: where x"[n] is the column vector of the U frequency domain symbols at subcarrier n transmitted by the MTs, y"fn] is the column vector of the A signals received by the BS antenna branches, and H" is the mmposite uplink channel: In the sequel, we drop the explicit dependency on fn] for clarity.Including the hardware, H"[nl can be expressed as:where bs and JhLm are complex diagonal matrices...

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