Backscatter communication (BC) is a promising technique for future Internet-of-Things (IoT) owing to its low complexity, low cost, and potential for energy-efficient operation in sensor networks. There are several network infrastructure setups that can be used for BC with IoT nodes. One of them is the bistatic setup where typically there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader. In this paper, we investigate a bistatic BC setup with multiple antennas. We propose a novel transmission scheme, which includes a protocol for channel estimation at the carrier emitter (CE) as well as a transmit beamformer construction that suppresses the direct link interference between the two ends of a bistatic link (namely CE and reader), and increases the detection performance of the backscatter device (BD) symbol. Further, we derive a generalized log-likelihood ratio test (GLRT) to detect the symbol/presence of the BD. We also provide an iterative algorithm to estimate the unknown parameters in the GLRT. Finally, simulation results show that the required dynamic range of the system is significantly decreased, and the detection performance of the BD symbol is increased, by the proposed algorithm compared to a system not using beamforming at the CE.

Reconfigurable surface (RS) has been shown to be an effective solution for improving wireless communication links in general multi-user multiple-input multiple-output (MU-MIMO) setting. Current research efforts have been largely directed towards the study of reconfigurable intelligent surface (RIS), which corresponds to an RS made of passive reconfigurable elements with only phase shifting capabilities. RIS constitutes a cost- and energy- efficient solution for increased beamforming gain since it allows to generate constructive interference towards desired directions, e.g., towards a base station (BS). However, in many situations, multiplexing gain may have greater impact on the achievable transmission rates and number of simultaneously connected devices, while RIS has only been able to achieve minor improvements in this aspect. Recent work has proposed the use of alternative RS technologies, namely amplitude-reconfigurable intelligent surface (ARIS) and fully-reconfigurable intelligent surface (FRIS), to achieve perfect orthogonalization of MU-MIMO channels, thus allowing for maximum multiplexing gain at reduced complexity. In this work we consider the use of ARIS and FRIS for simultaneously orthogonalizing a MU-MIMO channel, while embedding extra information in the orthogonalized channel. We show that the resulting achievable rates allow for full exploitation of the degrees of freedom in a MU-MIMO system with excess of BS antennas.

In this paper, a novel radio unit (RU) uplink (UL)/downlink (DL) configuration algorithm for dynamic time division duplex (DTDD) distributed multiple-input multiple-output (D-MIMO) systems with half-duplex RUs is proposed, where the configuration is performed only by the long-term channel statistics without the knowledge of instantaneous channel state information (CSI). Although there are several RU configuration approaches based on instantaneous CSI in the literature, a remaining challenge is to reduce the frequency of RUs UL/DL reconfiguration so as to lower the computational cost in the network and reduce the control signaling overhead in the fronthaul links. The proposed method is shown to be effective compared to the state-of-the-art methods in the trade-off between performance, complexity, and fronthaul overhead.

A novel amplification and phase optimization method for distributed reconfigurable repeater-assisted multiple-input multiple-output (MIMO) systems is proposed in this paper. Although distributed multiple-input multiple-output (D-MIMO) architectures such as cell-free multiple-input multiple-output (CF-MIMO) has shown in the literature to be a promising alternative to the current central multiple-input multiple-output (C-MIMO) deployed in real networks, there remain several challenges to be concured when it comes to real-world deployment. This paper intends to elucidate potential performance of repeater-assisted MIMO systems so as to highlight its capability to be a great successor to C-MIMO in a scenario where/when D-MIMO deployment is arduous. Simulation results are offered to illustrate the effectiveness of the proposed approach compared to other possible MIMO architectures.

We consider a robust beamforming problem where large amount of downlink (DL) channel state information (CSI) data available at a multiple antenna access point (AP) is used to improve the link quality to a user equipment (UE) for beyond-5G and 6G applications such as environment-specific initial access (IA) or wireless power transfer (WPT). As the DL CSI available at the current instant may be imperfect or outdated, we propose a novel scheme which utilizes the (unknown) correlation between the antenna domain and physical domain to localize the possible future UE positions from the historical CSI database. Then, we develop a codebook design procedure to maximize the minimum sum beamforming gain to that localized CSI neighborhood. We also incorporate a UE specific parameter to enlarge the neighborhood to robustify the link further. We adopt an indoor channel model to demonstrate the performance of our solution, and benchmark against a usually optimal (but now sub-optimal due to outdated CSI) maximum ratio transmission (MRT) and a subspace based method. We numerically show that our algorithm outperforms the other methods by a large margin. This shows that customized environment-specific solutions are important to solve many future wireless applications, and we have paved the way to develop further data-driven approaches.

Backscatter communication (BSC) is a promising solution for Internet-of-Things (IoT) connections due to its low-complexity, low-cost, and energy-efficient solution for sensors. There are several network infrastructure setups that can be used for BSC with IoT nodes/passive devices. One of them is a bistatic setup where there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader side. In this paper, we investigate a bistatic BSC setup with multiple antennas. We propose a novel algorithm to suppress direct link interference between the carrier emitter (CE) and the reader using beamforming into the nullspace of the CE-reader direct link to decrease the dynamic range of the system and increase the detection performance of the backscatter device (BSD). Further, we derive a Neyman-Pearson (NP) test and an exact closed-form expression for its performance in the detection of the BSD. Finally, simulation results show that the dynamic range of the system is significantly decreased and the detection performance of the BSD is increased by the proposed algorithm compared to a system not using beamforming in the CE, which could then be used in a host of different practical fields such as agriculture, transportation, factories, hospitals, smart cities, and smart homes.

Novel network architectures for 6G distributed massive MIMO systems rely on coherent signaling by distributed antenna panels which are coordinated by a central controller. This type of network architecture is based on reciprocity operation where antenna panels rely on uplink channel estimates for coherent downlink precoding. This paper proposes a calibration method for distributed massive MIMO systems, which overcomes hardware non-reciprocities in order to enable reciprocity-based operation. Measurements for system calibration are collected via a beam-sweep between all pairs of antenna panels. We lay out the system model for this new setup, and propose a maximum likelihood-based procedure to compute calibration coefficients based on the collected measurement set. The procedure is computationally efficient and stable, since 1) each iteration has a closed-form, and 2) the procedure is guaranteed to converge to at least a local optimum (or saddle point). Simulations indicate significant calibration improvements compared to re-using state of the art calibration schemes for the problem at hand.

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight (LOS) scenario with moderate mobility are presented, with 8 users served by a 100 antenna base Station (BS) at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time division duplex (TDD) configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the correlation of the MIMO channel vector over time is inspected for vehicles moving up to 29km/h. For a 100 antenna system, it is found that the channel state information (CSI) update rate requirement may increase by 7 times when compared to an 8 antenna system, whilst the power control update rate could be decreased by at least 5 times relative to a single antenna system.

The first measured results for massive multiple-input, multiple-output (MIMO) performance in a line-of-sight scenario with moderate mobility are presented, with eight users served in real time using a 100-antenna base station at 3.7 GHz. When such a large number of channels dynamically change, the inherent propagation and processing delay has a critical relationship with the rate of change, as the use of outdated channel information can result in severe detection and precoding inaccuracies. For the downlink (DL) in particular, a time-division duplex configuration synonymous with massive MIMO deployments could mean only the uplink (UL) is usable in extreme cases. Therefore, it is of great interest to investigate the impact of mobility on massive MIMO performance and consider ways to combat the potential limitations. In a mobile scenario with moving cars and pedestrians, the massive MIMO channel is sampled across many points in space to build a picture of the overall user orthogonality, and the impact of both azimuth and elevation array configurations are considered. Temporal analysis is also conducted for vehicles moving up to 29 km/h and real-time bit-error rates for both the UL and DL without power control are presented. For a 100-antenna system, it is found that the channel state information update rate requirement may increase by seven times when compared with an eight-antenna system, whilst the power control update rate could be decreased by at least five times relative to a single antenna system.

This paper presents a mutual coupling-based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An expectation-maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms the current state-of-the-art narrow-band calibration schemes in a mean squared error and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.

In massive MIMO performance evaluations it is often assumed that the terminal has a single antenna. The combination of multiple antennas in a terminal and massive MIMO precoding at the base station side can further improve overall system performance. We present measurement results for multi antenna terminals operating in different transmission schemes and how they perform under varying loading conditions. Gain expressions are derived that enable easy comparison between the transmission schemes. The evaluation is performed on realistic antennas integrated into Sony Xperia handsets tuned to 3.7 GHz and operated together with the Lund University massive MIMO (LuMaMi) test bed. It is concluded that the approach used in today's mobile systems, where up link and down link are addressed independently, will not provide the best performance. The performance can be improved by the selection of transmission schemes optimized for massive MIMO.

This paper presents a calibration technique for massive MIMO base stations, where the frequency responses of the transmit and/or receive analog front-ends are individually estimated and compensated for. Calibration is achieved by a first-round of channel sounding between base station antennas, followed by post-processing and a compensation stage. The proposed technique is general in the sense that is does not use external sources, nor internal dedicated circuits for calibration purposes. The only requirement of the technique is that mutual coupling between all pairs of sounded base station antennas exists and is known. Our analysis suggests that mutual coupling can be conveniently used for calibration purposes, and that multipath propagation during calibration is the most prominent source for calibration inaccuracies.

In this paper we consider time-division-duplex (TDD) reciprocity calibration of a massive MIMO system. The calibration of a massive MIMO system can be done entirely at the base station (BS) side by sounding the BS antennas one-by-one while receiving with the other BS antennas. With an M antenna BS, this generates M(M - 1) signals that can be used for calibration purposes. In this paper we study several least-squares (LS) based estimators, differing in the number of received signals that are being used. We compare the performance of the estimators, and we conclude that is possible to accurately calibrate an entire BS antenna array using the mutual coupling between antennas as the main propagation mechanism.