This study investigates a virtual multiuser multiple-input multiple-output (MU-MIMO) system with PSK modulation, realized with a reconfigurable intelligent surface (RIS)-based transmitter. The study focuses on minimizing transmit power under quality-of-service (QoS) constraints while addressing the associated computational complexity. A discrete phase-shift RIS model is considered, and the power minimization problem is formulated in two scenarios. First, for QPSK user data, the symbol-error probability (SEP) is adopted as the QoS criterion. Second, for generalM-PSK modulation, the union-bound SEP (UBSEP) is used to define the QoS constraints. Based on the considered formulations, a partial branch-and-bound (PBB) approach is developed, which improves on full branch-and-bound (FBB) methods in the sense of allowing for favorable complexity performance trade-offs. For the special case of high-resolution RIS, the discrete phase-shift set is approximated by its continuous counterpart, enabling the reformulation of the original problems as constrained optimizations on an oblique manifold, which are solved with reduced computational complexity with the proposed bisection method. Numerical results demonstrate the effectiveness of the proposed approaches in minimizing the transmit power for different SEP requirements and showcase the balance between power efficiency and computational complexity.

The low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator for low-resolution MIMO receivers has demonstrated a significant reduction in mean-squared error (MSE) through the integration of a comparator network. This network consists of multiple simple comparators that produce binary outputs. In this study, we propose the Kalman filter-based channel estimator with comparator networks (KFB-CN) for temporally and spatially correlated channels for MIMO system employing 1-bit ADCs and a comparator network. After providing a mathematical derivation of the real-valued Kalman filter system and observation models, we demonstrate, through numerical simulations, that the KFB-CN outperforms the original KFB estimator. Additionally, we propose a comparator network selection algorithm which switches the comparators used on-the-fly to adapt to changes in the channel correlation coefficients.

The present study considers a virtual multiuser multiple-input multiple-output system with PSK modulation realized via the reconfigurable intelligent surface-based passive transmitter setup. The study considers a discrete phase shift reconfigurable intelligent surface model where the reflecting elements' coefficients are restricted to a discrete set. With this, a symbol-level precoding power minimization problem under the condition that the union-bound symbol-error probability is below a given requirement is formulated. The corresponding mixed integer problem is solved via the utilization of a sophisticated branch-and-bound strategy. Numerical results show that the proposed power minimization approach yields reduced average transmit power when compared to state-of-the-art techniques.

Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. We also develop a robust detector which takes into account the channel state information (CSI) mismatch statistics. By exploiting knowledge of the channel coefficients we devise a mean-square error (MSE) greedy search and a sequential signal-to-interference-plus-noise ratio (SINR) search for optimization of partially connected networks. Numerical results show that a system with extra virtual channels can outperform a system with additional receive antennas, in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimation with its error statistics, we construct a lower bound on the ergodic sum rate for a linear receiver. Simulation results confirm that the proposed approach outperforms the conventional 1-bit MIMO system in terms of BER, MSE and sum rate.

This study focuses on the development of low-resolution symbol-level precoding techniques for multiuser MIMO downlink systems with PSK modulation. While for QPSK the established minimum symbol error probability criterion is used, a criterion for PSK modulation, in general, is proposed based on the minimum union-bound symbol-error probability. Based on these criteria different low-resolution precoding approaches are proposed. First, suboptimal solutions are computed via a partial greedy search method. Then the suboptimal solutions are utilized as initialization for a novel branch-and-bound algorithm that can exploit knowledge of the system’s quality-of-service demands. Different than existing branch-and-bound approaches the proposed quality-of-service branch-and-bound method searches for a solution that attains a target symbol-error probability while going in the direction of the global optimal solution. In this sense, the proposed branch-and-bound method allows for tunable complexity performance trade-offs. Numerical results confirm that the proposed quality-of-service branch-and-bound algorithm yields reduced symbol-error probability with significantly smaller computational complexity than other state-of-the-art branch-and-bound designs.

A promising approach to reducing the energy consumption is to consider coarse quantization at the receiver. In this study, we investigate novel precoding techniques in space and time for bandlimited multiuser MIMO downlink channels with 1-bit quantization and oversampling at the receiver, considering zero-crossing modulation. The proposed time-instance zero-crossing modulation conveys the information into the time-instances of zero-crossings. Two design criteria for time-instance zero-crossing modulation are investigated, namely, the minimum distance to the decision threshold and the mean-square error between the received and the desired signal. The maximization of the minimum distance to the decision threshold can be formulated as a quadratically constraint quadratic program. As an alternative, an equivalent problem can be formulated based on power minimization, which reduces computational complexity. Departing from the conventional mean-square error based technique, a more sophisticated algorithm is developed, which implies active constellation extension in order to improve the performance at high SNR. The extended problem is solved with two approaches, namely by formulating the problem as a second-order cone program and by considering an alternating optimization algorithm. Numerical results show that the proposed time-instance zero-crossing precoding methods significantly improve the bit error rate compared to the state-of-the-art methods.

We propose an optimal MMSE precoding technique using quantized signals with constant envelope. Unlike the existing MMSE design that relies on 1-bit resolution, the proposed approach employs uniform phase quantization and the bounding step in the branch-and-bound method is different in terms of considering the most restrictive relaxation of the nonconvex problem, which is then utilized for a suboptimal design also. We also propose robust MMSE designs where we exploit second order statistics of a channel state information mismatch. Moreover, unlike prior studies, we propose three different soft detection methods and an iterative detection and decoding scheme that allow the utilization of channel coding in conjunction with low-resolution precoding. Besides an exact approach for computing the extrinsic information, we propose two approximations with reduced computational complexity. Numerical simulations show that utilizing the MMSE criterion instead of the established maximum-minimum distance to the decision threshold yields a lower bit-error-rate in many scenarios. Furthermore, when using the MMSE criterion, a smaller number of bound evaluations in the branch-and-bound method is required for low and medium SNR. Finally, results based on an LDPC block code indicate that the proposed receive processing schemes yield a lower bit-error-rate compared to the conventional design.

In this work, large-scale multiple-antenna systems are investigated, where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters. To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. Unlike existing works that use uniform oversampling, which samples the signal at a constant rate, a novel dynamic oversampling scheme is proposed. The basic idea is to perform time-varying nonuniform oversampling, which selects samples with nonuniform patterns that vary over time. We consider two system design criteria: a design that maximizes the achievable sum rate and another design that minimizes the mean square error of detected symbols. Dynamic oversampling is carried out using a dimension reduction matrix Δ, which can be computed by the generalized eigenvalue decomposition or by novel submatrix-level feature selection algorithms. Moreover, the proposed scheme is analyzed in terms of convergence, computational complexity and power consumption at the receiver. Simulations show that systems with the proposed dynamic oversampling outperform those with uniform oversampling in terms of computational cost, achievable sum rate and symbol error rate performance.

Low-resolution devices are promising for systems that demand low energy consumption and low complexity as required in IoT systems. In this study, we propose a novel waveform for bandlimited channels with 1-bit quantization and oversampling at the receivers. The proposed method implies that the information is conveyed within the time instances of zero-crossings which is then utilized in combination with a Gray-coding scheme. Unlike the existing method, the proposed method does not require optimization and transmission of a dynamic codebook. The proposed approach outperforms the state-of-the-art method in terms of bit error rate.

In this paper, we propose a dynamic oversampling technique for asynchronous large-scale multiple-antenna systems with 1-bit analog-to-digital converters at the base station that is suitable for sustainable internet of things and cellular networks. To the best of our knowledge, this is the first paper to introduce a dynamic oversampling technique for such systems. The main idea is to sample the received signal at a higher rate and only few weighted samples are chosen for further signal processing. We apply the generalized eigenvalue decomposition algorithm for linearly combining the samples and performing dimension reduction. We investigate the proposed technique in terms of the Bussgang theorem based sum rate capacity. Numerical results show that with the proposed dynamic oversampling technique the system can use small number of processing samples to achieve the same sum rates as the standard uniform oversampling technique while maintaining the same power consumption.

Large‐scale multi‐user multiple‐input multiple‐output (MU‐MIMO) systems and cloud radio access networks (C‐RANs) are promising technologies for the fifth generation (5G) of wireless networks. In this context, the use of low‐resolution analogue‐to‐digital converters (ADCs) is key for energy efficiency and for complying with constrained fronthaul links. Processing signals with a few bits implies performance loss and, therefore, techniques that can compensate for quantisation distortion are fundamental. In wireless systems, an automatic gain control (AGC) precedes the ADCs to adjust the input signal level in order to reduce the impact of quantisation. In this work, the authors propose the joint optimisation of the AGC, which works in the remote radio heads (RRHs), and a low‐resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works in the cloud unit (CU), for large‐scale MU‐MIMO systems with coarsely quantised signals. They develop linear and successive interference cancellation receivers based on the proposed joint AGC and LRA MMSE (AGC‐LRA‐MMSE) approach. An analysis of the achievable sum rates along with a computational complexity study is also carried out. Simulations show that the proposed AGC‐LRA‐MMSE design obtains substantial gains in bit error rates and achievable information rates over existing techniques.

In this work, the authors proposed the iterative access point (AP) selection (APS), linear minimum mean‐square error (MMSE) precoding and power allocation techniques for cell‐free massive multiple‐input multiple‐output (MIMO) systems. They considered the downlink channel with single‐antenna users and multiple‐antenna APs. They derive sum‐rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming and zero‐forcing schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information.

Large-scale multiple-antenna systems have been identified as a promising technology for the next generation of wireless systems. However, by scaling up the number of receive antennas the energy consumption will also increase. One possible solution is to use low-resolution analog-to-digital converters at the receiver. This paper considers large-scale multiple-antenna uplink systems with 1-bit analog-to-digital converters on each receive antenna. Since oversampling can partially compensate for the information loss caused by the coarse quantization, the received signals are firstly oversampled by a factor M. We then propose a low-resolution aware linear minimum mean-squared error channel estimator for 1-bit oversampled systems. Moreover, we characterize analytically the performance of the proposed channel estimator by deriving an upper bound on the Bayesian Cramér-Rao bound. Numerical results are provided to illustrate the performance of the proposed channel estimator.

Analog-to-digital conversion with high resolution in amplitude has a relatively high energy consumption in communication systems. A promising alternative to reduce the energy consumption is 1-bit quantization. Considering such a receiver, we design and analyze continuous phase modulation (CPM) schemes, which are favorable because of their bandwidth efficiency and their constant envelope. In this context, oversampling with respect to the symbol duration is promising because CPM signals are not strictly bandlimited and because it reduces the loss in achievable rate caused by the quantization. The additional degrees of freedom brought by oversampling can be exploited by higher order modulation schemes. A lower bound on the achievable rate is computed based on an auxiliary channel law. In a further step, we optimize the input distribution with an optimization strategy based on a Markov source model. For a specific example, we give upper bounds on the achievable rate and present a state-machine representation for sequences which are reconstructible at the receiver. Finally, the proposed approach has the advantage of a constant envelope enabling an energy efficient transmitter design while achieving only a slightly lower 90% power containment bandwidth efficiency than existing methods with 1-bit quantization and oversampling.

In this work, a two-level hierarchical MIMO system is proposed to combine the spatial multiplexing gain and beamforming gain in a strong LOS channel. The superior is a MIMO system that consists of specially arranged sub-arrays to fully exploit the spatial multiplexing gain in deterministic channels. Additionally, a deterministic spherical-wave channel model is introduced. This channel model includes the radiation patterns of the sub-arrays, orthogonal phase relations introduced by the specific sub-array arrangement and the path loss considering deployment in practical scenarios. The attenuation includes the free space path loss, the oxygen absorption, the rain attenuation in bad weather and the front-end loss. The regulations for the maximum radiated power and the available bandwidth at 60 GHz were also investigated. Furthermore, the maximum transmission rate and upper bound of the energy efficiency are modeled and calculated for the proposed system operating at 60 GHz compliant to those regulations, as well for a constraint of the maximum available transmit power on-board. The result shows that the proposed system architecture is promising to achieve over 100 Gbps for macro-cell backhaul links with reasonable antenna sizes and high energy efficiency.

To relax power consumption requirements in multi- gigabit/s communications systems low resolution quantization can be used. Information-theoretic results have shown that systems employing 1-bit quantization and oversampling are a viable option for this. This work investigates such a structure under the influence of additive Gaussian noise and two matched pulse shaping filters. It is described how a BCJR algorithm, based on a finite-state channel assumption, can be used to reconstruct symbols of higher order modulation schemes that allow the transmission of more than one bit per symbol. Furthermore, it is shown how symbol sources that are fitted to the 1-bit constraint can significantly improve the error rate performance of the system.

Oversampling combined with low quantization resolutions has been shown to be a viable option when aiming for energy efficiency in multigigabit/s communications systems. This work considers the case of 1-bit quantization combined with oversampling and shows how the performance of such a system can be improved by using matched pulse shaping filters and faster than Nyquist signaling. The channel is considered with additive Gaussian noise and the performance of the system is evaluated in terms of achievable information rate under symbol-by-symbol detection.

The authors present a robust adaptive beamforming algorithm based on the worst‐case (WC) criterion and the constrained constant modulus (CCM) approach, which exploits the constant modulus property of the desired signal. Similar to the existing worst‐case beamformer with the minimum variance design, the problem can be reformulated as a second‐order cone programme and solved with interior point methods. An analysis of the optimisation problem is carried out and conditions are obtained for enforcing its convexity and for adjusting its parameters. Furthermore, low‐complexity robust adaptive beamforming algorithms based on the modified conjugate gradient and an alternating optimisation strategy are proposed. The proposed low‐complexity algorithms can compute the existing WC constrained minimum variance and the proposed WC‐CCM designs with a quadratic cost in the number of parameters. Simulations show that the proposed WC‐CCM algorithm performs better than existing robust beamforming algorithms. Moreover, the numerical results also show that the performances of the proposed low‐complexity algorithms are equivalent or better than that of existing robust algorithms, whereas the complexity is more than an order of magnitude lower.

Electronic systems of the future require a very high bandwidth communications infrastructure within the system. This way the massive amount of compute power which will be available can be inter-connected to realize future powerful advanced electronic systems. Today, electronic inter-connects between 3D chip-stacks, as well as intra-connects within 3D chip-stacks are approaching data rates of 100 Gbit/s soon. Hence, the question to be answered is how to efficiently design the communications infrastructure which will be within electronic systems. Within this paper approaches and results for building this infrastructure for future electronics are addressed.