Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Since classical algorithms for symbol detection in MIMO setups require large computational resources or provide poor results, data-driven algorithms are becoming more popular. Most of the proposed algorithms, however, introduce approximations leading to degraded performance for realistic MIMO systems. In this paper, we introduce a neural-enhanced hybrid model, augmenting the analytic backbone algorithm with state-of-the-art neural network components. In particular, we introduce a self-attention model for the enhancement of the iterative Orthogonal Approximate Message Passing (OAMP)-based decoding algorithm. In our experiments, we show that the proposed model can outperform existing data-driven approaches for OAMP while having improved generalization to other SNR values at limited computational overhead.

Multiple-input multiple-output (MIMO) millimeter-wave (mm-wave) systems are vulnerable to hardware impairments due to operating at high frequencies and employing a large number of radio-frequency hardware components. In particular, nonlinear power amplifiers (PAs) employed at the transmitter distort the signal when operated close to saturation due to energy efficiency considerations. In this paper, we study the performance of an MIMO mm-wave hybrid beamforming scheme in the presence of nonlinear PAs. First, we develop a statistical model for the transmitted signal in such systems and show that the spatial direction of the inband distortion is shaped by the beamforming filter. This suggests that even in the large antenna regime, where narrow beams can be steered toward the receiver, the impact of nonlinear PAs should not be ignored. Then, by employing a realistic power consumption model for the PAs, we investigate the tradeoff between spectral and energy efficiency in such systems. Our results show that increasing the transmit power level when the number of transmit antennas grows large can be counter-effective in terms of energy efficiency. Furthermore, using numerical simulation, we show that when the transmit power is large, analog beamforming leads to higher spectral and energy efficiency compared to digital and hybrid beamforming schemes.

Although the benefits of precoding and combining of data streams are widely recognized, the potential of precoding the pilot signals at the user equipment (UE) side and combining them at the base station (BS) side has not received adequate attention. This paper considers a multiuser multiple input multiple output (MU-MIMO) cellular system in which the BS acquires channel state information (CSI) by means of uplink pilot signals and proposes pilot precoding and combining to improve the CSI quality. We first evaluate the channel estimation performance of a baseline scenario in which CSI is acquired with no pilot precoding. Next, we characterize the channel estimation error when the pilot signals are precoded by spatial filters that asymptotically maximize the channel estimation quality. Finally, we study the case when, in addition to pilot precoding at the UE side, the BS utilizes the second order statistics of the channels to further improve the channel estimation performance. The analytical and numerical results show that, specially in scenarios with large number of antennas at the BS and UEs, pilot precoding and combining has a great potential to improve the channel estimation quality in MU-MIMO systems.

Although the benefits of precoding and combining data signals are widely recognized, the potential of these techniques for pilot transmission is not fully understood. This is particularly relevant for multiuser multiple-input multiple-output (MU-MIMO) cellular systems using millimeter-wave (mmWave) communications, where multiple antennas have to be used both at the transmitter and the receiver to overcome the severe path loss. In this paper, we characterize the gains of pilot precoding and combining in terms of channel estimation quality and achievable data rate. Specifically, we consider three uplink pilot transmission scenarios in an mmWave MU-MIMO cellular system: 1) non-precoded and uncombined; 2) precoded but uncombined; and 3) precoded and combined. We show that a simple precoder that utilizes only the second-order statistics of the channel reduces the variance of the channel estimation error by a factor that is proportional to the number of user equipment (UE) antennas. We also show that using a linear combiner design based on the second-order statistics of the channel significantly reduces multiuser interference and provides the possibility of reusing some pilots. Specifically, in the large antenna regime, pilot precoding and combining help to accommodate a large number of UEs in one cell, significantly improve channel estimation quality, boost the signal-to-noise ratio of the UEs located close to the cell edges, alleviate pilot contamination, and address the imbalanced coverage of pilot and data signals.

In an organisation several Business Process Management System (BPMS) products can co-exist and work alongside each other. Each one of these BPM tools has its own definition of process instances, creating a heterogeneous environment. This reduces interoperability between business process management systems and increases the effort involved in analysing the data. In this paper, we propose a common model for business process instances, named Business Process Instance Model (BPIM), which provides a holistic view of business process instances generated from multiple systems. BPIM consists of visual notations and their metadata schema. It captures three dimensions of process instances: process execution paths, instance data provenance and meta-data. BPIM aims to provide an abstract layer between the process instance repository and BPM engines, leading to common understanding of business process instances.

Despite the tremendous research effort spent on MIMO systems - very few papers address the impact of hardware imperfections. In this paper we analyze the distortion noise of the transmitter branches of a MIMO transmitter. In particular we analyze the cross-correlation of the distortion noises for the case that the transmitted signals are correlated. This case arises when there is just a single stream being transmitted with beamforming or in the case of multiple streams and linear precoding. We analyze the problem in two ways: using analytical derivations and using measurements on our testbed. In the analytical case we assume a transmitter impaired by a 3rd order non-linearity. For this scenario we find that the absolute correlation coefficient of the distortion noises is given by the absolute value of the correlation coefficients of the input signals to the power of three, while the phase is the same as that of the input signal. Our measurements also show good agreement between measurements and analytical results. It seems to suggest that the distortion noises can be regarded as practically independent whenever there are two or more modulation streams being transmitted (spatial multiplexing). In a single-stream scenario, the distortion noise will tend to have the same spatial distribution as the desired signal.