arXiv subjects

Christian Forsch

Publications and source records attributed to Christian Forsch.

8 recordsLinked to original sources

arXiv2026

On the Identifiability of Semi-Blind Estimation in Cell-Free Massive MIMO Networks

Semi-blind joint channel estimation and data detection (JCD) is a promising approach to mitigate pilot contamination in cell-free massive multiple-input multiple-output (CF-MaMIMO) networks. The effectiveness of such methods fundamentally depends on identifiability, i.e., the ability to unambiguously recover the unknown channel coefficients and transmitted data signals from the received uplink observations. In this work, we investigate the identifiability of semi-blind JCD from a large-scale system design perspective. We consider a CF-MaMIMO network in which access points (APs) and user equipments (UEs) are spatially distributed according to Poisson point processes (PPPs). The resulting network topology is modeled as bipartite random geometric graph (BRGG) that captures local connectivity induced by wireless propagation. To enable a tractable analysis, the spatially dependent graph model is approximated by a surrogate independent-edge random graph with matched degree distributions. Building on this model, we develop a recursive probabilistic analysis that characterizes the conditions under which semi-blind recovery succeeds with high probability. The proposed analysis reveals an identifiability region as a function of key system parameters, including AP and UE densities and the connectivity radius beyond which channel coefficients are assumed negligible. Monte Carlo simulations validate the predicted identifiability region and assess the accuracy of the proposed graph approximation. The proposed framework provides system level insights into how network density and connectivity affect identifiability in large-scale CF-MaMIMO systems and offers guidelines for selecting deployment parameters and pilot sequence lengths that enable reliable semi-blind recovery.

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arXiv2026

Expectation Propagation for Distributed Inference in Grant-Free Cell-Free Massive MIMO

Grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) systems are anticipated to be a key enabling technology for next-generation Internet-of-Things (IoT) networks, as they support massive connectivity without explicit scheduling. However, the large amount of connected devices prevents the use of orthogonal pilot sequences, resulting in severe pilot contamination (PC) that degrades channel estimation and data detection performance. Furthermore, scalable GF-CF-MaMIMO networks inherently rely on distributed signal processing. In this work, we consider the uplink of a GF-CF-MaMIMO system and propose two novel distributed algorithms for joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP). The first algorithm, denoted as JACD-EP, uses Gaussian approximations for the channel variables, whereas the second, referred to as JACD-EP-BG, models them as Bernoulli-Gaussian (BG) random variables. To integrate the BG distribution into the EP framework, we derive its exponential family representation and develop the two algorithms as efficient message passing over a factor graph constructed from the a posteriori probability (APP) distribution. The proposed framework is inherently scalable with respect to both the number of access points (APs) and user equipments (UEs). Simulation results show the efficient mitigation of PC by the proposed distributed algorithms and their superior detection accuracy compared to (genie-aided) centralized linear detectors.

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arXiv2025

Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO

Pilot contamination (PC) arises when the pilot sequences assigned to user equipments (UEs) are not mutually orthogonal, eventually due to their reuse. In this work, we propose a novel expectation propagation (EP)-based joint channel estimation and data detection (JCD) algorithm specifically designed to mitigate the effects of PC in the uplink of cell-free massive multiple-input multiple-output (CF-MaMIMO) systems. This modified bilinear-EP algorithm is distributed, scalable, demonstrates strong robustness to PC, and outperforms state-of-the-art Bayesian learning algorithms. Through a comprehensive performance evaluation, we assess the performance of Bayesian learning algorithms for different pilot sequences and observe that the use of non-orthogonal pilots can lead to better performance compared to shared orthogonal sequences. Motivated by this analysis, we introduce a new metric to quantify PC at the UE level. We show that the performance of the considered algorithms degrades monotonically with respect to this metric, providing a valuable theoretical and practical tool for understanding and managing PC via iterative JCD algorithms.

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arXiv2025

Distributed Iterative ML and Message Passing for Grant-Free Cell-Free Massive MIMO Systems

Cell-Free (CF) Massive Multiple-Input Multiple-Output (MaMIMO) is considered one of the leading candidates for enabling next-generation wireless communication. With the growing interest in the Internet of Things (IoT), the Grant-Free (GF) access scheme has emerged as a promising solution to support massive device connectivity. The integration of GF and CF-MaMIMO introduces significant challenges, particularly in designing distributed algorithms for activity detection and pilot contamination mitigation. In this paper, we propose a distributed algorithm that addresses these challenges. Our method first employs a component-wise iterative distributed Maximum Likelihood (ML) approach for activity detection, which considers both the pilot and data portions of the received signal. This is followed by a Pseudo-Prior Hybrid Variational Bayes and Expectation Propagation (PP-VB-EP) algorithm for joint data detection and channel estimation. Compared to conventional VB-EP, the proposed PP-VB-EP demonstrates improved convergence behavior and reduced sensitivity to initialization, especially when data symbols are drawn from a finite alphabet. The pseudo prior used in PP-VB-EP acts as an approximated posterior and serves as a regularization term that prevents the Message Passing (MP) algorithm from diverging. To compute the pseudo prior in a distributed fashion, we further develop a distributed version of the Variable-Level Expectation Propagation (VL-EP) algorithm.

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arXiv2024

Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO

We consider the uplink of a grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) system. We propose an algorithm for distributed joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP) called JACD-EP. We develop the algorithm by factorizing the a posteriori probability (APP) of activities, channels, and transmitted data, then, mapping functions and variables onto a factor graph, and finally, performing a message passing on the resulting factor graph. If users with the same pilot sequence are sufficiently distant from each other, the JACD-EP algorithm is able to mitigate the effects of pilot contamination which naturally occurs in grant-free systems due to the large number of potential users and limited signaling resources. Furthermore, it outperforms state-of-the-art algorithms for JACD in GF-CF-MaMIMO systems.

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arXiv2024

Detection Schemes with Low-Resolution ADCs and Spatial Oversampling for Transmission with Higher-Order Constellations in the Terahertz Band

In this work, we consider Terahertz (THz) communications with low-resolution uniform quantization and spatial oversampling at the receiver side, corresponding to a single-input multiple-output (SIMO) transmission. We fairly compare different analog-to-digital converter (ADC) parametrizations by keeping the ADC power consumption constant. Here, 1-, 2-, and 3-bit quantization is investigated with different oversampling factors. We analytically compute the statistics of the detection variable, and we propose the optimal and several suboptimal detection schemes for arbitrary quantization resolutions. Then, we evaluate the symbol error rate (SER) of the different detectors for 16- and 64-ary quadrature amplitude modulation (QAM). The results indicate that there is a noticeable performance degradation of the suboptimal detectors compared to the optimal detector when the constellation size is larger than the number of quantization levels. Furthermore, at low signal-to-noise ratios (SNRs), 1-bit quantization outperforms 2- and 3-bit quantization, respectively, even when employing higher-order constellations. We confirm our analytical results by Monte Carlo simulations. Both a pure line-of-sight (LoS) and a more realistically modeled indoor THz channel are considered. Then, we optimize the input signal constellation with respect to SER for 1- and 2-bit quantization. The results give insights for optimizing higher-order constellations for arbitrary quantization resolutions and show that the minimum SER can be lowered significantly by appropriately placing the constellation points.

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arXiv2023

Bilinear Expectation Propagation for Distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO

We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.

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arXiv2022

Analysis of Neural Image Compression Networks for Machine-to-Machine Communication

Video and image coding for machines (VCM) is an emerging field that aims to develop compression methods resulting in optimal bitstreams when the decoded frames are analyzed by a neural network. Several approaches already exist improving classic hybrid codecs for this task. However, neural compression networks (NCNs) have made an enormous progress in coding images over the last years. Thus, it is reasonable to consider such NCNs, when the information sink at the decoder side is a neural network as well. Therefore, we build-up an evaluation framework analyzing the performance of four state-of-the-art NCNs, when a Mask R-CNN is segmenting objects from the decoded image. The compression performance is measured by the weighted average precision for the Cityscapes dataset. Based on that analysis, we find that networks with leaky ReLU as non-linearity and training with SSIM as distortion criteria results in the highest coding gains for the VCM task. Furthermore, it is shown that the GAN-based NCN architecture achieves the best coding performance and even out-performs the recently standardized Versatile Video Coding (VVC) for the given scenario.

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