The POSEIDON project aims to define solutions for scalable CF-mMIMO operating in the sub-7 GHz frequency bands (where the available spectral resources are scarce) to overcome various challenges (coverage, capacity, environmental sustainability…). In particular, scalable CF-mMIMO architectures must be able to handle i) the dramatic increase in wireless traffic demand, which is caused by the exponential growth of connected wireless devices, and ii) the emerging services/applications requiring huge data traffic (e.g., high-quality video calls, holographic communications and Internet of Things/mMTC). Moreover, as an additional target, POSEIDON will propose architectures that may contribute in the roadmap for the necessary transition to greener solutions and infrastructures. With the ICT sector’s power consumption increasing exponentially through the different generations of radio mobile networks, a tenfold increase of the power consumption for the wireless access is expected over the next decade. Thus, power consumption is among the critical key performance indicators (KPIs) to be optimized in 6G networks. To this end, POSEIDON will provide solutions to satisfy the expected 6G’s requirements with ever-increasingly ubiquitous and reliable wireless connectivity while at the same time steadily addressing the crucial reduction of the ecological impact of cellular infrastructures. The project will focus on lower layers, physical and access, of CF-mMIMO where the consequences of these aforementioned objectives are direct and challenging. WP3 is dedicated to the development of signal processing solutions that enhance energy efficiency, spectral efficiency, and scalability in CF-mMIMO systems. These solutions will be based on realistic propagation and hardware impairment models from WP1, ensuring that the outcomes are practical. Special attention is given to distributed and scalable aspects of these architectures. This first version of this deliverable explores several areas of CF-mMIMO system development: **Channel Estimation Errors and Non-linearity: Addressing the inaccuracy of channel estimation and its effects on system efficiency. A detailed analysis of CSI imperfections, such as channel estimation errors and the influence of hardware impairments like non-linear power amplifiers, is provided. Additionally, precoding schemes for downlink data transmission are analyzed to improve performance in the presence of limited feedback. **State-of-the-Art CSI Compression: Different approaches for CSI compression are explored, such as quantization based, compressed sensing, dimensionality reduction techniques, and deep learning-based methods. These techniques are crucial for enhancing the feedback efficiency and computational scalability of CF-mMIMO systems, particularly in large-scale deployments. In the next version of this deliverable, we will implement one of these approaches specifically within the cell-free massive MIMO context, providing practical insights into their real-world application. **Energy-Efficient Precoding in CF-mMIMO-OFDM: A focus on local precoding techniques, specifically tailored to deal with hardware imperfections and to improve energy efficiency in OFDM-based CF-mMIMO systems. Two key precoding strategies, full-pilot zero-forcing (FZF) and regularized zero-forcing (RZF), are considered to enhance performance under hardware impairments (HWI), particularly power amplifier distortions.