Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction

Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the deployment of high-accuracy transformer-based predictors on base-station hardware remains challenging because the most capable models carry upwards of 30\,M parameters. This paper introduces Lightweight PCGAE-Net, which addresses the efficiency problem not by post-hoc compression but by correcting two architectural flaws in the current state of the art. The first is a sequential attention ordering bias: in CS3T-UNet, group-wise temporal attention (GTA) always operates on features that have already been transformed by cross-shaped spatial attention (CSA), distorting what temporal information GTA can capture. We remove this dependency by routing both attention modules to the same layer-normalized input and combining their independent outputs through a learned per-channel sigmoid CrossGate. The second flaw is an uncompressed bottleneck: applying full self-attention at the deepest encoder stage, where channel depth reaches $4C$, is quadratically expensive and carries redundant features. A Bottleneck AutoEncoder (BAE) with $1\times1$ convolutions halves this depth and uses an auxiliary reconstruction loss to prevent information collapse. Wrapping these components inside a shallower encoder-decoder with frequency-domain dimensionality reduction ($N_f\!=\!32$, $C\!=\!48$) produces a model with just 8.54\,M parameters -- 58\% fewer than the CS3T-UNet baseline -- that outperforms it by up to 3.26\,dB at 5\,km/h and 6.0\,dB at 9\,km/h in single-step prediction on QuaDriGa dataset.

Simultaneous Multi-Beam Radar with Full Range Resolution exploiting Space-Code Beamforming

Conventional monostatic radar systems typically exhibit a trade-off between long-range target detection achieved through narrow beams and short-range wide-area surveillance employing broad beams. Realizing both functionalities within a single system, enabling simultaneous surveillance and long-range target localization, poses a significant challenge. This paper presents a novel signal model and an all-digital frequency domain radar architecture leveraging first-of-its-kind space-code beamforming technique to achieve ubiquitous radar coverage. We show that the range-angle map can be estimated for all targets at full range-resolution for all beams compared to existing subcarrier based beamforming radars.

Low-Complexity Linear Decoupling of Users for Uplink Massive MU-MIMO Detection

Multi-user massive MIMO is a promising candidate for future wireless communication systems. It enables users with different requirements to be connected to the same base station (BS) on the same set of resources. In uplink massive MU-MIMO, while users with different requirements are served, decoupled signal detection helps in using a user-specific detection scheme for every user. In this paper, we propose a low-complexity linear decoupling scheme called Sequential Decoupler (SD), which aids in the parallel detection of each user's data streams. The proposed algorithm shows significant complexity reduction, particularly when the number of users in the system increases. In the numerical simulations, it has been observed that the complexity of the proposed scheme is only 0.15% of the conventional Singular Value Decomposition (SVD) based decoupling and 47% to the pseudo-inverse based decoupling schemes when 80 users with two antennas each are served by the BS.

A Novel Local and Hyper-Local Multicast Services Transmission Scheme for Beyond 5G Networks

The efficiency of the broadcast network is impacted by the different types of services that may be transmitted over it. Global services serve users across the entire network, while local services cater to specific regions, and hyper-local services have even narrower coverage. Multimedia Broadcast over a Single-Frequency Network (MBSFN) is typically used for global service transmission while existing literature extensively discusses schemes for transmitting local or hyper-local services with or without Single Frequency Network (SFN) gain. However, these schemes fall short when network-wide requests for only local and hyper-local services are made, leading operators to scale down to either Single Cell-Point to Multipoint (SCPtM) or Multi-Frequency Network (MFN). SCPtM is highly susceptible to interference, and MFN requires substantial amounts of valuable spectrum. They both employ the Least Channel Gain (LCG) strategy for transmitting hyper-local services without SFN gain. Our proposed Local and Hyper-Local Services (LHS) transmission scheme utilizes the knowledge of user distribution and their corresponding radio link channel quality to schedule single or multi-resolution, local or hyper-local services within a three-cell cluster and aims to enhance spectral efficiency and maximize system throughput. It leverages Scalable Video Coding (SVC) in conjunction with Hierarchical Modulation (HM) for transmitting multi-resolution multimedia content to address the problem of heterogeneity amongst the multicast group users. The proposed scheme also employs macro-diversity combining with optimal HM parameters for each gNB catering to a local service area in order to minimize the service outage. System-level simulation results testify to the better performance achieved by the proposed LHS transmission scheme with respect to SCPtM.

Using DCFT for Multi-Target Detection in Distributed Radar Systems with Several Transmitters

In distributed radar systems, when several transmitters radiate simultaneously, the reflected signals need to be distinguished at the receivers to detect various targets. If the transmit signals are in different frequency bands, they require a large overall bandwidth. Instead, a set of pseudo-orthogonal waveforms derived from the Zadoff-Chu (ZC) sequences could be accommodated in the same band, enabling the efficient use of available bandwidth for better range resolution. In such a design, special care must be given to the 'near-far' problem, where a reflection could possibly become difficult to detect due to the presence of stronger reflections. In this work, a scheme to detect multiple targets in such distributed radar systems is proposed. It performs successive cancellations (SC) starting from the strong, detectable reflections in the domain of the Discrete Chirp-Fourier Transform (DCFT) after compensating for Doppler shifts, enabling the subsequent detections of weaker targets which are not trivially detectable. Numerical simulations corroborate the efficacy and usefulness of the proposed method in detecting weak target reflections.