相关论文: GPU-accelerated partially linear multiuser detecti…
Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern…
In this paper, we study how to efficiently and reliably detect active devices and estimate their channels in a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) based grant-free non-orthogonal multiple…
This paper is to design and optimize a non-orthogonal and noncoherent massive multiple-input multiple-output (MIMO) framework towards enabling scalable ultra-reliable low-latency communications (sURLLC) in wireless systems beyond 5G. In…
We propose a method for channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The method exploits the band-sparsity of wireless channels in the…
The expected operating scenarios of 5G pose a great challenge to orthogonal frequency division multiplexing (OFDM) which has poor out of band (OoB) spectral properties, stringent synchronization requirements, and large symbol duration.…
The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates,…
In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and…
One of the primary challenges in short packet ultra-reliable and low-latency communications (URLLC) is to achieve reliable channel estimation and data detection while minimizing the impact on latency performance. Given the small packet size…
One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is less than 1ms . The very low latency requirement of URLLC entails a lot of work in all…
This work studies multiuser detection for one-bit massive multiple-input multiple-output (MIMO) systems in order to diminish the power consumption at the base station (BS). A low-complexity near-maximum-likelihood (nML) multiuser detection…
Ultra-low latency, the hallmark of fifth-generation mobile communications (5G), imposes exacting timing demands on identification as well. Current cryptographic solutions introduce additional computational overhead, which results in…
The development of sixth-generation (6G) mobile networks imposes unprecedented latency and reliability demands on multiple-input multiple-output (MIMO) communication systems, a key enabler of high-speed radio access. Recently, deep…
Network traffic is difficult to monitor and analyze, especially in high-bandwidth networks. Performance analysis, in particular, presents extreme complexity and scalability challenges. GPU (Graphics Processing Unit) technology has been…
Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave (mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output (MIMO) networks is to guarantee a fast radio resource management in both uplink…
As large language models (LLMs) continue to grow in size, distributed inference has become increasingly important. Model-parallel strategies must now efficiently scale not only across multiple GPUs but also across multiple nodes. In this…
In this paper, we investigate channel estimation techniques for 5G multicarrier systems. Due to the characteristics of the 5G application scenarios, channel estimation techniques have been tested in Orthogonal Frequency Division…
Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majority of existing approaches rely on temporal architectures that…
The fast-rising demand for wireless bandwidth requires rapid evolution of high-performance baseband processing infrastructure. Programmable many-core processors for software-defined radio (SDR) have emerged as high-performance baseband…
We propose a machine learning (ML)-based framework for downlink performance prediction in 5G networks using real-time measurements from commercial off-the-shelf (COTS) user equipment (UE). Our experimental platform integrates the srsRAN 5G…
This paper studies the detection and performance analysis problems for a relay network with $N$ parallel decode-and-forward (DF) relays. Due to the distributed nature of this network, it is practically very challenging to fulfill the…