This paper introduces LiTCom, a lightweight transmitter

and inference-capable receiver framework, designed to

enable robust 6G uplink communication under low signal-tonoise

(SNR) conditions. It embraces the resource asymmetry

between edge devices and the network infrastructure. LiTCom

simplifies transmitter design by applying basic low-pass filtering

for source coding and minimal channel coding, significantly

reducing the processing complexity. The receiver employs largescale

generative artificial intelligence (GenAI) models to infer

high semantic-fidelity content from highly distorted and degraded

signals beyond traditional decoding capabilities. Furthermore,

efficient power allocation strategies are developed by exploiting

data importance to improve system performance, which is

measured by the introduced quality of experience (QoE) metric.

Simulation results validate the effectiveness of the proposed LiTCom

framework and the lightweight coding design. Compared

with the 5G NR-like baseline (using JPEG source coding and

LDPC channel coding) and the Deep-JSCC baseline, LiTCom

achieves SNR gains up to 8 dB and 2.5 dB, respectively, while

reducing over 95% transmitter-side computations.