AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services

Recent advancements in large language models (LLMs) have led to their widespread adoption and large-scale deployment across various domains. However, their environmental impact, particularly during inference, has become a growing concern due to their substantial energy consumption and carbon footprint. Existing research has focused on inference computation alone, overlooking the analysis and optimization of carbon footprint in network-aided LLM service systems. To address this gap, we propose AOLO, a framework for analysis and optimization for low-carbon oriented wireless LLM services. AOLO introduces a comprehensive carbon footprint model that quantifies greenhouse gas emissions across the entire LLM service chain, including computational inference and wireless communication. Furthermore, we formulate an optimization problem aimed at minimizing the overall carbon footprint, which is solved through joint optimization of inference outputs and transmit power under quality-of-experience and system performance constraints. To achieve this joint optimization, we leverage the energy efficiency of spiking neural networks (SNNs) by adopting SNN as the actor network and propose a low-carbon-oriented optimization algorithm, i.e., SNN-based deep reinforcement learning (SDRL). Comprehensive simulations demonstrate that SDRL algorithm significantly reduces overall carbon footprint, achieving an 18.77% reduction compared to the benchmark soft actor-critic, highlighting its potential for enabling more sustainable LLM inference services.

Reconfigurable Intelligent Surface & Edge -- An Introduction of an EM manipulation structure on obstacles' edge

Reconfigurable Intelligent Surface (RIS) or metasurface is one of the important enabling technologies in mobile cellular networks that can effectively enhance the signal coverage performance in obstructed regions, and it is generally deployed on surfaces different from obstacles to redirect electromagnetic (EM) waves by reflection, or covered on objects' surfaces to manipulate EM waves by refraction. In this paper, Reconfigurable Intelligent Surface & Edge (RISE) is proposed to extend RIS' abilities of reflection and refraction over surfaces to diffraction around obstacles' edge for better adaptation to specific coverage scenarios. Based on that, this paper analyzes the performance of several different deployment locations and EM manipulation structure designs for different coverage scenarios. Then a novel EM manipulation structure deployed at the obstacles' edge is proposed to achieve static EM environment modification. Simulations validate the preference of the schemes for different scenarios and the new structure achieves better coverage performance than other typical structures in the static scheme.

Study on MCS Selection and Spectrum Allocation for URLLC Traffic under Delay and Reliability Constraint in 5G Network

To support Ultra-Reliable and Low Latency Communications (URLLC) is an essential character of the 5th Generation (5G) communication system. Unlike the other two use cases defined in 5G, e.g. enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC), URLLC traffic has strict delay and reliability requirement. In this paper, an analysis model for URLLC traffic is proposed from the generation of a URLLC traffic until its transmission over a wireless channel, where channel quality, coding scheme with finite coding length, modulation scheme and allocated spectrum resource are taken into consideration. Then, network calculus analysis is applied to derive the delay guarantee for periodical URLLC traffic. Based on the delay analysis, the admission region is found under certain delay and reliability requirement, which gives a lower bound on required spectrum resource. Theoretical results in the scenario of a 5G New Radio system are presented, where the SNR thresholds for adaptive modulation and coding scheme selection, transmission rate and delay, as well as admission region under different configurations are discussed. In addition, simulation results are obtained and compared with theoretical results, which validates that the admission region derived in this work provides a lower spectrum allocation bound.