The next generation of communication is envisioned to be intelligent
communication, that can replace traditional symbolic communication, where
highly condensed semantic information considering both source and channel will
be extracted and transmitted with high efficiency. The recent popular large
models such as GPT4 and the boosting learning techniques lay a solid foundation
for the intelligent communication, and prompt the practical deployment of it in
the near future. Given the characteristics of "training once and widely use" of
those multimodal large language models, we argue that a pay-as-you-go service
mode will be suitable in this context, referred to as Large Model as a Service
(LMaaS). However, the trading and pricing problem is quite complex with
heterogeneous and dynamic customer environments, making the pricing
optimization problem challenging in seeking on-hand solutions. In this paper,
we aim to fill this gap and formulate the LMaaS market trading as a Stackelberg
game with two steps. In the first step, we optimize the seller's pricing
decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes
the prices of large models iteratively by reasoning customers' future rental
decisions, which is able to achieve a near-optimal pricing solution. In the
second step, we optimize customers' selection decisions by designing a robust
selecting and renting (RSR) algorithm, which is guaranteed to be optimal with
rigorous theoretical proof. Extensive experiments confirm the effectiveness and
robustness of our algorithms.

本文提出了 Large Model as a Service (LMaaS) 的概念，将大型模型的灵活租赁作为智能通信的一种策略，通过优化定价和选择决策来解决动态和异质客户环境下的定价优化问题。

LMaaS：大型模型作为服务的定价策略探索

LMaaS: Exploring Pricing Strategy of Large Model as a Service for  Communication

Intelligent communication is gradually considered as the mainstream direction
in future wireless communications. As a major branch of machine learning, deep
learning (DL) has been applied in physical layer communications and has
demonstrated an impressive performance improvement in recent years. However,
most of the existing works related to DL focus on data-driven approaches, which
consider the communication system as a black box and train it by using a huge
volume of data. Training a network requires sufficient computing resources and
extensive time, both of which are rarely found in communication devices. By
contrast, model-driven DL approaches combine communication domain knowledge
with DL to reduce the demand for computing resources and training time. This
article reviews the recent advancements in the application of model-driven DL
approaches in physical layer communications, including transmission scheme,
receiver design, and channel information recovery. Several open issues for
further research are also highlighted after presenting the comprehensive
survey.

该文章回顾了最近在物理层通信中使用模型驱动深度学习方法的应用，包括传输方案、接收机设计和信道信息恢复，并在展示全面调查后，提出了若干进一步研究的开放问题。