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We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Starting by analyzing the defects of existing joint source channel coding (JSCC) methods, we show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. To address this problem, we take advantage of learning from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, to develop such a semantic communication system is indeed a nontrivial task, considering the nondifferentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement by using the policy gradient technique, and to interact with the surrounding noisy environment in a natural way. We have testified the proposed method in the challenging European-parliament dataset. Experiments on both AWGN and phase-invariant fading channel have confirmed the superiority of our method in revealing the semantic meanings, and better handling the channel noise especially in low-SNR situations. Apart from the experimental results, we further provide an indepth look at how the semantics model behaves, along with its superb generalization ability in real-life examples. As a brand new method in learning-based JSCC tasks, we also exemplify an RL-based image transmission paradigm, both to prove the generalization ability, and to leave this new topic for future discussion.
In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collision
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational input-response pairs. The resulting sentence embeddings perform w
Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper
Semantic Similarity between two sentences can be defined as a way to determine how related or unrelated two sentences are. The task of Semantic Similarity in terms of distributed representations can be thought to be generating sentence embeddings (de
We consider the problem where $M$ agents interact with $M$ identical and independent environments with $S$ states and $A$ actions using reinforcement learning for $T$ rounds. The agents share their data with a central server to minimize their regret.