Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications

Abstract

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. In this paper, a deep learning-based task-oriented communication architecture is proposed where the user extracts, compresses and transmits semantics in an end-to-end (E2E) manner. Furthermore, an approach is proposed to compress the semantics according to their importance relevant to the task, namely, adaptable semantic compression (ASC). Assuming a delay-intolerant system, supporting multiple users indicates a problem that executing with the higher compression ratio requires fewer channel resources but leads to the distortion of semantics, while executing with the lower compression ratio requires more channel resources and thus may lead to a transmission failure due to delay constraint. To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm by separating the problem into two subproblems and solving iteratively to obtain the convergent solution. Furthermore, considering the scenarios where users have various service levels, a compression ratio, resource allocation, and user selection (CRRAUS) algorithm is proposed to deal with the problem. In CRRAUS, users are adaptively selected to complete the corresponding intelligent tasks based on branch and bound method at the expense of higher algorithm complexity compared with CRRA. Simulation results show that the proposed ASC approach can reduce the size of transmitted data by up to 80% without reducing task success, and the proposed CRRA and CRRAUS algorithms can obtain at least 15% and 10% success gains over baseline algorithms, respectively.

Adaptive resource optimization for edge inference with goal-oriented communications

“…1, where, inspired by the IB principle, the bottleneck is made time-varying, by adaptively selecting in each time slot the most suitable CE/CC pair, according to a strategy resulting from the solution of two possible constrained optimization problems: i) minimum energy consumption, under average service delay and accuracy constraints strategy (MEDA); ii) maximum accuracy under average service delay and energy constraints strategy (MADE). This is significantly different from the static optimization proposed in [41], where scheduling (and buffering) is not considered as a fundamental ingredient to make best use of the available resources in a dynamic fashion. Furthermore, we adapt to the buffer load and channel condition the assignment of computation and transmission resources, as well as the size of the compressed data, by a dynamic choice of the proper CE/CC pair at each time slot.…”

“…In all the above works, except [36], the focus was on the communication system, but without optimizing the usage of the available resources, namely communication, computational, and semantic-related resources. Resource optimization has been considered in [41] and [5,6]. Specifically, the authors in [41] propose to tune the GOC resources, e.g., bandwidths and powers, as well as the size of the goal-oriented compressed representation of the data, in order to optimize the success probability of the task under flat-fading zero-mean Gaussian channels.…”

“…Resource optimization has been considered in [41] and [5,6]. Specifically, the authors in [41] propose to tune the GOC resources, e.g., bandwidths and powers, as well as the size of the goal-oriented compressed representation of the data, in order to optimize the success probability of the task under flat-fading zero-mean Gaussian channels. This optimization, which includes training of the compressive and classification (C C) architecture, and the choice of the data compression ratio, is performed once, by exploiting knowledge of the average statistics (e.g., standard deviation) of the flat-fading channel.…”

“…Furthermore, we adapt to the buffer load and channel condition the assignment of computation and transmission resources, as well as the size of the compressed data, by a dynamic choice of the proper CE/CC pair at each time slot. Note that this dynamic use of multiple low-complexity CE/CC (neural network) pairs, makes our approach quite different from [41] and the recent literature on semantic and GOC [15,17,19,33,44], where typically a fixed (very complex) DNN architecture is split among transmitter and receiver. The single DNN architecture that these GOC schemes have to train is (typically) very complex because it has to work well for a variety of state channels, noise levels, and required task performance, which the GOC may have to face.…”

Deep Learning for MIMO Communications

“…Measure of Semantic Importance: In semantic communications, the source data is mapped into different semantic features by the DNN models. Different semantic features are of different importance for completing target tasks, where semantic importance is defined as the correlation between the semantic features and the target task [36]. The method to measure the importance of semantic features can be variable in different semantic communication systems.…”