Low-Complexity Improved-Throughput Generalised Spatial Modulation: Bit-to-Symbol Mapping, Detection and Performance Analysis

Low-complexity improved-throughput generalised spatial modulation (LCIT-GSM) is proposed. More explicitly, in GSM, extra information bits are conveyed implicitly by activating a fixed number $N_{a}$ out of $N_{t}$ transmit antennas (TAs) at a time. As a result, GSM has the advantage of a reduced number of radio-frequency (RF) chains and reduced inter-antenna interference (IAI) at the cost of a lower throughput than its multiplexing-oriented full-RF based counterparts. Variable-${N_a}$ GSM mitigates this throughput reduction by incorporating all possible TA activation patterns associated with a variable value $N_{a}$ ranging from $1$ to $N_{t}$ during a single channel-use, which maximises the throughput of GSM but suffers a high complexity of the mapping book design and demodulation. In order to mitigate the complexity, \emph{first of all}, we propose two efficient schemes for mapping the information bits to the TA activation patterns, which can be readily scaled to massive MIMO setups. \emph{Secondly}, in the absence of IAI, we derive a pair of low-complexity near-optimal detectors, one of them has a reduced search scope, while the other benefits from a decoupled single-stream based signal detection algorithm. \emph{Finally}, the performance of the proposed LCIT-GSM system is characterised by the error probability upper bound (UB). Our Monte Carlo based simulation results confirm the improved error performance of our proposed scheme, despite its reduced signal detection complexity.

Augmented Shortcuts for Vision Transformers

Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation ability for extracting informative features from input images. However, the mainstream transformer models are designed with deep architectures, and the feature diversity will be continuously reduced as the depth increases, i.e., feature collapse. In this paper, we theoretically analyze the feature collapse phenomenon and study the relationship between shortcuts and feature diversity in these transformer models. Then, we present an augmented shortcut scheme, which inserts additional paths with learnable parameters in parallel on the original shortcuts. To save the computational costs, we further explore an efficient approach that uses the block-circulant projection to implement augmented shortcuts. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method, which brings about 1% accuracy increase of the state-of-the-art visual transformers without obviously increasing their parameters and FLOPs.

Decentralized Spatial-Temporal Trajectory Planning for Multicopter Swarms

Multicopter swarms with decentralized structure possess the nature of flexibility and robustness, while efficient spatial-temporal trajectory planning still remains a challenge. This report introduces decentralized spatial-temporal trajectory planning, which puts a well-formed trajectory representation named MINCO into multi-agent scenarios. Our method ensures high-quality local planning for each agent subject to any constraint from either the coordination of the swarm or safety requirements in cluttered environments. Then, the local trajectory generation is formulated as an unconstrained optimization problem that is efficiently solved in milliseconds. Moreover, a decentralized asynchronous mechanism is designed to trigger the local planning for each agent. A systematic solution is presented with detailed descriptions of careful engineering considerations. Extensive benchmarks and indoor/outdoor experiments validate its wide applicability and high quality. Our software will be released for the reference of the community.

Federated Learning with Positive and Unlabeled Data

We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation of resources and time. Different from the settings in traditional PU learning where the negative class consists of a single class, the negative samples which cannot be identified by a client in the federated setting may come from multiple classes which are unknown to the client. Therefore, existing PU learning methods can be hardly applied in this situation. To address this problem, we propose a novel framework, namely Federated learning with Positive and Unlabeled data (FedPU), to minimize the expected risk of multiple negative classes by leveraging the labeled data in other clients. We theoretically prove that the proposed FedPU can achieve a generalization bound which is no worse than $C\sqrt{C}$ times (where $C$ denotes the number of classes) of the fully-supervised model. Empirical experiments show that the FedPU can achieve much better performance than conventional learning methods which can only use positive data.

Optimal Status Update for Caching Enabled IoT Networks: A Dueling Deep R-Network Approach

In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users' data requests with the data packets cached in the edge caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status. Particularly, we first depict the evolutions of the AoI with each sensor from different users' perspective with time steps of non-uniform duration, which are determined by both the users' data requests and the ECN's status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies.

Universal Adder Neural Networks

Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values. In this paper, we present adder networks (AdderNets) to trade these massive multiplications in deep neural networks, especially convolutional neural networks (CNNs), for much cheaper additions to reduce computation costs. In AdderNets, we take the $\ell_1$-norm distance between filters and input feature as the output response. The influence of this new similarity measure on the optimization of neural network have been thoroughly analyzed. To achieve a better performance, we develop a special training approach for AdderNets by investigating the $\ell_p$-norm. We then propose an adaptive learning rate strategy to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient. As a result, the proposed AdderNets can achieve 75.7% Top-1 accuracy 92.3% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer. Moreover, we develop a theoretical foundation for AdderNets, by showing that both the single hidden layer AdderNet and the width-bounded deep AdderNet with ReLU activation functions are universal function approximators. These results match those of the traditional neural networks using the more complex multiplication units. An approximation bound for AdderNets with a single hidden layer is also presented.

Patch Slimming for Efficient Vision Transformers

This paper studies the efficiency problem for visual transformers by excavating redundant calculation in given networks. The recent transformer architecture has demonstrated its effectiveness for achieving excellent performance on a series of computer vision tasks. However, similar to that of convolutional neural networks, the huge computational cost of vision transformers is still a severe issue. Considering that the attention mechanism aggregates different patches layer-by-layer, we present a novel patch slimming approach that discards useless patches in a top-down paradigm. We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers. For each layer, the impact of a patch on the final output feature is approximated and patches with less impact will be removed. Experimental results on benchmark datasets demonstrate that the proposed method can significantly reduce the computational costs of vision transformers without affecting their performances. For example, over 45% FLOPs of the ViT-Ti model can be reduced with only 0.2% top-1 accuracy drop on the ImageNet dataset.

Adder Neural Networks

Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves massive multiplications between float values. In this paper, we present adder networks (AdderNets) to trade these massive multiplications in deep neural networks, especially convolutional neural networks (CNNs), for much cheaper additions to reduce computation costs. In AdderNets, we take the $\ell_1$-norm distance between filters and input feature as the output response. The influence of this new similarity measure on the optimization of neural network have been thoroughly analyzed. To achieve a better performance, we develop a special training approach for AdderNets by investigating the $\ell_p$-norm. We then propose an adaptive learning rate strategy to enhance the training procedure of AdderNets according to the magnitude of each neuron's gradient. As a result, the proposed AdderNets can achieve 75.7% Top-1 accuracy 92.3% Top-5 accuracy using ResNet-50 on the ImageNet dataset without any multiplication in convolutional layer. Moreover, we develop a theoretical foundation for AdderNets, by showing that both the single hidden layer AdderNet and the width-bounded deep AdderNet with ReLU activation functions are universal function approximators. These results match those of the traditional neural networks using the more complex multiplication units. An approximation bound for AdderNets with a single hidden layer is also presented.