25 research outputs found
Wireless Video Transmission with Over-the-Air Packet Mixing
In this paper, we propose a system for wireless video transmission with a
wireless physical layer (PHY) that supports cooperative forwarding of
interfered/superimposed packets. Our system model considers multiple and
independent unicast transmissions between network nodes while a number of them
serve as relays of the interfered/superimposed signals. For this new PHY the
average transmission rate that each node can achieve is estimated first. Next,
we formulate a utility optimization framework for the video transmission
problem and we show that it can be simplified due to the features of the new
PHY. Simulation results reveal the system operating regions for which
superimposing wireless packets is a better choice than a typical cooperative
PHY.Comment: 2012 Packet Video Worksho
Joint Optimization of DNN Inference Delay and Energy under Accuracy Constraints for AR Applications
The high computational complexity and high energy consumption of artificial
intelligence (AI) algorithms hinder their application in augmented reality (AR)
systems. This paper considers the scene of completing video-based AI inference
tasks in the mobile edge computing (MEC) system. We use multiply-and-accumulate
operations (MACs) for problem analysis and optimize delay and energy
consumption under accuracy constraints. To solve this problem, we first assume
that offloading policy is known and decouple the problem into two subproblems.
After solving these two subproblems, we propose an iterative-based scheduling
algorithm to obtain the optimal offloading policy. We also experimentally
discuss the relationship between delay, energy consumption, and inference
accuracy.Comment: 6 pages, 7 figures, accepted by Globecom202
Network coding cooperation performance analysis in wireless network over a lossy channel, M users and a destination scenario
Network coding (NC), introduced at the turn of the century, enables nodes in a network to combine data algebraically before either sending or forwarding them. Random network coding has gained popularity over the years by combining the received packet randomly before forwarding them, resulting in a complex Jordan Gaussian Elimination (JGE) decoding process. The effectiveness of random NC is through cooperation among nodes. In this paper, we propose a simple, low-complexity cooperative protocol that exploits NC in a deterministic manner resulting in improved diversity, data rate, and less complex JGE decoding process. The proposed system is applied over a lossy wireless network. The scenario under investigation is as follows: M users must send their information to a common destination D and to exchange the information between each others, over erasure channels; typically the channels between the users and the destination are worse than the channels between users. It is possible to significantly reduce the traffic amon g users and destination, achieving significant bandwidth savings, by combining packets from different users in simple, deterministic ways without resorting to extensive header information before being forwarded to the destination and the M users. The key problem we try to address is how to efficiently combine the packets at each user while exploiting user cooperation and the probability of successfully recovering information from all users at D with k < 2M unique linear equations, accounting for the fact that the remaining packets will be lost in the network and there are two transmission stages. Simulation results show the behaviour for two and three transmission stages. Our results show that applying NC protocols in two or three stages decreases the traffic significantly, beside the fact that the proposed protocols enable the system to retrieve the lost packets rather than asking for ARQ, resulting in improved data flow, and less power consumption. In fact, in some protocols the ARQ dropped from the rate 10-1 to 10-4, because of the proposed combining algorithm that enables the nodes to generate additional unique linear equations to broadcast rather than repeating the same ones via ARQ. Moreover, the number of the transmitted packets in each cooperative stage dropped from M (M − 1) to just M packets, resulting to 2 M packets instead 2 (M2 − 1) when three stages of transmission system are used instead of one stage (two cooperative stages)
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
Departing from the classic paradigm of data-centric designs, the 6G networks
for supporting edge AI features task-oriented techniques that focus on
effective and efficient execution of AI task. Targeting end-to-end system
performance, such techniques are sophisticated as they aim to seamlessly
integrate sensing (data acquisition), communication (data transmission), and
computation (data processing). Aligned with the paradigm shift, a task-oriented
over-the-air computation (AirComp) scheme is proposed in this paper for
multi-device split-inference system. In the considered system, local feature
vectors, which are extracted from the real-time noisy sensory data on devices,
are aggregated over-the-air by exploiting the waveform superposition in a
multiuser channel. Then the aggregated features as received at a server are fed
into an inference model with the result used for decision making or control of
actuators. To design inference-oriented AirComp, the transmit precoders at edge
devices and receive beamforming at edge server are jointly optimized to rein in
the aggregation error and maximize the inference accuracy. The problem is made
tractable by measuring the inference accuracy using a surrogate metric called
discriminant gain, which measures the discernibility of two object classes in
the application of object/event classification. It is discovered that the
conventional AirComp beamforming design for minimizing the mean square error in
generic AirComp with respect to the noiseless case may not lead to the optimal
classification accuracy. The reason is due to the overlooking of the fact that
feature dimensions have different sensitivity towards aggregation errors and
are thus of different importance levels for classification. This issue is
addressed in this work via a new task-oriented AirComp scheme designed by
directly maximizing the derived discriminant gain
Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference
Edge-device co-inference refers to deploying well-trained artificial
intelligent (AI) models at the network edge under the cooperation of devices
and edge servers for providing ambient intelligent services. For enhancing the
utilization of limited network resources in edge-device co-inference tasks from
a systematic view, we propose a task-oriented scheme of integrated sensing,
computation and communication (ISCC) in this work. In this system, all devices
sense a target from the same wide view to obtain homogeneous noise-corrupted
sensory data, from which the local feature vectors are extracted. All local
feature vectors are aggregated at the server using over-the-air computation
(AirComp) in a broadband channel with the
orthogonal-frequency-division-multiplexing technique for suppressing the
sensing and channel noise. The aggregated denoised global feature vector is
further input to a server-side AI model for completing the downstream inference
task. A novel task-oriented design criterion, called maximum minimum pair-wise
discriminant gain, is adopted for classification tasks. It extends the distance
of the closest class pair in the feature space, leading to a balanced and
enhanced inference accuracy. Under this criterion, a problem of joint sensing
power assignment, transmit precoding and receive beamforming is formulated. The
challenge lies in three aspects: the coupling between sensing and AirComp, the
joint optimization of all feature dimensions' AirComp aggregation over a
broadband channel, and the complicated form of the maximum minimum pair-wise
discriminant gain. To solve this problem, a task-oriented ISCC scheme with
AirComp is proposed. Experiments based on a human motion recognition task are
conducted to verify the advantages of the proposed scheme over the existing
scheme and a baseline.Comment: This work was accepted by IEEE Transactions on Wireless
Communications on Aug. 12, 202