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We study a co-design problem of control, scheduling, and routing over a multi-hop sensor and actuator network (WSANs) subject to energy-saving consideration. We formulate an optimization problem, minimizing a linear combination of the averaged linear quadratic Gaussian (LQG) control performance and the averaged transmission energy consumption. Optimal solutions are derived and their performance is illustrated in a numerical example.

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When a base station (BS) sees desired and interfering
users at different angles, it results in non-overlapping angleof-
arrival (AoA) regions for those users. This is important for
reducing pilot contamination (PC) of massive MIMO systems.
Most state of the art studies utilize a simple non-line-of-sight
(NLoS) one-ring model which assumes sparse support and can
reasonably schedule users with different AoA to minimize PC.
However, it is not confirmed with measurements that the one-ring

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25 Views

Traditional random access schemes are designed based on the aggregate process of user activation, which is created on the basis of independent activations of the users. However, in Machine-Type Communications (MTC), some users are likely to exhibit a high degree of correlation, e.g. because they observe the same physical phenomenon. This paves the way to devise access schemes that combine scheduling and random access, which is the topic of this work. The underlying idea is to schedule highly correlated users in such a way that their transmissions are less likely to result in a collision.

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In this paper, we investigate the resource allocation design for multicarrier (MC) systems employing a solar powered unmanned aerial vehicle (UAV) for providing communication services to multiple downlink users. We study the joint design of the three-dimensional positioning of the UAV and the power and subcarrier allocation for maximization of the system sum throughput. The algorithm design is formulated as a mixed-integer non-convex optimization problem, which requires a prohibitive computational complexity for obtaining the globally optimal solution.

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This paper describes a high-speed optical camera communication (OCC) technique using an LED and a rolling-shutter camera. In the proposed technique, the symbols being transmitted are encoded as time delays of optimally modulated signals derived theoretically. A receiver decodes the symbols by using intensities obtained from four consecutive line sensors of a camera.

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In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution.

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7 Views

Machine-type communication requires rethinking of the structure of short packets due to the coding limitations and the significant role of the control information. In ultra-reliable low-latency communication (URLLC), it is crucial to optimally use the limited degrees of freedom (DoFs) to send data and control information. We consider a URLLC model for short packet transmission with acknowledgement (ACK).

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