Communication channel

About: Communication channel is a research topic. Over the lifetime, 137411 publications have been published within this topic receiving 1715077 citations. The topic is also known as: communication channel & communications channel.

Completely Stale Transmitter Channel State Information is Still Very Useful

Abstract: Transmitter channel state information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser multiple-input multiple-output (MIMO) and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current channel state and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we show that in an MIMO broadcast channel with transmit antennas and receivers each with 1 receive antenna, K/1+1/2+···+1/K (>;1) degrees of freedom is achievable even when the fed back channel state is completely independent of the current channel state. Moreover, we establish that if all receivers have independent and identically distributed channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current channel state. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless channels.

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Abstract: In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, wide availability, and relative ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail, relative to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios.

Link performance models for system level simulations of broadband radio access systems

Abstract: This paper gives an overview of some so-called link performance models used in system level simulations to determine the link packet error rate (PER) at reduced complexity. A subset of link performance models is evaluated in terms of PER prediction accuracy focusing on a single receive and transmit antenna OFDM link with different coding options and channel characteristics. The results demonstrate that a mutual-information based metric which accounts for the modulation alphabet is preferable in the considered cases and, furthermore, applicable to the large class of MIMO-OFDM transmission techniques with linear pre- and post-processing

Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

Abstract: This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat frequency-selective fading in wireless channels. In this article, we take advantage of deep learning in handling wireless OFDM channels in an end-to-end approach. Different from existing OFDM receivers that first estimate CSI explicitly and then detect/recover the transmitted symbols with the estimated CSI, our deep learning based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from the simulation based on the channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach has the ability to address channel distortions and detect the transmitted symbols with performance comparable to minimum mean-square error (MMSE) estimator. Furthermore, the deep learning based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is presented. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortions and interferences.

Microfluidic devices incorporating improved channel geometries

Abstract: The present invention generally provides microfluidic devices which incorporate improved channel and reservoir geometries, as well as methods of using these devices in the analysis, preparation, or other manipulation of fluid borne materials, to achieve higher throughputs of such materials through these devices, with lower cost, material and/or space requirements.