Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPhysical-Layer Network Coding: An Efficient Technique for Wireless Communications
66
0
0.0
(
0
)
Added by
Chen Pingping
Publication date
2019
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
As a subfield of network coding, physical-layer network coding (PNC) can effectively enhance the throughput of wireless networks by mapping superimposed signals at receiver to other forms of user messages. Over the past twenty years, PNC has received significant research attention and has been widely studied in various communication scenarios, e.g., two-way relay communications (TWRC), nonorthogonal multiple access (NOMA) in 5G networks, random access networks, etc. To ensure network reliability, channel-coded PNC is proposed and related communication techniques are investigated, such as the design of channel code, low-complexity decoding, and cross-layer design. In this article, we briefly review the variants of channel-coded PNC wireless communications with the aim of inspiring future research activities in this area. We also put forth open research problems along with a few selected research directions under PNC-aided frameworks.
rate research
Read More
A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receivers entire information recovery process. We design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is used to improve the adaptability of the network to different input signal lengths. Multiple binary classifiers are used at the final classification layer to achieve multi-bit information stream recovery. We also exploit the DeepReceiver for unified blind reception of multiple modulation and coding schemes (MCSs) by including signal samples of corresponding MCSs in the training set. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under the influence of various factors such as noise, RF impairments, multipath fading, cochannel interference, dynamic environment, and unified reception of multiple MCSs.
This paper investigates noncoherent detection in a two-way relay channel operated with physical layer network coding (PNC), assuming FSK modulation and short-packet transmissions. For noncoherent detection, the detector has access to the magnitude but not the phase of the received signal. For conventional communication in which a receiver receives the signal from a transmitter only, the phase does not affect the magnitude, hence the performance of the noncoherent detector is independent of the phase. PNC, however, is a multiuser system in which a receiver receives signals from multiple transmitters simultaneously. The relative phase of the signals from different transmitters affects the received signal magnitude through constructive-destructive interference. In particular, for good performance, the noncoherent detector in PNC must take into account the influence of the relative phase on the signal magnitude. Building on this observation, this paper delves into the fundamentals of PNC noncoherent detector design. To avoid excessive overhead, we do away from preambles. We show how the relative phase can be deduced directly from the magnitudes of the received data symbols. Numerical results show that our detector performs nearly as well as a fictitious optimal detector that has perfect knowledge of the channel gains and relative phase.
The integration of unmanned aerial vehicles (UAVs) into the terrestrial cellular networks is envisioned as one key technology for next-generation wireless communications. In this work, we consider the physical layer security of the communications links in the millimeter-wave (mmWave) spectrum which are maintained by UAVs functioning as base stations (BS). In particular, we propose a new precoding strategy which incorporates the channel state information (CSI) of the eavesdropper (Eve) compromising link security. We show that our proposed precoder strategy eliminates any need for artificial noise (AN) transmission in underloaded scenarios (fewer users than number of antennas). In addition, we demonstrate that our nonlinear precoding scheme provides promising secrecy-rate performance even for overloaded scenarios at the expense of transmitting low-power AN.
Leveraging recent progress in physical-layer network coding we propose a new approach to random access: When packets collide, it is possible to recover a linear combination of the packets at the receiver. Over many rounds of transmission, the receiver can thus obtain many linear combinations and eventually recover all original packets. This is by contrast to slotted ALOHA where packet collisions lead to complete erasures. The throughput of the proposed strategy is derived and shown to be significantly superior to the best known strategies, including multipacket reception.
In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in a desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce the computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
