RESUMEN
This paper comprehensively investigates the performance of the D2D underlaying cellular networks where D2D communications are operated concurrently with cellular networks provided that the aggregate interference measured on licensed users is strictly guaranteed. In particular, we derive the outage probability (OP), the average rate, and the amount of fading (AoF) of the D2D networks in closed-form expressions under three distinct power allocation schemes, i.e., the path-loss-based, equal, and random allocation schemes. It is noted that the considered networks take into consideration the impact of the intra-D2D networks, the inter-interference from the cellular networks and background noise, thus involving many random variables and leading to a complicated mathematical framework. Moreover, we also reveal the behavior of the OP with respect to the transmit power based on the rigorous mathematical frameworks rather than the computer-based simulation results. The derived framework shows that increasing the transmit power is beneficial for the OP of the D2D users. Regarding the cellular networks, the coverage probability (Pcov) of the cellular users is computed in closed-form expression too. Monte Carlo simulations are given to verify the accuracy of the proposed mathematical frameworks. Our findings illustrate that the power allocation method based on prior path-loss information outperforms the other methods in the average sum rate.
Asunto(s)
Algoritmos , Redes de Comunicación de Computadores , Simulación por Computador , Método de Montecarlo , ProbabilidadRESUMEN
Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.