Stochastic Geometric Coverage Analysis in mmWave Cellular Networks With Realistic Channel and Antenna Radiation Models

Abstract

Millimeter-wave (mmWave) bands will play an important role in 5G wireless systems. The system performance can be assessed by using models from stochastic geometry that cater for the directivity in the desired signal transmissions as well as the interference, and by calculating the signal-to-interferenceplus-noise ratio (SINR) coverage. Nonetheless, the accuracy of the existing coverage expressions derived through stochastic geometry may be questioned, as it is not clear whether they capture the impact of the detailed mmWave channel and antenna features. In this study, we propose an SINR coverage analysis framework that includes realistic channel model and antenna element radiation patterns. We introduce and estimate two parameters, aligned gain and misaligned gain, associated with the desired signal beam and the interfering signal beam, respectively. The distributions of these gains are used to determine the distribution of the SINR which is compared with the corresponding SINR coverage calculated via system-level simulations. The results show that both aligned and misaligned gains can be modeled as exponentiallogarithmically distributed random variables with the highest accuracy, and can further be approximated as exponentially distributed random variables with reasonable accuracy. These approximations can be used as a tool to evaluate the systemlevel performance of various 5G connectivity scenarios in the mmWave band.

Wireless Network Intelligence at the Edge

“…Toward this vision, at the physical layer, an E2E communication framework was studied under unknown channels using an autoencoder (AE) [19], [20], recurrent NN (RNN) [21], and generative adversarial network (GAN) [22]. To overcome mmWave's link sensitivity to blockage [23], a GAN-aided long-term channel estimation [24] and a reinforcement learning (RL)-based beam alignment technique [25] were proposed. At the network layer, an RNN-aided caching solution was proposed in [26], and an unsupervised clustering algorithm was used with real user traffic patterns.…”

Performance of Radar and Communication Networks Coexisting in Shared Spectrum Bands

“…However, despite the conceptually simple definition in (1), no closed-form solution for I is known in the considered setting with dominant line-of-sight components (i.e. no fading, and α 2), and existing integral expressions are often involved and possibly difficult to be evaluated numerically [19]- [21]. In view of this, we characterise the coexistence performance of communications devices by means of detailed network simulations in Sec.…”

Wireless Edge Computing With Latency and Reliability Guarantees

“…Then, beam tracking is performed to adapt the beamforming, e.g., due to MUEs' movement leading to transmitter-receiver beam misalignments. Nevertheless, a full new directional channel discovery process will need to be triggered if the signal-to-interference-plus-noise ratio (SINR) drops below a certain threshold due to e.g., blockages and/or interference [28]. As analog BF employs a single RF chain, it is challenging to adjust the beam to channel conditions, leading to some performance loss.…”