49 research outputs found
Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT
As a key technology for future wireless networks, massive multiple-input
multiple-output (MIMO) can significantly improve the energy efficiency (EE) and
spectral efficiency (SE), and the performance is highly dependant on the degree
of the available channel state information (CSI). While most existing works on
massive MIMO focused on the case where the instantaneous CSI at the transmitter
(CSIT) is available, it is usually not an easy task to obtain precise
instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell
massive MIMO downlink transmission with statistical CSIT. To this end, we aim
to optimize the system resource efficiency (RE), which is capable of striking
an EE-SE balance. We first figure out a closed-form solution for the
eigenvectors of the optimal transmit covariance matrices of different user
terminals, which indicates that beam domain is in favor of performing RE
optimal transmission in massive MIMO downlink. Based on this insight, the RE
optimization precoding design is reduced to a real-valued power allocation
problem. Exploiting the techniques of sequential optimization and random matrix
theory, we further propose a low-complexity suboptimal two-layer
water-filling-structured power allocation algorithm. Numerical results
illustrate the effectiveness and near-optimal performance of the proposed
statistical CSI aided RE optimization approach.Comment: Typos corrected. 14 pages, 7 figures. Accepted for publication on
IEEE Transactions on Signal Processing. arXiv admin note: text overlap with
arXiv:2002.0488
Secure Massive MIMO Communication with Low-resolution DACs
In this paper, we investigate secure transmission in a massive multiple-input
multiple-output (MIMO) system adopting low-resolution digital-to-analog
converters (DACs). Artificial noise (AN) is deliberately transmitted
simultaneously with the confidential signals to degrade the eavesdropper's
channel quality. By applying the Bussgang theorem, a DAC quantization model is
developed which facilitates the analysis of the asymptotic achievable secrecy
rate. Interestingly, for a fixed power allocation factor , low-resolution
DACs typically result in a secrecy rate loss, but in certain cases they provide
superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically,
we derive a closed-form SNR threshold which determines whether low-resolution
or high-resolution DACs are preferable for improving the secrecy rate.
Furthermore, a closed-form expression for the optimal is derived. With
AN generated in the null-space of the user channel and the optimal ,
low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for
random AN with the optimal , the secrecy rate is hardly affected by the
DAC resolution because the negative impact of the quantization noise can be
compensated for by reducing the AN power. All the derived analytical results
are verified by numerical simulations.Comment: 14 pages, 10 figure
PHYSICAL LAYER SECURITY IN THE 5G HETEROGENEOUS WIRELESS SYSTEM WITH IMPERFECT CSI
5G is expected to serve completely heterogeneous scenarios where devices with low or high software and hardware complexity will coexist. This entails a security challenge because low complexity devices such as IoT sensors must still have secrecy in their communications. This project proposes tools to maximize the secrecy rate in a scenario with legitimate users and eavesdroppers considering: i) the limitation that low complexity users have in computational power and ii) the eavesdroppers? unwillingness to provide their channel state information to the base station. The tools have been designed based on the physical layer security field and solve the resource allocation from two different approaches that are suitable in different use cases: i) using convex optimization theory or ii) using classification neural networks. Results show that, while the convex approach provides the best secrecy performance, the learning approach is a good alternative for dynamic scenarios or when wanting to save transmitting power