Adaptive Precoding and Resource Allocation in Cognitive Radio Networks

In this thesis, we develop efficient resource allocation and adaptive precoding schemes for two scenarios: multiuser MIMO-OFDM and multiuser MIMO based CR networks. In the context of the multiuser MIMO-OFDM CR network, we have developed resource allocation and adaptive precoding schemes for both the downlink (DL) and uplink (UL). The proposed schemes are characterized by both computational and spectral efficiencies. The adaptive precoder operates based on generating degrees of freedom (DoF). The resource allocation has been formulated as a sum-rate maximization problem subject to the upper-limit of total power and interference at primary user constraints. The formulated optimization problem is a mixed integer programming having a combinatorial complexity which is hard to solve, and therefore we separated it into a two-phase procedure to elaborate computational efficiency: Adaptive precoding (DoF assignment) and subcarrier mapping. From the implementation perspective, the resource allocation of the DL is central based processing, but the UL is semi-distributed based. The DL and UL problems are sorted out using the Lagrange multiplier theory which is regarded as an efficient alternative methodology compared to the convex optimization theory. The solution is not only characterized by low-complexity, but also by optimality. Numerical simulations illustrate remarkable spectral and SNR gains provided by the proposed schemes.In dieser Dissertation werden effiziente Ressourcenallokation und adaptive Vorkodierungsverfahren für zwei Szenarios entwickelt: Mehrbenutzer-MIMO-OFDM und Mehrbenutzer-MIMO jeweils basierend auf CR-Netzwerken. Im Bereich der Mehrbenutzer-MIMO-OFDM CR-Netzwerke wurden Verfahren zur Ressourcenallokation und zur adaptiven Vorkodierung jeweils für den Downlink (DL) und den Uplink (UL) entwickelt. Die Ressourcenallokation wurde als Optimierungsproblem formuliert, bei dem die Summenrate maximiert wird, wobei die Gesamtsendeleistung und die Interferenz an den Primärnutzern begrenzt ist. Das formulierte Optimierungsproblem ist ein sogenanntes Mixed-Integer-Programm, dessen kombinatorische Komplexität nur extrem aufwendig lösbar ist. Auf Grund dessen wurde es zur Komplexitätsreduktion in zwei Phasen aufgeteilt: Adaptive Vorkodierung (DoF-Zuordnung) und Subkanalzuordnung. Während die Ressourcenallokation für den DL aus Implementierungssicht ein zentralistischer Prozess ist, kann sie für den UL als semiverteilt eingeordnet werden. Die Aufgabe der zentralen Ressourcenallokation wird gelöst, um die zentrale adaptive Vorkodierung und die Subkanalzuordnung für UL und DL zu verwalten. Die Subkanalzuordnung ist für den DL optimal und effizient gelöst, indem das Problem als konvexes Problem modelliert ist. Für den UL wiederum ist das Problem trotz der Konvexität quasi-optimal gelöst, da in der Problemformulierung eine Begrenzung der Ressourcen pro Benutzer existiert

Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN)