Cooperative Feedback for MIMO Interference Channels

Multi-antenna precoding effectively mitigates the interference in wireless networks. However, the precoding efficiency can be significantly degraded by the overhead due to the required feedback of channel state information (CSI). This paper addresses such an issue by proposing a systematic method of designing precoders for the two-user multiple-input-multiple-output (MIMO) interference channels based on finite-rate CSI feedback from receivers to their interferers, called cooperative feedback. Specifically, each precoder is decomposed into inner and outer precoders for nulling interference and improving the data link array gain, respectively. The inner precoders are further designed to suppress residual interference resulting from finite-rate cooperative feedback. To regulate residual interference due to precoder quantization, additional scalar cooperative feedback signals are designed to control transmitters' power using different criteria including applying interference margins, maximizing sum throughput, and minimizing outage probability. Simulation shows that such additional feedback effectively alleviates performance degradation due to quantized precoder feedback.Comment: 5 pages; submitted to IEEE ICC 201

Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex optimization for signal processin

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Analysis of dynamic spectrum leasing for coded Bi-directional communication

In this paper, we aim to present a cooperative relaying based two way wireless communication scheme which can provide both spectral and energy efficiency in future wireless networks. To this end, we propose a novel network coding based Dynamic Spectrum Leasing (DSL) technique in which the cognitive secondary users cooperatively relay the primary data for two-way primary communication. In exchange for the relaying services, the primary grants exclusive access to the secondary users for their own activity. We model the random geometry of the ad hoc secondary users using a Poisson point process. We devise a game theoretic framework for the division of leasing time between the primary cooperation and secondary activity phases. We demonstrate that under these considerations and employing network coding, DSL can improve the number of bits that are successfully transmitted by 54% as compared to un-coded direct two way primary communication. Also the energy costs of the proposed DSL scheme are more than 10 times lower. Employing DSL also enables the cognitive users to get reasonable time for their own transmission after increasing the primary spectral and energy efficiency

Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training dat