A backhaul adaptation scheme for IAB networks using deep reinforcement learning with recursive discrete choice model

Challenges such as backhaul availability and backhaul scalability have continued to outweigh the progress of integrated access and backhaul (IAB) networks that enable multi-hop backhauling in 5G networks. These challenges, which are predominant in poor wireless channel conditions such as foliage, may lead to high energy consumption and packet losses. It is essential that the IAB topology enables efficient traffic flow by minimizing congestion and increasing robustness to backhaul failure. This article proposes a backhaul adaptation scheme that is controlled by the load on the access side of the network. The routing problem is formulated as a constrained Markov decision process and solved using a dual decomposition approach due to the existence of explicit and implicit constraints. A deep reinforcement learning (DRL) strategy that takes advantage of a recursive discrete choice model (RDCM) was proposed and implemented in a knowledge-defined networking architecture of an IAB network. The incorporation of the RDCM was shown to improve robustness to backhaul failure in IAB networks. The performance of the proposed algorithm was compared to that of conventional DRL, i.e., without RDCM, and generative model-based learning (GMBL) algorithms. The simulation results of the proposed approach reveal risk perception by introducing certain biases on alternative choices and the results showed that the proposed algorithm provides better throughput and delay performance over the two baselines.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) and the University of Pretoria.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Electrical, Electronic and Computer Engineerin

GNSS-LTE/LTE-A interference mitigation : the adjacent channel rejection ratio approach

M.Ing.The increase of interest in the development of radio communications, both terrestrial and satellite is reaching far and beyond the most optimistic expectations. There has been an accelerated emergence of newer technologies, all claiming highly coveted radio frequency spectrum resources. With the push for the development of location based services, utilizing satellite com- communications for military purposes and later for civilian use; there has been a parallel development in terrestrial communications technology making it possible to implement cost efficient reliable user systems for voice and data services ..

A model-based deep learning approach to spectrum management in distributed cognitive radio networks

The acceleration towards the fifth generation (5G) and beyond will see the internet of things (IoT) being the primary strategy of deployment, and wireless networks will become more distributed and autonomous. Furthermore, network users will demand delivery of multimedia content to various network devices in dissimilar contexts. Thus, the cognitive radio (CR) paradigm requires some improvements for it to rigorously resolve quality of service (QoS) and quality of experience (QoE) in an energy-efficient manner before the 5G network is commissioned. Therefore, solving the distributed RA problem through thorough and in-depth investigations into the essentials and intricacies of energy-efficient RA by integrating artificial intelligence (AI) and signal processing (SP) strategies is a requisite. Having identified this knowledge gap and several limiting factors, this thesis focuses on two fronts to maximize the distributed opportunistic usage of the wireless spectrum with enhanced energy efficiency. The first contribution of this study provides a solution for missing spectrum sensing information to improve spectrum occupancy measurements in distributed CRNs. This is a problem commonly encountered in distributed cooperative spectrum sensing scenarios, where secondary users (SUs) are faced with the missing spectrum sensing data (SSD) problem owing to several impairments such as (i) the use of specific collaborative spectrum sensing schemes and (ii) imperfect reporting channel conditions. This results in the SSD contributed by SUs having gaps of missing entries. This degrades the performance of spectrum sensing algorithms, especially when the amount of missing SSD is quite large. Therefore, spectrum occupancy reconstruction is proposed as a solution to deal with missing values through missing value imputation. This is a deep learning (DL)-based strategy that uses deep belief networks (DBNs) composed of restricted Boltzmann machines (RBMs) to capture the feature of the input space of the spectrum occupancy data from a Markov random field (MRF). Link energy functions from the Ising models and the Metropolis-Hastings algorithm are used to pre-train the RBM to obtain a spectrum occupancy data matrix. The size of training samples and learning rates are decided using Gibbs sampling during the training process and missing spectrum values are learned using a scaled stochastic gradient descent (SGD). The simulation results obtained indicate that spectrum occupancy reconstruction problems can be solved better using the SGD algorithm because it takes advantage of correlations in multiple dimensions better than singular value decomposition (SVD) in matrix factorization. The second contribution provides a solution for energy saving and QoS provisioning for SUs with heterogeneous traffic, which is a problem exacerbated by the increased demand for multimedia services. This necessitates for the establishment of newer power control strategies for multimedia sources, where energy saving and QoS provisioning are viewed from the job arrival rate instead of the packet arrival rate perspective. Here, the model dynamics are formulated as a continuous-time non-linear input affine system which combines opportunistic transmission and opportunistic computing to obtain resource consumption efficiency. By treating the base station (BS) as a hybrid switching system, a weighted cost function is obtained and solved using model-based reinforcement learning (RL), which initiates a single look-ahead for optimum operating states. Then, using the resource consumption efficiency, a DL-based predictive control scheme was realized with control actions that drive a stacked auto-encoder (SAE) that plays dynamic games on queues and performs effective trade-offs between QoS provisioning and energy saving. The simulation results obtained indicate that the processor sharing (PS) scheduling scheme achieves better energy saving than first-come-first-served (FCFS) at higher job arrival rates. The last contribution deals with the problem of distributed RA in energy-constrained CRN environments, with the objective of ensuring user satisfaction in terms of QoE and QoS in an energy-efficient manner. QoE evaluation is performed using docitive techniques and the results obtained indicate that transfer-learning through docitive approaches achieves better convergence rates and superior spectral efficiency compared to the traditional cognitive approaches. Then, a computationally efficient optimization technique that handles the energy efficiency learning model is achieved using factored Markov decision processes (FMDPs), which provides a solvable framework for energy minimization. This completes the hierarchical deep RL (DRL) with a deep Q-network (DQN) formulation that learns energy consumption subject to latency constraints. The results obtained show that the DQN approach with experience replay achieves better QoS performance compared to the traditional RL in terms of minimizing buffer delays and power consumption.Thesis (PhD (Electronic Engineering))--University of pretoria, 2020.Association of Commonwealth UniversitiesElectrical, Electronic and Computer EngineeringPhD (Electronic Engineering)Unrestricte

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

One of the major challenges facing the realization of cognitive radios (CRs) in future mobile and wireless communications is the issue of high energy consumption. Since future network infrastructure will host real-time services requiring immediate satisfaction, the issue of high energy consumption will hinder the full realization of CRs. This means that to offer the required quality of service (QoS) in an energy-efficient manner, resource management strategies need to allow for effective trade-offs between QoS provisioning and energy saving. To address this issue, this paper focuses on single base station (BS) management, where resource consumption efficiency is obtained by solving a dynamic resource allocation (RA) problem using bipartite matching. A deep learning (DL) predictive control scheme is used to predict the traffic load for better energy saving using a stacked auto-encoder (SAE). Considered here was a base station (BS) processor with both processor sharing (PS) and first-come-first-served (FCFS) sharing disciplines under quite general assumptions about the arrival and service processes. The workload arrivals are defined by a Markovian arrival process while the service is general. The possible impatience of customers is taken into account in terms of the required delays. In this way, the BS processor is treated as a hybrid switching system that chooses a better packet scheduling scheme between mean slowdown (MS) FCFS and MS PS. The simulation results presented in this paper indicate that the proposed predictive control scheme achieves better energy saving as the traffic load increases, and that the processing of workload using MS PS achieves substantially superior energy saving compared to MS FCFS.The Association of Commonwealth Universities (ACU) and the Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://jcn.or.kr/htmlam2020Electrical, Electronic and Computer Engineerin

Spectrum occupancy reconstruction in distributed cognitive radio networks using deep learning

Spectrum occupancy reconstruction is an important issue often encountered in collaborative spectrum sensing in distributed cognitive radio networks (CRNs). This issue arises when the spectrum sensing data that are collaborated by secondary users have gaps of missing entries. Many data imputation techniques, such as matrix completion techniques, have shown great promise in dealing with missing spectrum sensing observations by reconstructing the spectrum occupancy data matrix. However, matrix completion approaches achieve lower reconstruction resolution due to the use of standard singular value decomposition (SVD), which is designed for more general matrices. In this paper, we consider the problem of spectrum occupancy reconstruction where the spectrum sensing results across the CRN are represented as a plenary grid on a Markov random eld. We formulate the problem as a magnetic excitation state recovery problem, and the stochastic gradient descent (SGD) method is applied to solve the matrix factorization. SGD is able to learn and impute the missing values with a low reconstruction error compared with SVD. The graphical and numerical results show that the SGD algorithm competes favorably SVD in the matrix factorization by taking advantage of correlations in multiple dimensions.The Association of Commonwealth Universities under Grant FE-2015-26, and in part by the Sentech Chair in Broadband Wireless Multimedia Communications.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2020Electrical, Electronic and Computer Engineerin

Optimization and learning in energy efficient resource allocation for cognitive radio networks

The recent surge in real-time traffic has led to serious energy efficiency concerns in cognitive radio networks (CRNs). Network infrastructure such as base stations (BSs) host different service classes of traffic with stringent quality-of-service (QoS) requirements that need to be satisfied. Thus, maintaining the desired QoS in an energy efficient manner requires a good trade-off between QoS and energy saving. To deal with this problem, this paper proposes a deep learning-based computational-resource-aware energy consumption technique. The proposed scheme uses an exploration technique of the systems' state-space and traffic load prediction to come up with a better trade-off between QoS and energy saving. The simulation results show that the proposed exploration technique performs 9% better than the traditional random tree technique even when the provisioning priority shifts away from energy saving towards QoS, i.e., α ≥ 0.5.Paper presented at the 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 28 April-1 May 2019, Kuala Lumpur, Malaysia.https://ieeexplore.ieee.org/xpl/conhome/8738891/proceedinghj2020Electrical, Electronic and Computer Engineerin