Spectrum Pricing for Cognitive Radio

This thesis examines how the price paid by the end users via an auction model can be used in regulating and controlling the admission process given a dynamic spectrum access and a heterogeneous small cell network. The performance of the system is judged by the energy consumed, the system throughput and the delay. A first price auction model with a reserve price is designed to take into consideration the signal to noise ratio of the users by introducing a novel tax and subsidy scheme called the green payments. Furthermore, the use of multiple bidding process and an admittance threshold, known as the probability of being among the highest bidders, helps in further reducing the energy consumed and improves the system throughput. A utility function is also found useful in determining the satisfaction of the users and in formulating a theoretical model for the admission process. Bid learning performance using Linear Reinforcement learning, Q learning, and Bayesian learning is compared and the results show that Bayesian learning converges faster because it incorporates prior information. It is shown that incorporating a price based utility function into the punishment or the reward weighting factor can help the learning process to converge at the optimal bidding price. A game model is formulated to allow all users in the system to learn depending on their priority. This enables users to learn different parameters such as the best offered bid price and the appropriate time to participate in the auction process. Results show that provided all the users take part in the learning process, a Nash Equilibrium can be established. The energy and the delay associated with the auction process are also further reduced when all the users are learning the different parameters

ENERGY EFFICENT AUCTION BASED DYNAMIC SPECTRUM ACCESS NETWORK

A framework is proposed which is aimed at increasing the much needed revenue of the wireless service providers. The model uses the price paid by the wireless users to control the amount of energy consumed and the admission process based on a dynamic spectrum access network. The scheme is based on using a first price auction process with a reserve price to allocate the radio spectrum. It allows an opportunistic access to the white space in a manner that would protect the primary users in the system. The concept of green payment is used to penalise users who require high transmit power and subsidies those who require low transmit power. This work shows that with the proposed green payment in combination with the knowledge of the reserve price, the energy consumed and the delay in an auction based dynamic spectrum access network can be reduced

Threats and challenges of smart grids deployments - a developing nations’ perspective

Considerable efforts in huge investments are being made to achieve a resilient Smart Grids (SGs) deployment for the improvement of power delivery scheme. Unsurprisingly, many developing nations are making slow progress to the achievement of this feat, which is set to revolutionize the power industry, own to several deployment and security issues. Studying these threats and challenges from both technical and non-technical view, this paper presents a highlight and assessment of each of the identified challenges. These challenges are assessed by exposing the security and deployment related threats while suggesting ways of tackling these challenges with prominence to developing nations. Although, a brief highlight, this review will help key actors in the region to identify the related challenges and it’s a guide to sustainable deployments of SGs in developing nations

Игровая интуитивная спектральная модель аукциона и изучение процесса подачи заявок на когнитивные радиосистемы

An auction based bid learning process for cognitive radio networks, where the users and the service providers are learning about each other to maximise each other’s utility is examined. A game model is formulated to allow players to learn depending on their priority. This enables users to learn different parameters such as the best offered bid price and the appropriate time to participate in the auction process. The performance of the system is examined based on the developed utility function. The results show that the blocking probability, utility function and the energy consumed is better with the learning users when compared to the non-learning users. Results also show that provided learning is taking place in the system, Nash Equilibrium can be establishedИзучается процесс обучения на основе аукциона для когнитивных радиосетей, где пользователи и поставщики услуг узнают друг о друге, чтобы максимизировать полезность друг друга. Игровая модель сформулирована так, чтобы позволить игрокам учиться в зависимости от их приоритета. Это дает возможность пользователям изучать различные параметры, такие как наилучшая цена предложения и подходящее время для участия в аукционном процессе. Производительность системы проверяется на основе разработанной функции полезности. Результаты показывают, что вероятность блокировки, функция полезности и потребляемая энергия лучше у пользователей обучения по сравнению с пользователями, не участвующими в обучении. Результаты также показывают, что при условии, что обучение будет проходить в системе, может быть установлено равновесие Нэш

Игровая интуитивная спектральная модель аукциона и изучение процесса подачи заявок на когнитивные радиосистемы

An auction based bid learning process for cognitive radio networks, where the users and the service providers are learning about each other to maximise each other’s utility is examined. A game model is formulated to allow players to learn depending on their priority. This enables users to learn different parameters such as the best offered bid price and the appropriate time to participate in the auction process. The performance of the system is examined based on the developed utility function. The results show that the blocking probability, utility function and the energy consumed is better with the learning users when compared to the non-learning users. Results also show that provided learning is taking place in the system, Nash Equilibrium can be establishedИзучается процесс обучения на основе аукциона для когнитивных радиосетей, где пользователи и поставщики услуг узнают друг о друге, чтобы максимизировать полезность друг друга. Игровая модель сформулирована так, чтобы позволить игрокам учиться в зависимости от их приоритета. Это дает возможность пользователям изучать различные параметры, такие как наилучшая цена предложения и подходящее время для участия в аукционном процессе. Производительность системы проверяется на основе разработанной функции полезности. Результаты показывают, что вероятность блокировки, функция полезности и потребляемая энергия лучше у пользователей обучения по сравнению с пользователями, не участвующими в обучении. Результаты также показывают, что при условии, что обучение будет проходить в системе, может быть установлено равновесие Нэш