Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems

In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA system (named as secondary system) dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed for the secondary system, where relay stations are employed for minimizing the interference to the users in the primary network and achieving fairness for cell-edge users. Based on this architecture, an asymptotically optimal solution is derived for jointly controlling data rates, transmission power, and subchannel allocation to optimize the average weighted sum goodput where the proportional fair scheduling (PFS) is included as a special case. This solution supports decentralized implementation, requires small communication overhead, and is robust against imperfect channel state information at the transmitter (CSIT) and sensing measurement. The proposed solution achieves significant throughput gains and better user-fairness compared with the existing designs. Finally, we derived a simple and asymptotically optimal scheduling solution as well as the associated closed-form performance under the proportional fair scheduling for a large number of users. The system throughput is shown to be $\mathcal{O}\left(N(1-q_p)(1-q_p^N)\ln\ln K_c\right)$, where $K_c$ is the number of users in one cluster, $N$ is the number of subchannels and $q_p$ is the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSIN

A Mechanism Design Approach to Decentralized Resource Allocation in Wireless and Large-Scale Networks: Realization and Implementation.

In this thesis we present a mechanism design approach to decentralized resource allocation in wireless and large-scale networks. For wireless networks we study the problem of power allocation where each user's transmissions create interference to all network users, and each user has only partial information about the network. We investigate the problem under two scenarios; the realization theory scenario and the implementation theory scenario. Under the realization theory scenario, we formu- late the power allocation problem as an allocation problem with externalities, and develop a decentralized optimal power allocation algorithm that (i) preserves the private information of the users; and (ii) converges to the optimal centralized power allocation. Under the implementation theory scenario, we formulate the power allo- cation problem as a public good allocation problem, and develop a game form that (i) implements in Nash equilibria the optimal allocations of corresponding centralized power allocation problem; (ii) is individually rational; and (iii) results in budget bal- ance at all Nash equilibria and off equilibria. Later we generalize the wireless network model to study resource allocation in large-scale networks where the actions of each user affect the utilities of an arbitrary subset of network users. This generalization is motivated by several applications including power allocation in large-scale wireless networks where the transmissions of each user create interference to only a subset of network users. We develop a formal model to study resource allocation problems in large-scale networks with above characteristics. We formulate two resource allocation prob- lems for the large-scale network model; one for the realization theory scenario, and the other for the implementation theory scenario. For the realization problem we develop a decentralized resource allocation algorithm using the principles of mecha- nism design that (i) preserves the private information of the users; and (ii) converges to the optimal centralized resource allocation. For the implementation problem we develop a game form that (i) implements in Nash equilibria the optimal allocations of corresponding centralized resource allocation problem; (ii) is individually rational; and (iii) results in budget balance at all Nash equilibria and off equilibria.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64734/1/svandana_1.pd

Network association strategies for an energy harvesting aided super-wifi network relying on measured solar activity

The super-WiFi network concept has been proposed for nationwide Internet access in the United States. However, the traditional mains power supply is not necessarily ubiquitous in this large-scale wireless network. Furthermore, the non-uniform geographic distribution of both the based-stations and the tele-traffic requires carefully considered user association. Relying on the rapidly developing energy harvesting techniques, we focus our attention on the sophisticated access point (AP) selection strategies conceived for the energy harvesting aided super-WiFi network. Explicitly, we propose a solar radiation model relying on the historical solar activity observation data provided by the University of Queensland, followed by a beneficial radiation parameter estimation method. Furthermore, we formulate both a Markov decision process (MDP) as well as a partially observable MDP (POMDP) for supporting the users’ decisions on beneficially selecting APs. Moreover, we conceive iterative algorithms for implementing our MDP and POMDP-based AP-selection, respectively. Finally, our performance results are benchmarked against a range of traditional decision-making algorithms

Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends

Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios

A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria

Spectrum Buyouts:A Mechanism to Open Spectrum(revised December 2003)

TAlthough the current shortage of radio spectrum is usually attributed to the scarcity of spectrum, it is due to the inefficiency of legacy radio technologies and old systems of spectrum management. Regulatory reforms are being proposed to assign exclusive rights to spectrum, but such "market-oriented" allocation would be harmful because the spectrum is not a property but a protocol by which information is carried. New packet radio technologies enable efficient communications by sharing a wide band without licenses. However, it is difficult to relocate spectrum by persuading incumbents to give back their spectrum. Therefore we propose reverse auctions by which the government buys back spectrum from incumbents as an optional mechanism for spectrum relocation. The equilibrium price of this reverse auction will be much cheaper than that of ordinary spectrum auctions, because the former price will be close to the value of the band that is used least efficiently if the auction is competitive.

ICT infrastructure supporting smart local energy systems: a review

Smart local energy systems (SLES) have been reported in the past decade, which are associated with diverse energy carriers, components and objectives. This paper provides a comprehensive review of information and communications technology (ICT) infrastructure of SLES. A systematic survey of existing research work and industrial projects was provided to highlight, categorise and analyse the ICT infrastructure, which lays the foundation for the successful functioning of SLES. First of all, various SLES measurements are described and categorised based on the energy carriers and technologies. Then, communications infrastructure for SLES is described with communications technologies summarised. Moreover, the ICT infrastructures for SLES are categorised and summarised based on their objectives and technologies. Finally, the challenges and recommendations are presented. The findings from this paper are intended to serve as a convenient reference for developing future SLES

Valuing Spectrum Allocations

Observing trends in which Wi-Fi and Bluetooth have become widely popular, some argue that unlicensed allocations hosting such wireless technologies are increasingly valuable and that administrative spectrum allocations should shift accordingly. We challenge that policy conclusion. A core issue is that the social value of a given spectrum allocation is widely assumed to equal the gains of the applications it is likely to host. This thinking is faulty, as vividly seen in what we deem the Broadcast TV Spectrum Valuation Fallacy – the idea that because wireless video, or broadcast network programs are popular, TV channels are efficiently defined. This approach has been appropriately rejected, in key instances, by spectrum regulators, but is similarly applied in other instances regarding unlicensed allocations. While traditional allocations have garnered widespread criticism for imposing rigid barriers tending to block innovation, and flexible-use spectrum access rights have gained favor, the regulatory methods used to allocate (or reallocate) bandwidth remain embedded in a “command and control” process. Reconfiguring spectrum usage to enable emerging wireless markets often requires lengthy, costly rule makings. The expense of this administrative overhead is generally omitted from spectrum allocation policy analysis. Yet, it constitutes an essential component of the consumer welfare analysis. We propose a more fulsome policy approach, one that includes not only the appropriate measures of marginal value and opportunity cost for rival allocations, but incorporates transaction costs. Instead of regulators attempting to guess how much bandwidth should be allocated to various types of licensed and unlicensed services – and imposing different rules within and across these allocations – a more generic approach is called for. By better enabling spontaneous adjustments to changing consumer demands and technological innovation, spectrum allocations can be more efficiently brought into their most valuable employments