Opportunistic Allocation Research Articles

Online Allocation with Replenishable Budgets: Worst Case and Beyond

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T^*\geq1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.

Online Allocation with Replenishable Budgets: Worst Case and Beyond

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T* ≥ 1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.

Learning Aided Auctioning-Based Spectrum Access System in a Wireless Optical Network

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution/5G based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.

RETRACTED ARTICLE: Throughput maximization of multichannel allocation mechanism under interference constraint for hybrid overlay/underlay cognitive radio networks with energy harvesting

By harvesting energy from ambient radio frequency (RF) signals, significant progress has been achieved in wireless networks self-maintaining their life cycles. Motivated by this and improved spectrum reuse by combined use of overlay/underlay modes of cognitive radio networks (CRNs), this paper proposes a novel multi-channel (m-channel) allocation performance maximization algorithm for low-power mobiles. CRNs, called secondary transmitters (STs), can harvest energy from RF signals by nearby active primary transmitters (PTs). In the proposed scheme, PTs and STs are distributed as independent homogeneous Poisson point processes and contact their receivers at fixed distances. Each PT contains a guard zone to protect its intended receiver from ST interference, and provides RF energy to STs located in its harvesting zone. Prioritization of STs during opportunistic allocation of channels is critical as properties like energy level and harvesting capability improve channel distribution performance. A novel metric is proposed that prioritizes STs based on initial energy levels, harvesting capability, and number of channels through which they can transmit. For comparison, three algorithms were considered: a greedy mechanism for m-channel allocation of hybrid CRNs without harvesting, the proposed m-channel allocation schemes based on maximum independent sets (MIS), and the proposed metric of hybrid CRNs with harvesting capability. The simulations show that the proposed m-channel allocation method based on MIS outperforms the greedy algorithm. The proposed m-channel allocation using the proposed metric on hybrid CRNs with energy harvesting ability produced the best performance of the three methods, proving the superiority of the proposed algorithm.

Throughput Optimization of Multichannel Allocation Mechanism under Interference Constraint for Hybrid Overlay/underlay Cognitive Radio Networks with Energy Harvesting

By harvesting energy from ambient radio frequency (RF) signals, significant progress has been achieved in wireless networks self-maintaining their life cycles. Motivated by this and improved spectrum reuse by combined use of overlay/underlay modes of cognitive radio networks (CRNs), this paper proposes a novel multi-channel (m-channel) allocation performance maximization algorithm for low-power mobiles. CRNs, called secondary transmitters (STs), can harvest energy from RF signals by nearby active primary transmitters (PTs). In the proposed scheme, PTs and STs are distributed as independent homogeneous Poisson point processes and contact their receivers at fixed distances. Each PT contains a guard zone to protect its intended receiver from ST interference, and provides RF energy to STs located in its harvesting zone. Prioritization of STs during opportunistic allocation of channels is critical as properties like energy level and harvesting capability improve channel distribution performance. A novel metric is proposed that prioritizes STs based on initial energy levels, harvesting capability, and number of channels through which they can transmit. For comparison, three algorithms were considered: a greedy mechanism for m-channel allocation of hybrid CRNs without harvesting, the proposed m-channel allocation schemes based on maximum independent sets (MIS), and the proposed metric of hybrid CRNs with harvesting capability. The simulations show that the proposed m-channel allocation method based on MIS outperforms the greedy algorithm. The proposed m-channel allocation using the proposed metric on hybrid CRNs with energy harvesting ability produced the best performance of the three methods, proving the superiority of the proposed algorithm.

Designing a MAC Algorithm for Equitable Spectrum Allocation in Cognitive Radio Wireless Networks

Spectrum sharing is one of the most important stages in cognitive radio wireless networks, responsible for the opportunistic allocation of free channels to unlicensed users (SUs) to be utilized in ...

Effect of opportunistic scheduling on the quality of service perceived by the users in OFDMA cellular networks

Our objective is to analyze the impact of fading and opportunistic scheduling on the quality of service perceived by the users in an orthogonal frequency-division multiple access cellular network. To this end, assuming Markovian arrivals and departures of customers that transmit some given data volumes, as well as some temporal channel variability (fading), we study the mean throughput that the network offers to users in the long run of the system. Explicit formulas are obtained in the case of allocation policies, which may or may not take advantage of the fading, called respectively opportunistic and non-opportunistic. The main practical results of the present work are the following: Firstly, we evaluate for the non-opportunistic allocation the degradation due to fading compared to additive white Gaussian noise (AWGN; that is, a decrease of at least 13% of the throughput). Secondly, we evaluate the gain induced by the opportunistic allocation. In particular, when the traffic demand per cell exceeds some value (about 2 Mbits/s in our numerical example), the gain induced by opportunism compensates the degradation induced by fading compared to AWGN.

Rate Adaptive Resource Allocation and Utility-Based Packet Scheduling in Multicarrier Systems

There are several approaches for Radio Resource Management (RRM) in multicarrier cellular systems. This work analyzes and compares two of them: rate-adaptive resource allocation (sub-carriers and power) based on instantaneous data rates, and utility-based packet scheduling based on average data rates. A fundamental RRM problem in wireless cellular networks was chosen as a background to evaluate the aforementioned approaches: the trade-off between system spectral efficiency and fairness among the users when opportunistic allocation is used. Extensive system-level simulations were performed and important network metrics such as total cell throughput, mean user throughput, system fairness index and user satisfaction were assessed. It was concluded from the simulation results that it is possible to achieve an efficient trade-off between resource efficiency and fairness using any of the two RRM approaches. However, utility-based packet scheduling algorithms based on average data rates have the advantage of presenting higher user satisfaction with less computational complexity.

Flow-level performance of proportional fairness with hierarchical modulation in OFDMA-based networks

Proportional Fairness (PF) is known to achieve a good trade-off between efficiency and fairness by an opportunistic allocation of resources to users with good radio conditions, without sacrificing fairness towards the other users of the system who have worse radio conditions. On the other hand, Hierarchical Modulation (HM) is a means to increase the spectral efficiency of a given system by superposing an additional information stream over a basic one, by means of embedded constellations. This, in turn, results in a high resource utilization and hence overall system throughput. In this work, we model the system performance, obtained by jointly using PF with HM, for a realistic dynamic setting where users come to the system at random time epochs and leave it after a finite duration, upon the completion of their data transfers. We show that, in the presence of HM, a simple cyclic service, such as round robin, yields a better performance than PF, along with less complex implementation.

Survey of resource allocation methods for distributed manufacturing systems

Distributed manufacturing systems are more flexible in their arrangement and use of their resources. This permits a more dynamic and opportunistic allocation of manufacturing resources to work elements than is possible with today's centralized allocation systems. However, unlike solving the problem from a single solver view, there is no absolute control or tight coordination over how and when to decompose the problem, when to undertake a solution or the procedure for verifying consistency. Such activities will largely be left to the distributed entities to decide either individually or jointly. As a consequence the resulting allocations may suffer and induce myopic behaviour lacking in stability and regularity in the performance of the total system. Thus, the issues connected with solving the problem often move beyond the pure algorithmic focus of computation to include how the problem is decomposed, how or to whom it is assigned, and the organization for solving the subproblems, strategies used for resolving conflicts, the manner in which communication is conducted, etc. These issues, in fact, highlight the holistic nature of the solution and are used here as parameters to compare and contrast the approaches reported in the literature.