How to Allocate Resources For Features Acquisition
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We study classification problems where features are corrupted by noise and where the magnitude of the noise in each feature is influenced by the resources allocated to its acquisition. This is the case, for example, when multiple sensors share a common resource (power, bandwidth, attention, etc.). We develop a method for computing the optimal resource allocation for a variety of scenarios and derive theoretical bounds concerning the benefit that may arise by non-uniform allocation. We further demonstrate the effectiveness of the developed method in simulations.Keywords:
Feature (linguistics)
Resource constraints
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In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destination can maximize a utility function which represents the task to be executed only knowing the quantized information. For example, for utility functions under the form u (x; g), x might represent a decision in terms of using some radio resources and g the system state which is only observed through its quantized version Q(g). Both in the case where the utility function is known and the case where it is only observed through its realizations, we provide solutions to determine such a quantizer. We show how this approach applies to energy-efficient power allocation. In particular, it is seen that quantizing the state very roughly is perfectly suited to sum-rate-type function maximization, whereas energy-efficiency metrics are more sensitive to imperfections.
Maximization
Resource Management
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The behavior of users in relatively predictable, both in terms of the data they request and the wireless channels they observe. In this paper, we consider the statistics of such predictable patterns of the demand and channel jointly across multiple users, and develop a novel predictive resource allocation method. This method is shown to provide performance benefits over a reactive approach, which ignores these patterns and instead aims to satisfy the instantaneous demands, irrespective of cost to the system. In particular, we show that our proposed method is able to attain a novel fundamental bound on the achievable cost, as the service window grows. Through numerical evaluation, we gain insights into how different uncertainty sources affect the decisions and the cost.
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In this paper, we investigate efficient resource allocation for the uplink transmission of wireless powered IoT networks. LoRa is adopted as an example network of focus, however the work can be easily generalized to other radios. Allocating limited resources, like spectrum and energy resources, among massive numbers of users faces critical challenges. We consider grouping wireless powered users first, and for users assigned to the same channel, power allocation is investigated to improve the throughput of each user. Specifically, a user grouping problem has been formulated as a many-to-one matching game by treating users and channels as two sets of selfish players aiming to maximize their respective utilities. A low-complexity efficient channel allocation algorithm (ECAA) is proposed for user grouping. Additionally, a Markov Decision Process (MDP) is adopted to model the unpredictable energy arrival and channel conditions uncertainty, and a power allocation algorithm is proposed to maximize the accumulative throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation decision making local to users. Simulation results demonstrate that the proposed ECAA scheme can achieve near-optimal performance with much lower computational complexity than brute force exhaustive-search approach. Moreover, simulations show that the distributed optimal power allocation policy for each user is obtained with better performance than a centralized offline scheme.
Wireless Power Transfer
Channel allocation schemes
Resource Management
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We study a sequential second-price auction for allocating wireless resources between two non-cooperative users. This mechanism requires relatively little computation and information exchange among agents, but does not always achieve an efficient allocation. This is a continuation of previous work in which the worst-case efficiency is evaluated, assuming each user has full knowledge of the other user's utility function. Here we assume that the users are randomly placed within a region, and evaluate the associated efficiency via simulation. Sequential auctions for bandwidth (with fixed power) and for power (with fixed bandwidth) are considered, where each user utility is the achievable rate, and interference is treated as background noise. Our results show that the sequential auction typically achieves the efficient (utility-maximizing) allocation. We also relate observed improvements in the worst-case efficiency to constraints on the size of the marginal utilities associated with each resource.
Bandwidth allocation
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We consider a multi-user system that employs mixed numerology as defined by the 3rd Generation Partnership Project for a new radio physical layer design. Accounting for different channel conditions, imperfect channel knowledge and inter-band interference as a consequence of mixed numerology, we propose a new optimization method for resource allocation. Based on this method, we are able to optimally distribute the available bandwidth amongst the users, such that they achieve the same quality of service. We show how resource allocation depends on the channel properties such as Doppler and delay spread. In addition, optimal resource allocation is verified by simulations of a 5G compliant system.
Channel allocation schemes
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In the emerging fog computing ecosystem, a fundamental problem is to allocate the available resources for computing and communication. Moreover, in many cases these resources have a natural hierarchical structure to them, e.g., allocating resources to network slices, which in turn are shared by a group of users. We consider auction-based approaches for allocating resources in such an environment so as to account for diverse user incentives. The well-known Vickrey-Clarke-Groves(VCG) mechanism provides a strong solution to the incentive issue, but also has the well-known drawback of requiring an excessive amount of information for a wireless system. Recent work has shown that, when allocating a single divisible resource, this information can be reduced via quantization while maintaining VCG's incentive properties. Here, we build on this approach and apply it instead to a hierarchical setting, in which users are divided into groups. Each group is subject to a local resource constraint as well as a global resource constraint across all groups. We specify a distributed quantized mechanism for such a setting that has the same incentive properties as VCG. We characterize the communication overhead and the worst-case efficiency loss in this mechanism. We also consider how to assign constraints on groups for a given sum constraint as well as for a case where the sum-constraint can be varied at a given per unit cost.
Resource Management
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In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to packet dropouts, proper allocation of limited resources, e.g. transmission power, across plants is critical for maintaining reliable operation. In this paper, we formulate the design of an optimal resource allocation policy that uses current plant states and wireless channel states to assign resources used to send control actuation information back to plants. While this problem is challenging due to its infinite dimensionality and need for explicit system model and state knowledge, we propose the use of deep reinforcement learning techniques to find neural network-based resource allocation policies. In particular, we use model-free policy gradient methods to directly learn continuous power allocation policies without knowledge of plant dynamics or communication models. Numerical simulations demonstrate the strong performance of learned policies relative to baseline resource allocation methods in settings where state information is available both with and without noise.
Channel state information
Resource Management
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In this paper, we study utility-based maximization for resource allocation in the downlink direction of centralized wireless networks. We consider two types of traffic, i.e., best effort and hard QoS, and develop some essential theorems for optimal wireless resource allocation. We then propose three allocation schemes. The performance of the proposed schemes is evaluated via simulations. The results show that optimal wireless resource allocation is dependent on traffic types, total available resource, and channel quality, rather than solely dependent on the channel quality or traffic types as assumed in most existing work. In this paper, we also focus on “user satisfaction” for resource allocation to avoid such a “throughput-fairness” dilemma. Since it is unlikely to fully satisfy the different demands of all users, we turn to maximize the total degree of user satisfaction.
Max-min fairness
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This chapter is dedicated to the study of distributed resource allocation problems in wireless communication networks. Non-cooperative game theory proves to be a useful tool to investigate this type of problems. The players, the transmitter nodes, choose their power allocation policies in order to maximize their own information theoretic payoffs, namely their individual achievable Shannon transmission rates. Our attention is mostly focused on the basic multi-user channel models: the multiple access channel (MAC) and the interference channel (IC). However, more complex channels such as the interference relay channel (IRC) and the cognitive radio channel (CRC) are also discussed. We provide an updated overview of the existing results with respect to the non-cooperative solution of the game, the Nash equilibrium (NE), its existence, uniqueness, and convergence of distributed algorithms. Furthermore, we evaluate the performance gap between the distributed and the centralized approach where a network authority allocates the overall resources of the network. We discuss several methods that improve the performance of the NE at the cost of introducing a supplementary signaling cost or intervention of a central authority or a certain cooperation degree at the user level. We conclude by a critical discussion about the drawbacks and possible improvements of the game theoretical approach to solve resource allocation problems in general.
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One of the many problems faced by current cellular network technology is the under utilization of the dedicated, licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each others' spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. This allows for computation of the game theoretical solution using generic Markov Chain Monte Carlo method. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.
Operator (biology)
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