Optimal User Set Research Articles

A dynamic distributed energy storage control strategy for providing primary frequency regulation using multi‐armed bandits method

Maintaining frequency stability is a crucial but challenging task for the stable operation of a power system. The distributed energy storage (DES) can charge or discharge for both upward and downward frequency regulation, exploring and effectively using the regulation capabilities will provide a strong backup for frequency regulation. Here, a dynamic DES control strategy for providing primary frequency regulation is proposed. The different behaviours of storage owners are considered when they respond to the regulation requests from the aggregator. This kind of uncertainty would lead to a mismatch between the final aggregation result and the expected target. Hence, the multi-armed bandit approach is applied to learn users’ response behaviour and select the optimal set of users to mitigate the mismatch. Case studies on the IEEE RTS 24-bus system demonstrate that the proposed method's mismatch between the actual aggregation result and the regulation target is less than half as much as the conventional method, and it can restore the frequency 20 events earlier than the traditional method.

Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation

Based on the concepts of “word-of-mouth” effect and viral marketing, the diffusion of an innovation may be triggered starting from a set of initial users. Estimating the influence spread is a preliminary step to determine a suitable or even optimal set of initial users to reach a given goal. In this paper, we focus on a stochastic model called the independent cascade model and compare a few approaches to compute activation probabilities of nodes in a social network, i.e., the probability that a user adopts the innovation. First, we propose the path method that computes the exact value of the activation probabilities but has high complexity. Second, an approximated method, called SSS-Noself, is obtained by the modification of the existing SteadyStateSpread algorithm, based on fixed-point computation, to achieve better accuracy. Finally, an efficient approach, also based on fixed-point computation, is proposed to compute the probability that a node is activated through a path of minimal length from the seed set. This algorithm, called SSS-Bounded-Path algorithm, can provide a lower bound for the computation of activation probabilities. Furthermore, these proposed approaches are applied to the influence maximization problem combined with the SelectTop$K$ algorithm, the RankedReplace algorithm, and the greedy algorithm.

Throughput performance of NOMA in WLANs with a CSMA MAC protocol

The existing medium access control (MAC) protocols are not able to utilize the full opportunities from power-domain non-orthogonal multiple access (NOMA) technique in wireless local area networks (WLANs). In this paper, we propose a carrier sense multiple access (CSMA) MAC protocol to increase downlink throughput by utilizing the opportunities offered by NOMA technique in downlink access of WLANs. For downlink transmission, an algorithm is developed to select an optimal user-set with appropriate power allocation from a randomly selected user-set. We then develop an analytical model to compute the uplink and downlink throughputs of a WLAN under the proposed MAC protocol by modelling the WLAN system as a discrete time Markov chain. The uplink and downlink throughputs of a WLAN under the proposed MAC protocol are determined by means of the analytical model and the accuracy of the analytical model is verified via extensive simulation. It is demonstrated that the proposed NOMA based MAC protocol improves the downlink throughput significantly compared to an orthogonal multiple access (OMA) based traditional CSMA MAC protocol without reducing the uplink throughput considerably. For a reasonable configuration, the downlink throughput gain is found to be more than 250%. We also study the throughput performance of the proposed MAC protocol for different transmit power levels, user medium access rates, data rates, path loss exponents and number of users in WLAN. We find that the throughput gain obtained by the proposed MAC protocol increases with increasing the transmit power and decreases with increasing the data rates and path loss. However, the change in the throughput gain obtained by the proposed MAC protocol is not significant for increasing the number of users and the user medium access rate.

MAIM: A Novel Incentive Mechanism Based on Multi-Attribute User Selection in Mobile Crowdsensing

In the user selection phase of mobile crowdsensing, most existing incentive mechanisms focus on either single-attribute selection or random selection, which possibly lead to serious consequences such as low user enthusiasm, decreased task completion rate, and increased cost of platform consumption. To tackle these issues, in this paper, we propose a novel incentive mechanism MAIM, which is based on multi-attribute user selection and participation intention analysis function in mobile crowdsensing. In this mechanism, the sensing platform employs the analytic hierarchy process to determine the weights of three attributes: participation threshold, cost, and reputation. The weight calculation results of each sensing user with respect to each attribute are then integrated to obtain the sorted weight of each user, with which the sensing platform will then obtain the optimal user set. From the users’ perspective, they can autonomously decide whether to accept task processing requests, as enabled by the participation intention analysis function, thereby voiding the absolute authority and control of the sensing platform over users and achieving a two-way selection between the sensing platform and the sensing users. Furthermore, the sensing platform establishes a score-based reputation reward to inspire active performers and utilizes a punishment mechanism to overawe malicious vandals, which substantially helps activize enthusiasm of user participation and improve sensing data quality. Simulation results indicate that the proposed MAIM has significantly improved the sensing task completion ratio and the budget surplus ratio compared with the existing incentive mechanisms in mobile crowdsensing.

An optimization approach for district heating strategic network design

District heating systems provide the heat generated in a centralized location to a set of users for their residential and commercial heating requirements. Heat distribution is generally obtained by using hot water or steam flowing through a closed network of insulated pipes and heat exchange stations at the users’ locations. The use of optimization techniques for the strategic design of such networks is strongly motivated by the high cost of the required infrastructures but is particularly challenging because of the technical characteristics and the size of the real world applications.We present a mathematical model developed to support district heating system planning. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues and minimizing infrastructure and operational costs. The model considers steady state conditions of the hydraulic system and takes into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed.

User scheduling for MU-MIMO transmission with active CSI feedback

User scheduling boosts the multi-user multi-input multi-output (MU-MIMO) gain by selecting an optimal set of users to increase the 802.11 Wi-Fi system capacities. Many kinds of user scheduling algorithms, however, fail to fully realize the advantages of MU-MIMO due to considerable channel state information (CSI) overhead. In this paper, we propose a new MU-MIMO MAC protocol, called 802.11ac+, including a novel user scheduling algorithm. Unlike most proposals, where user scheduling is performed after an access point (AP) receives CSI from all users, 802.11ac+ determines the best user set during the CSI feedback phase. In particular, the AP broadcasts a channel hint about previously scheduled users, and the remaining users actively send CSI reports according to their effective channel gains (ECGs) calculated from the hint. Based on the proposed scheme, we develop two fair scheduling protocols, Round-Robin 802.11ac+ (RR-11ac+) and Proportional-Fair 802.11ac+ (PF-11ac+). Through trace-driven MATLAB simulations, we prove that the proposed schemes not only improve the throughput gain but also enhance the fairness among users.

다중 사용자 거대 다중 안테나 네트워크에서의 사용자 및 안테나 선택 기법

ABSTRACT Recently, multi-user massive MIMO (MU-Massive MIMO) network has attracted a lot of attention as a technology to accommodate explosively increasing mobile data traffic. However, the MU-Massive MIMO network causes a tremendous hardware complexity in a base station and computational complexity to select optimal set of users. In this paper, we thus propose a simple algorithm for selecting antennas and users while reducing the hardware and computational complexities simultaneously. The proposed scheme has a computational complexity of     ×min    , which is significantly reduced compared to the complexity of optimal scheme based on Brute-Force searching,        min    , where  ,   , and  denote the number of total transmit antennas, the number of selected antennas, and the number of all users, respectively. 키워드 : MIMO, 다중 안테나&사용자 네트워크, 복잡도, 사용자 선택, 안테나 선택 Key word : MIMO, MU-Massive MIMO networks, Complexity, User selection, Antenna selectionJournal of the Korea Institute of Information andCommunication Engineering

Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time. The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding. All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.

Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems

Social explanation, the statement with the form of "A and B also like the item", is widely used in almost all the major recommender systems in the web and effectively improves the persuasiveness of the recommendation results by convincing more users to try. This paper presents the first algorithm to generate the most persuasive social explanation by recommending the optimal set of users to be put in the explanation. New challenges like modeling persuasiveness of multiple users, different types of users in social network, sparsity of likes, are discussed in depth and solved in our algorithm. The extensive evaluation demonstrates the advantage of our proposed algorithm compared with traditional methods.

Low complexity user scheduling algorithm for energy‐efficient multiuser multiple‐input multiple‐output systems

In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, block diagonalisation (BD) is a well-known precoding technique that eliminates inter-user interference. The number of simultaneously supportable users with BD is limited by the number of base station transmit antennas and the number of user receive antennas. Traditional MU-MIMO scheduling algorithms focus on sum capacity. However, these user scheduling algorithms might not be optimal with respect to energy efficiency. Here, the authors consider the energy-efficient MU-MIMO scheduling that maximises the energy efficiency. The brute-force search for the optimal user set, however, is computationally prohibitive. Therefore they propose a low-complexity user scheduling algorithm for energy-efficient MU-MIMO systems. They first obtain an approximation expression for the energy efficiency by utilising the upper bound of the MU-MIMO system capacity. Then they show that the maximum energy efficiency can be achieved if the scheduling user set is selected to obtain the largest matrix volume. The numerical results show that the proposed algorithm achieves a good tradeoff between energy efficiency and computational complexity. They also consider the problem of maintaining fairness among users, and propose a simplified proportional fair (PF) scheduling algorithm.

Low Complexity Suboptimal User Selection Algorithm for Multiuser MIMO Systems with Block Diagonalization

In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, block diagonalization (BD) is a well-kown precoding technique that eliminates interuser interference. The number of simultaneously supportable users with BD is limited by the number of base station transmit antennas and the number of user receive antennas. The brute-force search for the optimal user set, however, is computationally prohibitive. Therefore, we propose a low complexity and suboptimal user selection algorithm based on block diagonalization for MU-MIMO systems. We introduce a strong tight upper bound of sum capacity as selection metric. Furthermore, we employ a substitution operation to improve system performance. The computational complexity analysis and simulation results show that the proposed algorithm achieves comparable throughput with low complexity compared to the existing algorithms.

Conditional Entropy Based User Selection for Multiuser MIMO Systems

We consider the problem of user subset selection for maximizing the sum rate of downlink multi-user MIMO systems. The brute-force search for the optimal user set becomes impractical as the total number of users in a cell increase. We propose a user selection algorithm based on conditional differential entropy. We apply the proposed algorithm on Block diagonalization scheme. Simulation results show that the proposed conditional entropy based algorithm offers better alternatives than the existing user selection algorithms. Furthermore, in terms of sum rate, the solution obtained by the proposed algorithm turns out to be close to the optimal solution with significantly lower computational complexity than brute-force search.

Joint Optimization of User Set Selection and Transmit Power Allocation for Orthogonal Random Beamforming in Multiuser MIMO Systems

When the number of users is finite, the performance improvement of the orthogonal random beamforming (ORBF) scheme is limited in high signal-to-noise ratio regions. In this paper, to improve the performance of the ORBF scheme, the user set and transmit power allocation are jointly determined to maximize sum rate under the total transmit power constraint. First, the transmit power allocation problem is expressed as a function of a given user set. Based on this expression, the optimal user set with the maximum sum rate is determined. The suboptimal procedure is also presented to reduce the computational complexity, which separates the user set selection procedure and transmit power allocation procedure.

Low-complexity multiuser MIMO downlink system based on a small-sized CQI quantizer

Abstract It is known that the conventional semi-orthogonal user selection based on a greedy algorithm cannot provide a globally optimal solution due to its semi-orthogonal property. To find a more optimal user set and prevent the waste of the feedback resource at the base station, we present a multiuser multiple-input multiple-output system using a random beamforming (RBF) scheme, in which one unitary matrix is used. To reduce feedback overhead for channel quality information (CQI), we propose an efficient CQI quantizer based on a closed-form expression of expected SINR for selected users. Numerical results show that the RBF with the proposed CQI quantizer provides better throughput than conventional systems under minor levels of feedback.

Iterative Precoder Design and User Scheduling for Block-Diagonalized Systems

The block diagonalization (BD) scheme is a low-complexity suboptimal precoding technique for multiuser multiple input-multiple output (MIMO) downlink channels, which completely precancels the multiuser interference. Accordingly, the precoder of each user lies in the null space of other users' channel matrices. In this paper, we propose an iterative algorithm using QR decompositions (QRDs) to compute the precoders. Specifically, to avoid dealing with a large concatenated matrix, we apply the QRD to a sequence of matrices of lower dimensions. One problem of BD schemes is that the number of users that can be simultaneously supported is limited due to zero interference constraints. When the number of users is large, a set of users must be selected, and selection algorithms should be designed to exploit the multiuser diversity gain. Finding the optimal set of users requires an exhaustive search, which has too high computational complexity to be practically useful. Based on the iterative precoder design, this paper proposes a low-complexity user selection algorithm using a greedy method, in which the precoders of selected users are recursively updated after each selection step. The selection metric of the proposed scheduling algorithm relies on the product of the squared row norms of the effective channel matrices, which is related to the eigenvalues by the Hadamard and Schur inequalities. An asymptotic analysis is provided to show that the proposed algorithm can achieve the optimal sum rate scaling of the MIMO broadcast channel. The numerical results show that the proposed algorithm achieves a good trade-off between sum rate performance and computational complexity. When users suffer different channel conditions, providing fairness among users is of critical importance. To address this problem, we also propose two fair scheduling (FS) algorithms, one imposing fairness in the approximation of the data rate, and another directly imposing fairness in the product of the squared row norms of the effective channel matrices.

Low Complexity Multiuser Scheduling in Time-Varying MIMO Broadcast Channels

The sum-rate maximization rule can find an optimal user set that maximizes the sum capacity in multiple input multiple output (MIMO) broadcast channels (BCs), but the search space for finding the optimal user set becomes prohibitively large as the number of users increases. The proposed algorithm selects a user set of the largest effective channel norms based on statistical channel state information (CSI) for reducing the computational complexity, and uses Tomlinson-Harashima precoding (THP) for minimizing the interference between selected users in time-varying MIMO BCs.

On the Convergence of Genetic Scheduling Algorithms for Downlink Transmission in Multi-User MIMO Systems

In a multi-user MIMO system using a successive precoding method such as dirty paper coding, it is combinatorially complex to determine the optimal set of users to schedule and the proper order to encode their signals in order to optimize a utility function in a scheduling algorithm. Genetic algorithms represent a fast suboptimal approach to reducing the complexity of the search. In this paper, we build upon prior work that implements scheduling via genetic algorithms. We examine the impact of parameter values within the adaptive mutation rate of the algorithm on its convergence time. We demonstrate that although there is a range of values for the parameters that yields similar near-minimum convergence times, it is nonetheless important to ensure that the parameters are tuned to be within that range. In one case, tuning the parameter values reduces the time of convergence to less than 30% compared to that achievable with the initial parameter values. We also demonstrate that the proper parameter values are dependent on both the number of transmit antennas and the number of users in the pool of users to be scheduled. A simple equation is proposed that is linear in the adaptive mutation parameters to tune the values for different numbers of transmit antennas and users.

Joint Throughput Maximization and Fair Uplink Transmission Scheduling in CDMA Systems

We study the fundamental problem of optimal transmission scheduling in a code-division multiple-access wireless system in order to maximize the uplink system throughput, while satisfying the users quality-of-service (QoS) requirements and maintaining fairness among them. The corresponding problem is expressed as a weighted throughput maximization problem, under certain power and QoS constraints, where the weights are the control parameters reflecting the fairness constraints. With the introduction of the power index capacity, it is shown that this optimization problem can be converted into a binary knapsack problem, where all the corresponding constraints are replaced by the power index capacities at some certain system power index. A two-step approach is followed to obtain the optimal solution. First, a simple method is proposed to find the optimal set of users to receive service for a given fixed target system load, and then the optimal solution is obtained as a global search within a certain range. Furthermore, a stochastic approximation method is presented to effectively identify the required control parameters. The performance evaluation reveals the advantages of our proposed policy over other existing ones and confirms that it achieves very high throughput while maintains fairness among the users, under different channel conditions and requirements.

A Distributed Downlink Scheduling Method for Multi-user Communication with Zero-Forcing Beamforming

A distributed scheduling approach to multi-user beamforming at a base-station (BS) is proposed. The scheme assumes the BS is equipped with multiple antennas and uses zero-forcing beamforming to schedule multiple users on the same time-frequency uplink/downlink channels. The optimal user set that the BS can schedule is the one that maximizes the sum-rate for the communication channel. The computational complexity of finding the optimal user set becomes intractable as the number of users exceeds 20-30. Sub-optimal schemes for maximizing the sum-rate generally require large control overhead. We propose an effective sum-rate performance metric that accounts for the control overhead required by the scheduling method. We also propose a user scheduling method that has both low complexity and low overhead. The complexity and overhead are reduced by adding one user at a time and distributing the computation of the scheduling problem across the users. Each user calculates the sum-rate that would result if they were added to the existing user set using their own channel and knowledge of the channels of existing users that is broadcast by the BS. The user with greatest sum-rate increase is scheduled. We use simulations to show that this approach can achieve significantly higher effective sum rates than previously proposed sub-optimal schemes. We also derive an approximate expression for the expected sum-rate with our distributed scheduling method and demonstrate its accuracy with simulations.

Optimal Beam Subset and User Selection for Orthogonal Random Beamforming

The throughput performance of orthogonal random beamforming (ORBF) with a finite number of users is limited due to the increasing amount of residual interference. In this letter, we find the optimal beam subset, the optimal user set, and the optimal number of random beams to maximize the sum throughput of the ORBF. The proposed scheme provides the best trade-off between the multiplexing gain and the multiuser interference by the determination of the optimal number of random beams as well as the beam selection diversity gain due to the selection of the optimal beam subset. In addition, two efficient suboptimal schemes are presented to reduce the computational complexity and the feedback overhead of the optimal method.