User Selection Problem Research Articles

Low-Complexity User Selection Algorithms for Multiuser Transmissions in mmWave WLANs

In this paper, we propose a low-complexity user selection algorithm for an uplink multiuser transmission in millimeter wave (mmWave) WLAN. We first formulate the user selection problem, taking hybrid beamforming (HBF), an NP-hard problem, into consideration. We then develop a three-step HBF algorithm that incorporates user selection. Specifically, users can be selected based on semi-orthogonality instead of collecting perfect channel state information (CSI) from all potential users. We optimize the digital beamforming to mitigate residual interference among the selected users. Furthermore, we provide analytical validation for the proposed user selection algorithm and study the impact of angle correlation, analog beam pattern, and beamwidth on the achievable rate of the selected users. Extensive simulations validate the performance of the proposed overall HBF algorithm when compared with existing solutions.

Distributed self-optimizing interference management in ultra-dense networks with non-orthogonal multiple access

Both non-orthogonal multiple access (NOMA) and ultra-dense network (UDN) are promising technologies in future wireless networks. However, considering the overlapped coverage of small base stations (SBSs) and the spectrum sharing with NOMA, interference management (IM) becomes a more complex and fundamental problem. Moreover, considering the massive SBSs and dynamic network conditions in UDN, more efficient mechanisms need to be designed to deal with the IM issue. Thus, we propose a distributed self-optimizing interference management approach to address both the intra-cell interference caused by NOMA and the inter-cell interference among dense deployed SBSs. Aiming to minimize the interference and guarantee the users’ requirements, we mathematically formulate the joint resource allocation and user selection problem with consideration of the diverse user requirements, complicated interference topology, and limited resources. Furthermore, we consider the imperfections of successive interference cancellation at receivers for separating and decoding superimposed signals and analyze the impacts of residual interference and outage probability in NOMA-based UDNs. For tractability purpose, we introduce interference graph and satisfaction game theory and propose distributed algorithms to solve the problem. Simulation results show that interference can be reduced significantly in UDNs with NOMA compared with the traditional IM approaches.

Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms

Non-orthogonal multiple access (NOMA) is a promising technology to increase the spectral efficiency and enable massive connectivity in 5G and future wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA multiplexes several users on the same frequency and time resource. Joint subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in general. In this family of problems, we consider the weighted sum-rate (WSR) objective function as it can achieve various tradeoffs between sum-rate performance and user fairness. Because of JSPA's intractability, a common approach in the literature is to solve separately the power control and subcarrier allocation (also known as user selection) problems, therefore achieving sub-optimal result. In this work, we first improve the computational complexity of existing single-carrier power control and user selection schemes. These improved procedures are then used as basic building blocks to design new algorithms, namely Opt-JSPA, $\varepsilon$-JSPA and Grad-JSPA. Opt-JSPA computes an optimal solution with lower complexity than current optimal schemes in the literature. It can be used as a benchmark for optimal WSR performance in simulations. However, its pseudo-polynomial time complexity remains impractical for real-world systems with low latency requirements. To further reduce the complexity, we propose a fully polynomial-time approximation scheme called $\varepsilon$-JSPA. Since, no approximation has been studied in the literature, $\varepsilon$-JSPA stands out by allowing to control a tight trade-off between performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based on gradient descent. Numerical results show that it achieves near-optimal WSR with much lower complexity than existing optimal methods.

A Prediction-Based User Selection Framework for Heterogeneous Mobile CrowdSensing

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular $k$k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.

Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.

Coverage-Balancing User Selection in Mobile Crowd Sensing with Budget Constraint.

Mobile crowd sensing (MCS) is a new computing paradigm for the internet of things, and it is widely accepted as a powerful means to achieve urban-scale sensing and data collection. In the MCS campaign, the smart mobilephone users can detect their surrounding environments with their on-phone sensors and return the sensing data to the MCS organizer. In this paper, we focus on the coverage-balancing user selection (CBUS) problem with a budget constraint. Solving the CBUS problem aims to select a proper subset of users such that their sensing coverage is as large and balancing as possible, yet without violating the budget specified by the MCS campaign. We first propose a novel coverage balance-based sensing utility model, which effectively captures the joint requirement of the MCS requester for coverage area and coverage balance. We then formally define the CBUS problem under the proposed sensing utility model. Because of the NP-hardness of the CBUS problem, we design a heuristic-based algorithm, called MIA, which tactfully employs the maximum independent set model to determine a preliminary subset of users from all the available users and then adjusts this user subset to improve the budget implementation. MIA also includes a fast approach to calculating the area of the union coverage with any complicated boundaries, which is also applicable to any MCS scenarios that are set up with the coverage area-based sensing utility. The extensive numeric experiments show the efficacy of our designs both in coverage balance and in the total coverage area.

User selection utilizing data properties in mobile crowdsensing

The information sensed by a mobile device is just data, while the aggregation of information sensed by thousands of devices is knowledge. This is the basic idea of Mobile CowdSensing (MCS). In MCS, the user selection problem has always been a key issue, where the task publisher attempts to recruit a suitable set of users to cooperatively sense the events or data at some particular time points and places. Hence, the temporal and spatial constraint becomes the main criterion to select the users. However, whether a user prefers to accomplish the sensing data not only depends on the temporal and spatial relationship, but also lies on the data property. In other words, a user is likely to take the sensing task if it is interested in the data to be sensed. In this paper, we propose a user Selection utilizing data properties in mobile crowdsensing (SPM), where a triple-layer structure considering not only the temporal and spatial probability, but also the data’s property is formulated, and task finishing probabilities are calculated in both the intentional and unintentional situations. Then, a greedy algorithm is proposed to select a suitable set of users for finishing the sensing tasks. We conduct extensive simulations based on three widely-used real-world traces: geolife, roma/taxi, epfl and a synthetic data set. The results show that, compared with other user selection strategies, SPM finishes the largest number of sensing tasks.

Hybrid cooperative spectrum sensing scheme based on spatial–temporal correlation in cognitive radio enabled VANET

Cognitive radio enabled vehicular ad-hoc networks (CR-VANETs) are one of the promising architectures in the future vehicular ad-hoc networks. In this study, the joint spatial-temporal correlation is exploited to improve decision accuracy of cooperative spectrum sensing (CSS) while reducing overhead introduced by cooperation in the CR-VANETs. Firstly, a theoretical method is presented to analyse the impact of spatial-temporal correlation on cooperative sensing performance when using soft combining in the CR-VANET. Then, the expression of the optimal probability of detection with respect to spatial-temporal correlation is given for a target probability of false alarm by employing likelihood ratio test. Additionally, the user selection problem is formulated as an efficient double-threshold optimisation problem by considering both sensing accuracy and stability to achieve the optimal probability of detection. Finally, a hybrid CSS scheme based on spatial-temporal correlation is designed for the CR-VANET. Simulation results reveal that the proposed scheme could achieve significant sensing performance gain by selecting a subset of secondary users for combination, and could reduce user selection frequency by employing spatial-temporal diversity.

Deep Reinforcement Learning Based Intelligent User Selection in Massive MIMO Underlay Cognitive Radios

Cognitive radio (CR) and massive multiple-input multiple-output (MIMO) have attracted much interest recently due to the amazing ability to accommodate more users and improve spectrum utilization. This paper investigates the QoS-aware user selection approach for massive MIMO underlay cognitive radio. Two main CR scenarios are considered: 1) the channel state information (CSI) of the cross channels are available at the secondary base station (SBS), and 2) any CSI of cross-network is unavailable at SBS. For the former, we develop the low-complexity increase-user-with-minimum-power algorithm (IUMP) and decrease-user-with-maximum-power algorithm (DUMP) which both can address the problem of user selection with power allocation. However, the CSI is typically not available in practice. To address the intractable issue, we propose a deep reinforcement learning-based approach, which can enable the SBS to realize efficient and intelligent user selection. The simulation results show that the IUMP and DUMP algorithms have obvious performance advantages over traditional user selection methods. In addition, results also verify that our constructed neural network can efficiently learn the optimal user selection policy in the unknown dynamic environment with fast convergence and high success rate.

The Ergodic Capacity of the Multiple Access Channel Under Distributed Scheduling - Order Optimality of Linear Receivers

Consider the problem of a multiple-input multiple-output multiple-access channel at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. However, since solutions which collect channel state information from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we analyze a distributed user selection algorithm, which selects a group of users to transmit without coordinating between users and without all users sending CSI to the base station. This threshold-based algorithm is analyzed for both zero-forcing and minimum mean square error receivers, and its expected sum rate in the limit of large number of users is investigated. It is shown that for large number of users, it achieves the same scaling laws as the optimal centralized scheme.

QoS Aware Power Allocation and User Selection in Massive MIMO Underlay Cognitive Radio Networks

We address the problem of power allocation and secondary user (SU) selection in the downlink from a secondary base station (SBS) equipped with a large number of antennas in an underlay cognitive radio network. A new optimization framework is proposed in order to select the maximum number of SUs and compute power allocations in order to satisfy instantaneous rate or QoS requirements of SUs. The optimization framework also aims to restrict the interference to primary users (PUs) below a predefined threshold using available imperfect CSI at the SBS. In order to obtain a feasible solution for power allocation and user selection, we propose a low-complexity algorithm called DeleteSU-with-Maximum-Power-allocation (DMP). Theoretical analysis is provided to compute the interference to PUs and the number of SUs exceeding the required rate. The analysis and simulations show that the proposed DMP algorithm outperforms the stateof-the art selection algorithm in terms of serving more users with minimum rate constraints, and it approaches the optimal solution if the number of antennas is an order of magnitude greater than the number of users.

On the Performance of Beam Division Nonorthogonal Multiple Access for FDD-Based Large-Scale Multi-User MIMO Systems

For the frequency division duplexing-based large-scale multiple-input multiple-output (MIMO) systems, we consider a multi-user MIMO (MU-MIMO) non-orthogonal multiple access (NOMA) scheme, referred to as beam division-NOMA (BD-NOMA) , which is featured by a long-term feedback-based user grouping and clustering, along with a short-term feedback-based BD-NOMA scheduling. The performance of BD-NOMA is not warranted in practice because near users are severely interfered by strong signals for the far users of other beams, and furthermore, the long-term feedback-based user grouping and clustering might result in short-term inter-beam interference. We consider the short-term feedback-based joint optimization problem of user selection and power allocation for NOMA in each beam and system-wide power allocation for MU-MIMO, dealing with both intra-beam and inter-beam interference at the same time. We formulate a joint optimization problem for a large-scale MIMO system with the multiple channel quality indicators-based short-term feedback. In our analysis, we reformulate the original problem into an equivalent one that can be solved iteratively with the weighted minimum mean square error-based algorithm. Then, the performance analysis is presented to conclude that the proposed optimization is essential for enabling BD-NOMA to outperform beam division multiple access, especially when inter-beam interference becomes critical.

Low Complexity BF scheme for downlink MISO‐NOMA systems

In this study, the authors investigate the user selection (US), beamforming (BF) and power allocation (PA) problems for the downlink multiple-input–single-output-based non-orthogonal multiple-access system. For the US problem, they matched the BF and PA schemes with different US schemes to achieve the best spectral efficiency performance. For the BF and PA problems, they derive a non-normalised BF scheme to accomplish the BF and PA problems jointly. The non-normalised BF scheme requires much lower computational complexity than the duality BF scheme. According to their simulation results, the non-normalised BF scheme can achieve the same or better spectral efficiency performance than the duality BF scheme. When the number of users sharing the same time–frequency resource is large, both the duality BF scheme and the singular value decomposition (SVD)-based BF scheme cannot obtain spectral efficiency advantage over the OMA scheme. However, the non-normalised BF scheme can maintain the advantage over the OMA scheme.

Optimizing the interested area coverage with efficient mobile advertisement user selection

SummaryMobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are focusing on efficient distribution especially when user mobilities are involved. Unfortunately, previous studies have shown the interested area property during mobile advertisement propagation. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location‐centric mobile crowdsourcing network, where locations are vitally important for advertisement distribution, and mobile users need to be carefully selected for efficiency considerations. Different from traditional user‐centric and platform‐centric crowdsourcing networks, this work focuses on the mobile advertisement user selection problem when interested area coverage is considered.There are several fundamentally important challenges needed to be addressed before developing a location‐centric scheme for mobile advertisement user selection. First of all, we need to deal with the spatio‐temporal features for each user, where the interested area coverage ratio needs to be effectively evaluated. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes the following efforts: First, a budget‐constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved NP‐hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio . Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%. When considering the user's interest to different advertisements, the real user interest data set has also been used to validate that our proposed method could achieve improved performance than the random method.

Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

Mobile crowd sensing (MCS) is a novel sensing paradigm which can sense human-centered daily activities and the surrounding environment. The impact of mobility and selfishness of participants on the data reliability cannot be ignored in most mobile crowd sensing systems. To address this issue, we present a universal system model based on the reverse auction framework and formulate the problem as the Multiple Quality Multiple User Selection (MQMUS) problem. The quality-aware incentive mechanism (QAIM) is proposed to meet the quality requirement of data reliability. We demonstrate that the proposed incentive mechanism achieves the properties of computational efficiency, individual rationality, and truthfulness. And meanwhile, we evaluate the performance and validate the theoretical properties of our incentive mechanism through extensive simulation experiments.

Spatial Resource Squeezing and Its Usage in User Selection for Multi-User Multiple-Input Multiple-Output Systems

In this paper, a user selection problem for multiuser multiple-input multiple-output systems in the general wireless environment with full-rank and rank-deficient channel users is investigated. It is demonstrated that the maximum number of simultaneously supportable users is elastic, and the spatial resources occupied by each user can be squeezed in the rank-deficient environment. Therefore, a user selection algorithm is then proposed with relatively low computational overhead. The data rate requirement distinctions of different services are considered in the user selection criterion, and the resources occupied by real-time service users are primarily squeezed since they do not need extremely high data rate. Numerical results show that the effective throughput and the number of selected users achieved by the proposed user selection algorithm are larger than that of exiting researches.

User Selection Algorithms for Block Diagonalization Aided Multiuser Downlink mm-Wave Communication

In order to meet the ever increasing data rate demands, the next generation of wireless communication systems is being designed for exploiting the large amounts of unused spectrum in the millimeter-wave (mm-wave) band. Since operating at mm-wave frequencies imposes several challenges, such as high-path loss, as well as both spatial and temporal channel sparsity, there is a significant research interest focused on designing feasible solutions for establishing reliable and high-throughput links at mm-wave frequencies. In this paper, we consider a cellular system relying on hybrid beamforming aided base station (BS) as well as user equipment and study the user selection problem, which has not been hitherto studied in the literature. More specifically, we study the problem of selecting $K^\prime $ -users by the BS for communication out of $K$ -users whilst ensuring that the sum rate is maximized. Specifically, we propose a user selection algorithm, which relies on the knowledge of both the channel gains and the angle of departure (AoD) of the channel paths spanning to the various users, which is termed the AoD aided user selection (AoD-US). Furthermore, we devise a pair of subspace metrics based on: 1) the angle between the subspaces spanned by the BS array response vectors; 2) the ratio of interference and signal space dimensions of various users, in order to reduce the user search space in AoD-US. This modified user selection algorithm is termed the AoD aided user selection with user set pruning (AoD-US-P). Furthermore, we study the attainable sum-rate performance of the block-diagonalization aided downlink and show that the proposed selection algorithms guarantee both multiuser diversity and multiplexing gains. Additionally, the proposed algorithms are studied in the round-robin scheduling scenario, where all the $K$ -users are scheduled for achieving fairness. Our simulation results revealed that the AoD-US-P achieves nearly the same performance as that achieved by the AoD-US despite having a small user set, while both are observed to outperform the channel power-based selection scheme.

Distributed Linear Precoding and User Selection in Coordinated Multicell Systems

In this paper, we tackle the problem of semidistributed user selection with distributed linear precoding for sum-rate maximization in multiuser multicell systems. A set of adjacent base stations (BSs) forms a cluster to perform coordinated transmission to cell-edge users, and coordination is carried out through a central processing unit (CU). However, the message exchange between BSs and the CU is limited to scheduling control signaling, and no user data or channel state information (CSI) exchange is allowed. In the considered multicell coordinated approach, each BS has its own set of cell-edge users and transmits only to one intended user while interference to nonintended users at other BSs is suppressed by signal steering (precoding). We use two distributed linear precoding schemes, namely, distributed zero forcing and distributed virtual signal-to-interference-plus-noise ratio (DVSINR). Considering multiple users per cell and the backhaul limitations, the BSs rely on local CSI to solve the user selection problem. First, we investigate how the signal-to-noise ratio regime and the number of antennas at the BSs impact the effective channel gain (the magnitude of the channels after precoding) and its relationship with multiuser diversity. Considering that user selection must be based on the type of implemented precoding, we develop metrics of compatibility (estimations of the effective channel gains) that can be computed from local CSI at each BS and reported to the CU for scheduling decisions. Based on such metrics, we design user selection algorithms that can find a set of users that potentially maximizes the sum rate. Numerical results show the effectiveness of the proposed metrics and algorithms for different configurations of users and antennas at the BSs.

Joint User Selection and Feedback Bit Allocation Based on Sparsity Constraint in MIMO Virtual Cellular Networks

In this paper, we jointly consider the user selection and feedback design problems in a virtual cellular network (VCN), where multiple base stations (BSs) share a user set. In many practical systems, the uplink feedback channel is generally shared by multiple users. Thus, the feedback budget allocated to unselected users not only wastes the feedback resources, but harms the system throughput by decreasing the available feedback budget for the selected users. We optimize both the user selection and feedback bit allocation based on long-term average channel information of the users. We first analyze the effects of the quantization error on the average achievable rate of the VCN system. Next, we propose a user selection and feedback bit allocation protocol under each BS’s sum feedback rate constraint as well as the sparsity constraint on all users’ feedback sizes. We show that the joint optimization problem can be decoupled into several NP-hard subproblems, one for each BS. We describe the brute-force searching algorithm for the optimal solution, and propose an efficient algorithm with significantly reduced computational complexity by relaxing the sparsity constraint on the feedback sizes. As a result, only the selected users exploit the uplink feedback budget, and the system performance is improved.

Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing

Mobile crowdsensing can enable numerous attractive novel sensing applications due to the prominent advantages such as wide spatiotemporal coverage, low cost, good scalability, pervasive application scenarios, etc. In mobile crowdsensing applications, incentive mechanisms are necessary to stimulate more potential smartphone users and to achieve good service quality. In this paper, we focus on exploring truthful incentive mechanisms for a novel and practical scenario where the tasks are time window dependent, and the platform has strong requirement of data integrity. We present a universal system model for this scenario based on reverse auction framework and formulate the problem as the Social Optimization User Selection (SOUS) problem. We design two incentive mechanisms, MST and MMT . In single time window case, we design an optimal algorithm based on dynamic programming to select users. Then we determine the payment for each user by VCG auction; while in multiple time window case, we show the general SOUS problem is NP-hard, and we design MMT based on greedy approach, which approximates the optimal solution within a factor of $\mathbf{In}|{\mathcal W}|+1$ , where $|{\mathcal W}|$ is the length of sensing time window defined by the platform. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, individual rationality and truthfulness.