Non-real Time Users Research Articles

Impact of Prediction Errors on High Throughput Predictive Resource Allocation

Predictive resource allocation (PRA) can leverage machine learning and historical data to boost the performance of mobile networks, say to support high throughput or improve the quality of service. While the large gain of PRA has been demonstrated by assuming perfect prediction or by optimizing with modeled prediction errors, how the prediction errors translate to the performance loss of PRA and how the future information should be predicted are not well understood. In this paper, we consider high throughput PRA policy for non-real-time (NRT) mobile users when the base stations also serve real-time (RT) users, where the future average rates of NRT users in a prediction window need to be predicted. The average rate can be predicted in various ways, where one typical way is to predict the rate directly from the historical average rates and another typical way is to predict the rate indirectly by first predicting traffic load and user trajectory. Given that the traffic load and user location can be gathered easily in cellular networks, we consider such an indirect prediction and analyze the impact of prediction errors of traffic load and trajectory on the prediction errors of average data rate. To this end, we first find the relation between the RT traffic load and the residual bandwidth after serving the RT users by resorting to effective capacity and effective bandwidth theory. To further justify why we analyze the indirect prediction, we compare the predictability of the time series used for the typical direct prediction and indirect prediction with entropy theory. Since different machine learning techniques lead to different prediction bias and prediction error variance, we then derive the relation between the prediction error statistics of the average data rate and those of traffic load and trajectory. We evaluate the performance loss of PRA using both modeled prediction errors and real predictions with deep learning. Our analyses show that for a network busy with RT service, the prediction errors of RT traffic load have larger impact on those of average data rate than the prediction errors of trajectory. The prediction bias has a larger impact on the throughput loss of PRA than the prediction error variance. This provides a guidance of designing learning techniques for predicting the required information.

Adaptive priority-based fair-resource allocation for MIMO-OFDM multicast networks

In MIMO-OFDM multicast networks, sub-carrier allocation for real-time and non-real time users is complex due to delay and throughput issues. In order to overcome these issues, in this paper, we propose to develop a priority-based fair-radio resource allocation for MIMO-OFDMA-based multicast system. Initially, the multicast groups are classified as RT, NRT and BE. Then priorities are assigned for the groups based on the QoS requirements such as delay tolerance, packet dropping rate and transmission rate. When the priority value of a group is greater than the priority threshold, then the resource is allocated to the relevant group as per fair resource allocation technique. Overall this technique improves the sub-carrier allocation for real-time and non-real time users in terms of spectral efficiency and fairness. By simulation results, we show that the proposed technique enhances the average throughput, spectral efficiency, sub-carrier distribution for each class of users.

Adaptive priority-based fair-resource allocation for MIMO-OFDM multicast networks

In MIMO-OFDM multicast networks, sub-carrier allocation for real-time and non-real time users is complex due to delay and throughput issues. In order to overcome these issues, in this paper, we propose to develop a priority-based fair-radio resource allocation for MIMO-OFDMA-based multicast system. Initially, the multicast groups are classified as RT, NRT and BE. Then priorities are assigned for the groups based on the QoS requirements such as delay tolerance, packet dropping rate and transmission rate. When the priority value of a group is greater than the priority threshold, then the resource is allocated to the relevant group as per fair resource allocation technique. Overall this technique improves the sub-carrier allocation for real-time and non-real time users in terms of spectral efficiency and fairness. By simulation results, we show that the proposed technique enhances the average throughput, spectral efficiency, sub-carrier distribution for each class of users.

Mixed Quality of Service in Cell-Free Massive MIMO

A mixed quality-of-service (QoS) problem is investigated in the uplink of a cell-free massive multiple-input multiple-output system where the minimum rate of non-real time users is maximized with per user power constraints while the rates of the real-time users (RTUs) meet their target rates. The original mixed QoS problem is formulated in terms of receiver filter coefficients and user power allocations, which can iteratively be solved through two sub-problems, namely, receiver filter coefficient design and power allocation. Numerical results show that, while the rates of RTUs meet the QoS constraints, the 90%-likely throughput improves significantly, compared with a simple benchmark scheme.

Optimal Power Control and Scheduling for Real-Time and Non-Real-Time Data

We consider a joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose two sum-rate-maximizing algorithms that satisfy these constraints as well as achieving the system's capacity region. In both algorithms, the power allocation policy has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users, which we call the Lambert-power policy, differs in structure from the water-filling policy for the NRT users. The first algorithm is optimal for the on–off channel model with a polynomial-time scheduling complexity in the number of RT users. The second, on the other hand, works for any channel fading model, which is shown through simulations to have an average complexity that is close to linear. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.

Game‐theoretic beamforming techniques for multiuser multi‐cell networks under mixed quality of service constraints

We propose a game-theoretic approach for the downlink beamformer design for a multiuser multi-cell wireless network under a mixed quality of services criterion. The network has real time users that must attain a specific set of signal-to-interference-plus-noise ratios, and non-real time users whose signal-to-interference-plus-noise ratios should be balanced and maximized. We propose a mixed QoS strategic noncooperative game wherein base stations determine their downlink beamformers in a fully distributed manner. In the case of infeasibility, we have proposed a fall back mechanism which converts the problem to a pure max-min optimization. We further propose the mixed QoS bargain game to improve the Nash equilibrium operating point through Egalitarian and Kalai-Smorodinsky bargaining solutions. We have shown that the results of bargaining games are comparable to that of the optimal solutions.

Optimized secondary user selection for quality of service enhancement of Two-Tier multi-user Cognitive Radio Network: A game theoretic approach

Cognitive Radio Networks (CRN) is the solution for the spectrum scarcity problem, that has surfaced with the exponential increase in number of mobile users. In CRN, the cognitive devices sense the spectrum, and transmits, on detecting an available channel. Thus CRN resourcefully utilize the idle spectrum bands. In CRN, the users are classified into two types - Primary users and Secondary users. Secondary users are further classified into two tiers- Real Time users (RT) and Non-Real Time users (NRT). Users from both the tiers share a single channel for transmission, resulting in efficient spectrum usage. However, with ever growing number of secondary mobile users having diverse QoS requirements, the cognitive interactions among them need to be optimized. This paper uses game theory to study the conflict and cooperation among the two tiers of secondary users. An auction game model is proposed that analyzes the complex decision making process and efficiently allocates an idle channel to a pair of RT and NRT secondary users from a pool of users. The game model is further extensively analyzed mathematically, using Markov chain, showing a reduction in user Blocking probability, user Dropping probability, Channel Saturation probability and increase in user Acceptance probability, compared to the Basic One-Tier CRN and Non-Optimized Two-Tier CRN. The optimized network model is then simulated using Qualnet 7.1. The simulation result shows improved Bandwidth Utilization Efficiency, Spectral efficiency and increased number of secondary users served, compared with respect to Basic One-Tier CRN and Non-Optimized Two-Tier CRN.

Energy-Saving Predictive Resource Planning and Allocation

Predictive resource allocation is an emerging approach to improve the performance of mobile systems as human behavior is reported predictable by leveraging big data analytics. Yet what information can be predicted by big data, what information need to be predicted for wireless access optimization, how to translate the information, and how to exploit the synthetic knowledge for allocating radio resources are not well understood and largely explored. In this paper, we are concerned with the latter two issues. In particular, we devise an energy-saving resource planning and allocation policy for multiple base stations (BSs) to serve mobile users with non-real-time (NRT) traffic by exploiting the user, network, and application levels of context information, where RT traffic may occupy partial resources of each BS. Inspired by the solution from an energy minimization problem with future instantaneous information, a low complexity multi-timescale predictive policy is proposed. Upon the arrival of each NRT user request, the resource planning is made with the user and network level context information, defined as the average channel gains of the NRT users and the statistics of residual bandwidth after serving RT traffic, with which the scheduling, power allocation, and BS sleeping can be accomplished after instantaneous channel information and residual network resource are available at each BS in each time slot. Simulation results show that the proposed policy can dramatically reduce the energy consumed by the BSs for serving the NRT traffic.

Optimization of user behavior based handover using fuzzy Q-learning for LTE networks

In LTE networks, handover optimization is necessary to enhance the users’ satisfaction. Specifically, users using real time traffic need to experience continuous connectivity. Hence, radio link failures (RLFs) severely affect their quality of experience. Decreasing the RLFs for non-real time users is not as urgent as the case of real time users. On the other hand, a total network collapse can happen in case of too much unnecessary handovers (ping-pongs). In this work, fuzzy Q-learning is used to optimize the two contradictory handover problems, which are RLFs and ping-pongs. The former needs to decrease Handover Margin (HOM) to reduce the too late handover, and the latter needs to increase the HOM to reduce the unnecessary signaling. In the developed algorithm, the users in the network are divided into four categories, according to their speed and the data traffic used. This increases the satisfaction of some users, while keeping the overall handover problems within acceptable limits. For each category of users, fuzzy Q-learning is applied with a different initial candidate fuzzy actions. The proposed technique shows the best performance for each category of users in terms of the most preferred metric, either decreasing RLF or decreasing ping-pongs, for this category of users in comparison with two other literature techniques, or without using any optimization technique. Moreover, the algorithm is robust against changes in the number of users in the system, as it maintains the best solution when the number of users is halved or even doubled.

A Cross-layer Scheduling Algorithm Based on Cognitive Radio Network

Cognitive radio network users have different QoS requirements. In order to provide different QoS requirements to users as well as increase the capacities of the cognitive radio networks, this paper presents a first in-depth comparative study on different factors effect QoS which include system performance, power interference, wireless channel conditions, services priorities and fairness constraints. A cross-layer scheduling algorithm which could optimize system performance and control power interference is proposed. Latency default probability of real-time users and throughput performance of nonreal-time users are respectively used to measure the fairness of real-time operational system and non-real-time operational system. Simulation results are computed and the results are shown. Delay performance of real-time traffic and throughput performance of non-real-time traffic are guaranteed by setting reasonable weight to index. And power interference to authorized users caused by cognitive radio network users reduces obviously. And system obtains a very good fair performance.

UTILITY BASED SCHEDULING AND CALL ADMISSION CONTROL FOR LONG TERM EVOLUTION NETWORKS

In this study, we propose to design a call admission control algorithm which schedules the channels for Real time and non-real time users. In Long Term Evolution (LTE) 3GPP Networks, several works were done on call admission control but these works rarely considers scheduling of resources to the real time and non-real time users.When the system meets traffic oriented performance degration, maximum resources are utilized for load balancing and to maintain the consistent quality. In order to avoid the channel degradation and improve the Quality of Service (QoS), the call requests are classified into New Call (NC) request and Handoff Call (HC) request and the type of services are classified as VoIP and video. Then based upon the Received Signal Strength (RSS) value, the channel is estimated as good channel or bad channel. Resource allocation is made for VoIP users based on traffic density. Then non-VoIP users and the non-real time users are allocated resource blocks using the channel condition based marginal utility function. When there are no sufficient resources to allocate, it allocates the resources of bad channel users there by degrading their service. We have designed the network topology with G (n) and B (n) for representing the available good and bad channels. We investigate the performance degradation when the real time, Non real Time, video and VOIP environments based on RSS threshold value.Comparison is made with the VOS in terms of the paramenters like throughput,bandwidth,delay,fairness and rate. Our proposed method provides good performance and quality.From our simulation results we show that this admission control algorithm provides channel quality and prioritizes the handover calls over new calls which allocates resources to all kinds of users.

Fast Optimal Resource Allocation is Possible for Multiuser OFDM-Based Cognitive Radio Networks with Heterogeneous Services

In this paper we study the resource allocation in multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks, where secondary users (SUs) have flexible traffic demands, including heterogeneous real-time (RT) and non-real-time (NRT) services. We try to maximize the sum capacity of the NRT users and maintain the minimal rate requirements of the RT users simultaneously. Additionally, the interference introduced to primary users, which is generated by the access of the SUs, should be kept below a predefined threshold, which makes the optimization task more complex. The contribution of this work is two folds. First, we show that the formulated optimization problem has a special structure which can be exploited to implement a fast barrier method to obtain the optimal solution with a reasonable complexity. Second, we propose an effective measurement criterion to normalize OFDM subchannels' achievable rates, based on which we develop simple but efficient heuristic algorithm for subchannel assignment and power distribution. Simulation results show that our proposed resource allocation schemes work quite well for concerned wireless scenarios. The fast barrier method converges very fast and can always work out the optimal solution, while the heuristic algorithm produces solution close to the optimal with much lower complexity.

Utility Based Scheduling and Call Admission Control for LTE (3GPP) Networks

In this paper, we propose to design a call admission control algorithm which schedules the channels for real time and non-real time users based on Utility function. In Long Term Evolution (LTE) 3GPP Networks, several works were done on call admission control but these works rarely considers scheduling of resources to the real time and non-real time users. The call requests are classified into new call (NC) request and handoff call (HC) request and the type of services are classified as VoIP and video. Then based upon the received signal strength (RSS) value, the channel is estimated as good channel or bad channel. Resource allocation is made for VoIP users based on traffic density. Then non-VoIP users and the non-real time users are allocated resource blocks using the channel condition based marginal utility function. When there are no sufficient resources to allocate, it allocates the resources of bad channel users there by degrading their service. Thus from our simulation results we show that this admission control algorithm provides channel quality and prioritizes the handover calls over new calls which allocates resources to all kinds of users.

QoS Provisioning for Heterogeneous Services in Cooperative Cognitive Radio Networks

In this paper, we propose a spectrum allocation framework that jointly considers the Quality-of-Service (QoS) provisioning for heterogeneous secondary Real-Time (RT) and Non-Real Time (NRT) users, the spectrum sensing, spectrum access decision, channel allocation, and call admission control in distributed cooperative Cognitive Radio Networks (CRNs). Giving priority to the RT users with QoS requirements in terms of the dropping and blocking probabilities, a number of the identified available channels are allocated to the optimum number of the RT users that can be admitted into the network, while the remaining identified available channels are allocated adaptively to the optimum number of the NRT users considering the spectrum sensing and utilization indispensability. Extensive analytical and simulation results are provided to demonstrate the effectiveness of the proposed QoS-based spectrum resource allocation framework.

Adaptive resource allocation for multi-user multi-server power-line communication OFDM systems

The bits and power allocation model of adaptive power-rate mixture for multi-user multi-server power-line communication systems was analyzed with the restrictions of maximal total power, fixed rate for each real time (RT) user, minimal rate for each non-real time (NRT) user, maximal bits and power for each subcarrier in each orthogonal frequency division multiplexing (OFDM) symbol. An algorithm of resource dynamic allocation in the first OFDM symbol of each frame and resource optimal adjustment in the latter OFDM symbol of each frame was proposed. In the first OFDM symbol of every frame, resource is firstly assigned for RT users so as to minimize their total used power until satisfying their fixed rates; secondly the remainder resource of power and subcarriers are assigned for NRT users so as to minimize their total used power until satisfying their minimal rates also; lastly the remainder resource is again assigned for NRT users according to the proportional fairness strategy so as to maximize their total assigning rate. In the latter OFDM symbol of each frame, bits are swapped and power is adjusted for every user based on the resource allocation results of anterior OFDM symbol. The algorithm is tested in the typical power-line channel scenarios and the simulation results indicate that the proposed algorithm has better performances than the classical multi-user resource allocation algorithms and it realizes the multiple aims of multi-user multi-server resource allocation for power-line communication systems.

Neuro-Fuzzy based Joint Relay-Selection and Resource-Allocation for Cooperative Networks

This paper focuses on a joint relay-selection and resource-allocation algorithm for an Amplify-and-Forward (AF) cooperative network. In a multiuser scenario, joint relay selection and power allocation is a combinational problem for heterogeneous, i.e., real time (RT) and non-real time (NRT), users. In single relay AF (S-AF) scheme, a source-destination pair selects best relay. Thus only two channels are needed (i.e., one for source-destination direct link and other one for the source-relay-destination indirect link) between a source-destination pair. We propose a Neuro Fuzzy (NF) based optimal relay selection algorithm for selecting best relay based on link's signal-to-noise ratio (SNR), link's delay and degree of mobility between a source-destination pair. The available radio resources are then allocated sub-optimally to the RT and NRT users on priority basis. The priority parameter depends on Quality-of-service (QoS) requirement of the RT and NRT users. We deduce a close form expression of the moment-generating-function (MGF) for independent and non identical Rayleigh fading channels. Performance evaluations reveal that the proposed joint scheme has lower complexity and better outage behavior as compared to the conventional schemes.

Optimal Resource Allocation Based on Resource Factor for Power-Line Communication Systems

The optimal multilayer, multiobjective resource allocation model for multiuser and multitraffic orthogonal frequency-division multiplexing (OFDM)-based power-line communication (PLC) systems is analyzed with the restrictions of maximal total power, constant rates for every real time (RT) user, minimal rates for every non-real time (NRT) user, maximal upper limit of power and number of bits for every subcarrier in each OFDM symbol. A bit-loading lookup table resource allocation algorithm with rate- and margin-adaptation is proposed based on resource factor, which first assigns fairly Pareto non-dominated resources for all RT users according to resource factor so as to attain their constant rates, second computes the relevant remaining power and non-used subcarriers for every Pareto solution and assigns fairly them for all NRT users so as to attain their minimal rates, third also computes the relevant remaining resources and assigns them so as to achieve the maximal sum of the allocated rates for all NRT users, lastly selects out the globally optimal solution according to the order of partial information. Based on the typical power-line channel, the simulation results illustrate that the proposed algorithm outperforms the existed multiuser bit-loading greedy algorithm and it realizes better the multiple aims of resource allocation in OFDM-based PLC systems.

Scheduling for real/non-real time traffic in wireless packet networks

In this paper,a wireless scheduling scheme for a mixture of Real/Non-Real Time traffic,called TF-RNS,was proposed.The objective of TF-RNS is to provide temporal fairness among heterogeneous users while improving the quality of service(QoS)of each user.Considering that Real and Non-Real Time users had different QoS requirements,TF-RNS was designed as a hierarchical scheduling scheme.First,TF-RNS used M-LWDF and PF to make scheduling for Real and Non-Real Time users respectively.Then,TF-RNS used our designed scheme SFQ-CS to provide temporal fairness.Simulation results show that compared with M-LWDF and DS-PS,TF-RNS can provide excellent temporal fairness for Real/Non-Real Time users and then effectively guarantee the QoS of all the users in the system.

Adaptive subcarrier and bit allocation in OFDMA systems supporting heterogeneous services

Orthogonal Frequency Division Multiple Access (OFDMA) is an efficient multiple access method for the future wireless systems. This paper studies the adaptive subcarrier and bit allocation problem in OFDMA systems to support heterogeneous services. The goal of the considered resource optimization technique is to maximize the total system throughput under the overall transmit power constraint while guaranteeing the QoS requirement of realtime users and supporting proportional fairness among non-realtime users. First, we introduce a Rate Adaptive (RA) resource allocation algorithm for non-realtime users and a Margin Adaptive (MA) algorithm for realtime users. Then, based on the previous algorithms, a novel algorithm is proposed to allocate the resource to both classes of users, which makes an efficient tradeoff between the resource usage of realtime users and non-realtime users. The algorithm is locally optimal solution provided that the MA and RA algorithms are utilized. Also, to reduce the computational complexity, a suboptimal method based on the balancing of the average power per subcarrier is also introduced. Monte Carlo simulation results show that all the proposed algorithms outperform the existing counterparts. The results also show that the suboptimal method for heterogeneous services can efficiently reduce the computational complexity at the cost of very little performance degradation.

Traffic Aided Uplink Opportunistic Scheduling with QoS Support in Multiservice CDMA Networks

In this letter, we address the problem of resource allocation with efficiency and quality of service (QoS) support in uplink for a wireless CDMA network supporting real-time (RT) and non-realtime (NRT) communication services. For RT and NRT users, there are different QoS requirements. We introduce and describe a new scheme, namely, traffic aided uplink opportunistic scheduling (TAUOS). While guaranteeing the different QoS requirements, TAUOS exploits the channel condition to improve system throughput. In TAUOS, the cross-layer information, file size information, is used to improve fairness for NRT users. Extensive simulation results show that our scheme can achieve high system throughput in uplink wireless CDMA systems, while guaranteeing QoS requirements.