Resource Block Scheduling Research Articles

Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

Queue-Aware Resource Allocation in Full-Duplex Multi-Cellular Wireless Networks

In this paper, we aim to tackle the challenges of resource block scheduling and power allocation in the context of multi-cell full-duplex wireless networks. This is a more realistic setting than the single cell scenario, and it better envisions how full-duplex wireless communications could eventually be implemented. We propose an optimal queue-aware joint scheduling and power allocation algorithm for full-duplex wireless networks in a multi-cell scenario. Because of its mathematical intractability, we decouple the problem and solve it for scheduling first, and for power allocation second. We consider both indoor and outdoor scenarios and show that the gains of multi-cell full-duplex wireless networks, with respect to their half-duplex counter-parts, are not always prevalent. Furthermore, we highlight the importance of inter-cell cooperation when it comes to scheduling resources and show that depending on the scenario at hand, interference mitigation from inter-cell cooperation can improve the performance of user equipment in terms of throughput and waiting delay. Finally, we show that power allocation can improve user equipment throughput with its efficiency being tied to the deployment scenario at hand.

Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communications

This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-to-everything communications. To provide minimal AoI and high reliability for vehicles' safety information, NOMA is exploited. We reformulate a resource allocation problem that involves half-duplex transceiver selection, broadcast coverage optimization, power allocation, and resource block scheduling. First, to obtain the optimal solution, we formulate the problem as a mixed-integer nonlinear programming problem and then study its NP-hardness. The NP-hardness result motivates us to design simple solutions. Consequently, we model the problem as a single-agent Markov decision process to solve the problem efficiently using fingerprint deep reinforcement learning techniques such as deep-Q-network (DQN) methods. Nevertheless, applying DQN is not straightforward due to the curse of dimensionality implied by the large and mixed action space that contains discrete and continuous optimization decisions. Therefore, to solve this mixed discrete/continuous problem efficiently, simply and elegantly, we propose a decomposition technique that consists of first solving the discrete subproblem using a matching algorithm based on state-of-the-art stable roommate matching and then solving the continuous subproblem using DRL algorithm that is based on deep deterministic policy gradient DDPG. We validate our proposed method through Monte Carlo simulations where we show that the decomposed matching and DRL algorithm successfully minimizes the AoI and achieves almost 66% performance gain compared to the best benchmarks for various vehicles' speeds, transmission power, or packet sizes. Further, we prove the existence of an optimal value of broadcast coverage at which the learning algorithm provides the optimal AoI.

Deep-Reinforcement-Learning-Based Proportional Fair Scheduling Control Scheme for Underlay D2D Communication

In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users' fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep Q-network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.

Enhancement of Proportional Scheduling in LTE Using Resource Allocation Based Proposed Technique

LTE (Long Term Evolution) is a standard for 4G remote broadband innovations that offers enhanced network capacity and speed to mobile device users. LTE offers higher information exchange rates up to 100 Mbps downstream and 30 Mbps upstream. It also provides reduced latency, scalable bandwidth capacity and backward-compatibility with existing GSM and UMTS technology. Future developments could yield peak throughput on the order of 300 Mbps. LTE system allocates radio resources for different type of services such as real time (RT) services, non-real time (NRT) services, control signaling through a radio resource manager, and these services require dynamic resource allocation for transmitting corresponding data packets. A scheduler in radio resource manager needs to satisfy demand of these services with limited radio resources. Therefore, scheduler should be efficient in performing radio resource allocation in such dynamic environment. In this paper we are working on resource block scheduling in LTE system and proposed an algorithm to allocate resource blocks so that user’s rate requirement can be fulfilled optimally and throughput of the system can be improved. The overall performance of proposed technique is better and it enhances the network throughput up to 9.5.

Analytical Calculation of Spectrum Requirements for LTE-A Using the Probability Distribution on the Scheduled Resource Blocks

Recently, analytical calculations of spectrum requirements for 4G/5G systems have gained much attention from mobile industry and radio authority worldwide. Resource block (RB) scheduling based on devices’ channel conditions is important to guarantee quality of service in such systems. However, despite these attention and importance, there have been scarce works on stochastic modeling for the number of scheduled RBs in calculating spectrum requirements. Thus, in this letter, we derive a probability distribution of the number of scheduled RBs using 2-D Markov chain model, and present a calculation method and examples for LTE-A spectrum requirements based on the derived distribution.

PROPOSING ENHANCED NA GAUN TECHNIQUE (ENGT) FOR RESOURCE BLOCK ALLOCATION IN LTE(LONG TERM EVOLUTION) SYSTEMS FOR IMPROVING QUALITY OF SERVICE

Due to its distinct and unique features viz. high speed data rate, self-organized network, packet optimized radio access, low latency and flexible bandwidth deployment LTE (Long Term Evolution) is more preferred in wireless communication. Such kind of terminology uses especially in mobile phones for improving its throughput. LTE uses Orthogonal Frequency Division Multiplexing (OFDM) for the downlink - that is, from base station to the terminal to transmit the data over many narrow band carriers each of 180 KHz instead of spreading one signal over the complete 5MHz career bandwidth. The complete working of LTE is based on the basic principle of OFDMA (Orthogonal Frequency Division Multiplexing). At first step, it divides the broad spectrum into multiple narrow bands and transmits information on this narrow channel in parallel.OFDM meets the LTE requirement for spectrum flexibility and enables cost-efficient solutions for very wide carriers with high peak rates.Orthogonal frequency-division multiplexing (OFDM), is a frequency-division multiplexing (FDM) scheme used as a digital multi-carrier modulation method. Scheduling plays a vital role on the time of resource allocation which has become an essential component for high- speed wireless data systems. This allocation of resources is done according to the need and priority of the user. It has three key components which are involved in the resource allocation and network optimization: resource block scheduling, power control, and client association. The motive to design this new designed methodology is to increase its privacy rate by increasing its security level that automatically improves its speed and data rate. LTE uses OFDM (orthogonal frequency division multiplexing) for downlink transmission. In resource allocation algorithms in OFDM of LTE systems, the allocation of the radio resource has to be in the units of Scheduling Block (SB) and there exists the need for all SBs of each user to use the same Modulation and Coding Scheme (MCS) leading to the degradation of performance. Main challenge for LTE systems is that, by applying OFDMA, multiple resource blocks can be scheduled by a transmission over the frequency and time. In this research paper, we have to focus on resource block scheduling in LTE system and proposes an enhanced version of Na Gaun technique that would be helpful for resource allocation blocks so that user’s rate requirement can be fulfilled optimally and throughput of the system can be improved.

Performance Analysis and Optimisation of FFR-Aided OFDMA Networks using Channel-Aware Scheduling

Modern cellular standards typically incorporate interference coordination schemes allowing near universal frequency reuse while preserving reasonably high spectral efficiencies over the whole coverage area. In particular, fractional frequency reuse (FFR) and its variants are deemed to play a fundamental role in the next generation of cellular deployments (B4G/5G systems). This paper presents an analytical framework allowing the downlink performance evaluation of FFR-aided OFDMA networks when using channel-aware scheduling policies. Remarkably, the framework contemplates the use of different rate allocation strategies, thus allowing to assess the network behaviour under ideal (capacity-based) or realistic (throughput-based) conditions. Analytical performance results are used to optimise the FFR parameters as a function of, for instance, the resource block scheduling policy or the density of UEs per cell. Furthermore, different optimisation designs of the FFR component are proposed that allow a tradeoff between throughput performance and fairness by suitably dimensioning the FFR-defined cell-centre and cell-edge areas and the corresponding frequency allocation to each region. Numerical results serve to confirm the accuracy of the proposed analytical model while providing insight on how the different parameters and designs affect network performance.

Analysis and Optimization of FFR-Aided OFDMA-Based Heterogeneous Cellular Networks

Two-tier networks combining an operator-managed infrastructure of macrocell base stations combined with a user-deployed network of femtocells have recently emerged in the context of modern wireless standards as a solution to meet the ambitious performance requirements envisaged in 4G/5G architectures. Most often, these systems require interference coordination schemes that allow near universal frequency reuse while maintaining a considerably high signal-to-interference-plus-noise ratio levels across the coverage area. In particular, fractional frequency reuse (FFR) and its variants are deemed to play a fundamental role in the next generation of cellular systems. This paper develops an analytical framework targeting the downlink performance evaluation of FFR-aided orthogonal frequency division multiple access-based two-tier heterogeneous networks. In the considered scenario, macrocell and femtocell tiers are assumed to be uncoordinated and co-channel deployed, thus representing a worst-case scenario in terms of inter-tier interference. The proposed framework allows the evaluation of the impact produced by both inter- and co-tier interferences on the performance of either the macro-users (MUs) or the femto-users. Analytical results are used to optimize the FFR parameters as a function of, for example, the density of MUs per cell, the resource block scheduling policy, the density of femto base stations per area unit, or the degree of isolation provided by wall penetration losses. Moreover, different optimization designs of the FFR component are proposed that allow a tradeoff between throughput performance and fairness by suitably dimensioning the FFR inner and outer areas and the corresponding frequency allocation.

Towards an Application-Aware Resource Scheduling With Carrier Aggregation in Cellular Systems

In this paper, we introduce an application-aware approach for resource block scheduling with carrier aggregation in Long Term Evolution Advanced (LTE-Advanced) cellular networks. In our approach, users are partitioned in different groups based on the carriers coverage area. In each group of users, users equipments (UE)s are assigned resource blocks (RB)s from all in band carriers. We use a utility proportional fairness (PF) approach in the utility percentage of the application running on the UE. Each user is guaranteed a minimum quality of service (QoS) with a priority criterion that is based on the type of application running on the UE. We prove that our scheduling policy exists and therefore the optimal solution is tractable. Simulation results are provided to compare the performance of the proposed RB scheduling approach with other scheduling policies.

Downlink Scheduling in Long Term Evolution

This is an investigated research article on resource block scheduling of Long Term Evolution (LTE). LTE is one of the evolutions of the Universal Mobile Telecommunication System (UMTS). It provides internet access to mobile users through smart phone, laptop and other android devices. LTE offers a high speed data and multimedia services. It supports data rates up to 100 Mbps in the downlink and 50 Mbps in the uplink transmission. Our research investigation was aim to the downlink scheduling. We have considered The Best CQI scheduling algorithm and the Round Robin scheduling algorithm. The implementation, analysis and comparison of these scheduling algorithms have been performed through MATLAB simulator. We have analyzed the impact of the scheduling schemes on the throughput and the fairness of both scheduling schemes. Here we have proposed a new scheduling algorithm that achieves a compromise between the throughput and the fairness. Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the downlink transmission scheme. We have considered the impact of the channel delay on the throughput. In addition, MIMO transceiver systems have been implemented to increase the throughput.

Radio Resource Management of CoMP System in HetNet under Power and Backhaul Constraints

Recently, Heterogeneous Network (HetNet) with Coordinated Multi-Point (CoMP) scheme is introduced into Long Term Evolution-Advanced (LTE-A) systems to improve digital services for User Equipments (UEs), especially for cell-edge UEs. However, Radio Resource Management (RRM), including Resource Block (RB) scheduling and Power Allocation (PA), in this scenario becomes challenging, due to the intercell cooperation. In this paper, we investigate the RRM problem for downlink transmission of HetNet system with Joint Processing (JP) CoMP (both joint transmission and dynamic cell selection schemes), aiming at maximizing weighted sum data rate under the constraints of both transmission power and backhaul capacity. First, joint RB scheduling and PA problem is formulated as a constrained Mixed Integer Programming (MIP) which is NP-hard. To simplify the formulation problem, we decompose it into two problems of RB scheduling and PA. For RB scheduling, we propose an algorithm with less computational complexity to achieve a suboptimal solution. Then, according to the obtained scheduling results, we present an iterative Karush-Kuhn- Tucker (KKT) method to solve the PA problem. Extensive simulations are conducted to verify the effectiveness and efficiency of the proposed algorithms. Two kinds of JP CoMP schemes are compared with a non-CoMP greedy scheme (max capacity scheme). Simulation results prove that the CoMP schemes with the proposed RRM algorithms dramatically enhance data rate of cell-edge UEs, thereby improving UEs fairness of data rate. Also, it is shown that the proposed PA algorithms can decrease power consumption of transmission antennas without loss of transmission performance.

Resource allocation for device-to-device communications underlaying LTE-advanced networks

The Long Term Evolution-Advanced (LTEAdvanced) networks are being developed to provide mobile broadband services for the fourth generation (4G) cellular wireless systems. Deviceto- device (D2D) communications is a promising technique to provide wireless peer-to-peer services and enhance spectrum utilization in the LTE-Advanced networks. In D2D communications, the user equipments (UEs) are allowed to directly communicate between each other by reusing the cellular resources rather than using uplink and downlink resources in the cellular mode when communicating via the base station. However, enabling D2D communications in a cellular network poses two major challenges. First, the interference caused to the cellular users by D2D devices could critically affect the performances of the cellular devices. Second, the minimum quality-of-service (QoS) requirements of D2D communications need to be guaranteed. In this article, we introduce a novel resource allocation scheme (i.e. joint resource block scheduling and power control) for D2D communications in LTE-Advanced networks to maximize the spectrum utilization while addressing the above challenges. First, an overview of LTE-Advanced networks, and architecture and signaling support for provisioning of D2D communications in these networks are described. Furthermore, research issues and the current state-of-the-art of D2D communications are discussed. Then, a resource allocation scheme based on a column generation method is proposed for D2D communications. The objective is to maximize the spectrum utilization by finding the minimum transmission length in terms of time slots for D2D links while protecting the cellular users from harmful interference and guaranteeing the QoS of D2D links. The performance of this scheme is evaluated through simulations.