Uplink Resource Research Articles

Spectral Efficiency Improvement of Full-Duplex D2D Communication in Cellular Networks

Radio spectrum is becoming scarce due to increasing demand for high data rate, mobile communication and ever-expanding population with diverse need to always stay interconnected. The cellular network providers and researchers in the academia are continually finding innovative ways to efficiently manage the existing telecommunication infrastructure and plan effective for future expansions and technology. Device to Device (D2D) communication, Full-Duplex (FD) radio, Heterogeneous network are a few of such innovative technologies developed to face the challenges. Device to device communication is one promising technology that is studied for deployment in future network technologies, however it is not without its challenges. Various researches have been carried out and are still being carried out to better understand and improve device to device capabilities. The use of full-duplex radios is an area of study with capability for improving device to device communication due to recent development in full-duplex radio although its major drawback is limited self-interference cancellation abilities to be deployed in large transmit power system. This research presents the practicality of deploying existing FD radios in device to device communication and simulate the amount of self-interference cancellation required using MATLAB for effective use with device to device. Two interference management schemes were implemented to improve the performance of FD-D2D communication, first power control scheme was developed to mitigate interference between D2D and base station in uplink resource sharing, Interference Limited Area (ILA) method was adapted to deal with interference between D2D and cellular user Uplink and Downlink transmission. The performance between 75dB to 110dB Self-Interference (SI) cancellation was carried out. The result was compared to conventional cellular and Half-Duplex D2D communication to estimate the improvement offered on spectral efficiency. The work has improved on achieving almost 100% spectral efficiency thereby improving the Quality of Service (QoS) for cellular network

Joint uplink and downlink delay‐aware resource allocation in C‐RAN

AbstractThis work considers two‐way communication between each pair of users with highly delay‐aware applications. We formulate a joint uplink and downlink resource allocation problem in a cloud radio access network. Assuming average end‐to‐end (E2E) delay of each user pair and practical limitation such as maximum transmit power, we maximize the total throughput of all pair of users in the cloud radio access network. In this setup, we consider that each user can be connected to at most one remote radio head and a limited capacity fronthaul link between each remote radio head and baseband unit. To present the resource allocation problem in a more tractable manner, we replace the E2E delay limitation with its equivalent throughput‐based formulation. Due to inherent NP‐hard and nonconvex nature of the proposed problem, we apply successive convex approximation to reach a two‐step iterative algorithm where, in each step, a specific set of optimization variable derived while other variables are fixed. The problem of each step is transformed into the standard geometric programming via the arithmetic‐geometric mean approximation. Simulation results reveal that our proposed joint uplink‐downlink resource allocation algorithm outperforms a case that uplink and downlink resources are allocated separately in terms of total throughput and outage probability of E2E delay, ie, a chance that E2E delay does not hold.

Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems

Ensuring an effective coexistence of conventional broadband cellular users with machine type communications (MTCs) is challenging due to the interference from MTCs to cellular users. This interference challenge stems from the fact that the acquisition of channel state information (CSI) from machine type devices (MTD) to cellular base stations (BS) is infeasible due to the small packet nature of MTC traffic. In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between MTC and conventional cellular communications. In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives data from a large set of MTDs. The BS and MTA share the same uplink resources, and, therefore, MTD transmissions create interference on the BS. However, if there is a large number of MTDs to chose from for transmission at each given time for each beamformer, one MTD can be selected such that it causes almost no interference on the BS. A comprehensive analytical study of the characteristics of such an interference from several MTDs on the same beamformer is carried out. It is proven that, for each beamformer, an MTD exists such that the interference on the BS is negligible. To further investigate such interference, the distribution of the signal-to-interference-plus-noise ratio (SINR) of the cellular user is derived, and, subsequently, the distribution of the outage probability is presented. However, the optimal implementation of OSO requires the CSI of all the links in the BS, which is not practical for MTC. To solve this problem, an online learning method based on the concept of contextual multi-armed bandits (MAB) learning is proposed. The receive beamformer is used as the context of the contextual MAB setting and Thompson sampling: a well-known method of solving contextual MAB problems is proposed. Since the number of contexts in this setting can be unlimited, approximating the posterior distributions of Thompson sampling is required. Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. Simulation results show that is possible to implement OSO with no CSI from MTDs to the BS. Linear full posterior sampling achieves almost 90% of the optimal allocation when the CSI from all the MTDs to the BS is known.

Interference Management in In-Band D2D Underlaid Cellular Networks

Recently, it has been standardized by the 3rd generation partnership project (3GPP) that device-to-device (D2D) communications should use uplink resources when coexisting with conventional cellular communications. With uplink resource sharing, both cellular and D2D links cause significant co-channel interference. In this paper, we address the critical issue of interference management in the network considering a practical path loss model incorporating both line-of-sight (LoS) and non-LoS (NLoS) transmissions. To reduce the severe interference caused by active D2D links, we consider a mode selection scheme based on the maximum received signal strength (MRSS) for each user equipment (UE) to control the D2D-to-cellular interference. Furthermore, we analyze the performance in terms of the coverage probability and the area spectral efficiency (ASE) for both the cellular network and the D2D one. Analytical results are obtained and the accuracy of the proposed analytical framework is validated through Monte Carlo simulations. Through our theoretical and numerical analyses, we quantify the performance gains brought by D2D communications in cellular networks and we find an optimum mode selection threshold $\beta $ to maximize the total ASE in the network. This insight is expected to provide a design guideline for D2D mode selections.

An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks

In future wireless communication, a large number of devices equipped with several different types of sensors will require access networks with diverse quality-of-service constraints. In cellular network evolution, the long term evolution advanced (LTE-A) networks has standardized Machine-to-Machine (M2M) features. Such M2M technology can provide a promising infrastructure for Internet of things (IoT) sensing applications, which usually require real-time data reporting. However, LTE-A is not designed for directly supporting such low-data-rate devices with optimized energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. This paper investigate the maximum energy efficient data packets M2M transmission with uplink channels in LTE-A network. We formulate it into a joint problem of Modulation-and-Coding Scheme (MCS) assignment, resource allocation and power control, which can be expressed as a non-deterministic polynomial hard (NP-hard) mixed-integer linear fractional programming problem. Then we propose a global optimization scheme with Charnes-Cooper transformation and Glover linearization. The numerical experiment results show that with limited resource blocks, our algorithm can maintain low data packets dropping ratios while achieving optimal energy efficiency for a large number of M2M nodes, comparing with other typical counterparts.

Spectrum Sharing Among Rapidly Deployable Small Cells: A Hybrid Multi-Agent Approach

On-demand deployment of small cells plays a key role in augmenting macro-cell coverage for outdoor hotspots, where user devices are brought together and intensively upload self-generated data. In this paper, we study spectrum sharing among rapidly deployable small cells in the uplink, even without a priori global knowledge. We propose a hybrid multi-agent approach, which allows a leading macro-cell base station (MBS) and multiple following small base stations (SBSs) to take part in a user-centric, online joint optimization of small cell deployment and uplink resource allocation. Specifically, we propose a centralized mechanism for the MBS to solve the first subproblem of small cell deployment stage by stage, based on an adversarial bandit model. Furthermore, we propose a distributed mechanism for the group of SBSs to collectively solve the second subproblem of uplink resource allocation stage by stage, based on a stochastic game model. We prove that our approach is guaranteed to produce a joint strategy, which is built upon a mixed strategy with bounded regret on the first tier and an equilibrium solution on the second tier. Our approach is validated by simulations on the aspects of convergence behavior, strategy correctness, power consumption, and spectral efficiency.

Uplink resource allocation for multiple access computational offloading

The mobile edge computing framework offers the opportunity to reduce the energy that devices must expend to complete computational tasks. The extent of that energy reduction depends on the nature of the tasks, and on the choice of the multiple access scheme. In this paper, we first address the uplink communication resource allocation for offloading systems that exploit the full capabilities of the multiple access channel (FullMA). For indivisible tasks we provide a closed-form optimal solution of the energy minimization problem when a given set of users with different latency constraints are offloading, and a tailored greedy search algorithm for finding a good set of offloading users. For divisible tasks we develop a low-complexity algorithm to find a stationary solution. To highlight the impact of the choice of multiple access scheme, we also consider the TDMA scheme, which, in general, cannot exploit the full capabilities of the channel, and we develop low-complexity optimal resource allocation algorithms for indivisible and divisible tasks under that scheme. The energy reduction facilitated by FullMA is illustrated in our numerical experiments. Further, those results show that the proposed algorithms outperform existing algorithms in terms of energy consumption and computational cost.

Interference-aware channel assignment and power allocation for device-to-device communication underlaying cellular network

Device-to-device (D2D) communication is a promising technology in 5G new radio (NR) interface to improve the system performance in terms of high data rate, low latency, coverage extension, traffic offloading, and higher spectrum and energy efficiency. In this study, channel assignment and power allocation techniques for D2D communication underlaying cellular network are proposed, those in turn, lead to a spectrum-efficient system. Firstly, the channel assignment problem is solved by employing channel-characteristics of target link and interference links. Then the power allocation problem is solved by allocating the optimal power to the D2D transmitters with allowable interference. This optimal power allocation approach allows multiple D2D communications to reuse the available cellular uplink resources. Numerical results demonstrate significant throughput gains for D2D communication for different transmission and interference power settings. Network throughput comparison with other existing approaches is provided to confirm the efficacy of the proposed approach. In addition, energy efficiency analysis is carried out by considering D2D transmission power, circuit power consumption, D2D link distance, and D2D user density.

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

AbstractNowadays, Long‐Term Evolution Advanced (LTE‐A) network is the primary innovation in 4G networks. The LTE‐A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE‐A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0‐1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0‐1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)–based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE‐A network than state‐of‐art methods in the proposed strategy.

Joint Allocation of Radio and Fronthaul Resources in Multi-Wavelength-Enabled C-RAN Based on Reinforcement Learning

Cloud radio access network (C-RAN) is a key technology for the new-generation mobile network (5G). Multi-wavelength passive optical networks (PONs) such as wavelength division multiplexing (WDM) and time wavelength division multiplexing (TWDM) PONs are outstanding solutions for providing a sufficient bandwidth to support mobile fronthaul in C-RAN-based 5G architecture. In this paper, a joint allocation framework for multi-wavelength PONs fronthaul uplink resources and C-RAN radio interface uplink resources is presented. From the principle that the uplink resource allocation in mobile networks is an NP-hard optimization problem, this paper proposes a novel uplink scheduling algorithm based on reinforcement learning (RL). The performance of the algorithm is evaluated with numerical simulations and compared with two other algorithms from the literature; namely genetic algorithm (GA) and tabu search (TS) algorithm. The simulation results show that the new algorithm achieves higher system throughput, faster convergence and lower scheduling latency compared to the two other algorithms. Also, show that RL-based adaptive allocation of fronthaul transport block size based on actual radio resource block capacity can significantly reduce the capacity required on fronthaul link and decrease the total end to end uplink scheduling latency compared to the static allocation method.

Three-Dimensional Resource Allocation in D2D-Based V2V Communication

Device-to-Device (D2D) communication is the major enabler of Vehicle-to-Everything communication in 3rd Generation Partnership Project (3GPP) Release 14. The user equipment/device can engage either in direct communication with the infrastructure, use a relay node, or it can communicate directly with another device with or without infrastructure support. The user equipment can be either a hand-held cellular device or a moving vehicle. The coexistence of cellular user equipment with the vehicular user equipment imposes different Quality of Service (QOS) requirements due to the rapid mobility of the vehicles and interference. Resource allocation is an important task by which the user equipment is allocated the required resources based on different QOS parameters. In this paper, we introduced the case of three types of users which share uplink resources: two types of vehicular users, and a third user that acts as a handheld cellular phone, which is nearly static. By keeping in mind, the differential QOS requirements for the three types of users, we have calculated the optimum power and then applied a 3-dimensional graph-based matching and hypergraph coloring based resource block (RB) allocation.

Uplink resource allocation for multicarrier grouping cognitive internet of things based on K-means Learning

Integrating cognitive radio into internet of things (IoT), cognitive IoT (CIoT) can achieve more available spectrum resources by accessing the licensed frequency bands of primary user (PU), providing the communication of the PU is not disturbed. In this paper, a multicarrier grouping CIoT is proposed to decrease the interference to the PU, where the CIoT nodes are divided into several groups by K-means Learning and each group transmits data to the base station in the allocated time slot. In the underlay case, the uplink resource allocation is formulated as an optimization problem, which seeks to maximize the transmission rate of the CIoT under the constraint of the interference power to the PU, by jointly allocating subcarrier and power for each node. The optimization problem is solved by group power optimization and node power optimization. The upper and lower bounds of the optimal number of groups are calculated. In the overlay case, the lower bound of sensing time is achieved to guarantee the spectrum sensing performance, and the transmission rate of the CIoT is maximized without considering any interference. The simulations show the predominance of the multicarrier grouping CIoT under the strict interference restriction.

Learning-Based Energy-Efficient Resource Management by Heterogeneous RF/VLC for Ultra-Reliable Low-Latency Industrial IoT Networks

Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.

Joint Uplink and Downlink Transmissions in User-Centric OFDMA Cloud-RAN

This paper studies joint uplink (UL) and downlink (DL) resource allocation in user-centric orthogonal frequency division multiple access cloud radio access network (CRAN), where the users select the distributed remote radio heads (RRHs) to cooperatively serve their UL and DL transmissions over different subcarriers (SCs). The goal of this paper is to maximize the system throughput through jointly optimizing UL/DL scheduling, SC assignment, RRH grouping/clustering, and power allocation under the maximum power and fronthaul capacity constraints. The problem is formulated as a mixed integer programming problem, which is nonconvex and NP-hard. We propose an efficient algorithm based on the Lagrange duality method to obtain an asymptotically optimal solution for this problem. A heuristic algorithm is further proposed to reduce the complexity. Simulation results illustrate that the proposed heuristic algorithm also has a close-to-optimal performance, and the proposed algorithms can considerably improve the system throughput compared to other benchmark schemes.

Dynamic Time Division Duplexing for Downlink/Uplink Decoupled Millimeter Wave-Based Cellular Networks

The upcoming 5G cellular network’s standard from 3GPP is expected to support the millimeter-wave (mmwave)-based small cell base stations (SCBSs) to increase the data rate and network capacity. Furthermore, the downlink/uplink decoupling (DUDe) has been proposed in 5G to compensate for the transmit power imbalance between the macro-base stations and SCBSs. In this letter, joint uplink and downlink scheduling and resource allocation in a dynamic time division duplex (TDD) system is formulated as an optimization problem. Given a generalized $\alpha $ -fair scheduler, the dynamic TDD and the user scheduling time fractions are derived for a relaxed problem. The derived dynamic TDD results are shown to be a function of the system load. Simulation results are presented, which match with the derived results and show 17% gain in throughput for certain scenarios.

Energy Efficient Resource Allocation for M2M Devices in 5G.

Resource allocation for machine-type communication (MTC) devices is one of the keys challenges in the 5G network as it affects the lifetime of battery powered devices and also the quality of service of the applications. MTC devices are battery restrained and cannot afford a lot of power consumption due to spectrum usage. In this paper, we propose a novel resource allocation algorithm termed threshold controlled access (TCA) protocol. We propose a novel technique of uplink resource allocation in which the devices make a decision of resource allocation blocks based on their battery status and related application’s power profile that eventually leads to required quality of service (QoS) metric. The first phase of the TCA algorithm selects the number of carriers to be allocated to a certain device for the better lifetime of low power MTC devices. In the second phase, the efficient solution is implemented through inducing a threshold value. A certain value of the threshold is selected through a mapping based on a QoS metric. The threshold enhances the selection of subcarriers for less powered devices, such as small e-health sensors. The algorithm is simulated for the physical layer of the 5G network. Simulation results show that the proposed algorithm is less complex and achieves better performance when compared to existing solutions in the literature.

Machine-to-Machine Communications With Massive Access: Congestion Control

With the deployment of machine-to-machine (M2M) communications, it is expected that the number of devices will enormously increase. When these devices attempt to concurrently access the network, a radio access network overload problem arises. In this case, the conventional random access procedure used in Long Term Evolution-Advanced (LTE-A) networks is rendered inefficient due to the frequent collisions that lead to excessive delay and resource wastage. In this paper, we propose an efficient scalable overload control algorithm for M2M with massive access. The proposed algorithm can allocate the uplink resources within bounded contention time in a distributed manner. Hence, it can achieve full resource utilization that leads to reduced: access delay, energy consumption, and blocking probability. Additionally, we provide a method for estimating the number of backlogged devices in the network. The performance of the proposed algorithm is evaluated analytically and using simulations. To prove its effectiveness, the performance of the proposed algorithm is compared to the dynamic-access class-barring scheme, where the results depict the superiority of the proposed scheme. Finally, a binary integer programming problem is formulated, where we show that the achieved access delay using the proposed algorithm approaches the optimal value.

Fast Uplink Grant for Machine Type Communications: Challenges and Opportunities

The notion of a fast uplink grant is emerging as a promising solution for enabling massive machine type communication (MTC) in the Internet of Things over cellular networks. By using the fast uplink grant, machine type devices (MTDs) will no longer require RA channels to send scheduling requests. Instead, uplink resources can be actively allocated to MTDs by a base station. In this article, the challenges and opportunities for adopting the fast uplink grant to support MTCs are investigated. First, the fundamentals of fast uplink grant and its advantages over conventional scheduled and uncoordinated access schemes are presented. Then, the key challenges that include the prediction of the set of MTDs with data to transmit, as well as the optimal scheduling of MTDs, are exposed. To overcome these challenges, a two-stage approach that includes traffic prediction and optimized scheduling is proposed. In particular, various solutions for source traffic prediction for periodic MTC traffic are reviewed and novel methods for event-driven traffic prediction are proposed. For optimal allocation of uplink grants, advanced machine learning techniques are presented. By using the proposed solutions, the fast uplink grant has the potential to enable cellular networks to support massive MTCs and effectively reduce the signaling overhead and overcome the delay and congestion challenges of conventional RA schemes.

Optimal Resource Management and Binary Power Control in Network-Assisted D2D Communications for Higher Frequency Reuse Factor.

Device-to-device (D2D) communications can be adopted as a promising solution to attain high quality of service (QoS) for a network. However, D2D communications generates harmful interference when available resources are shared with traditional cellular users (CUs). In this paper, network architecture for the uplink resource management issue for D2D communications underlaying uplink cellular networks is proposed. We develop a fractional frequency reuse (FFR) technique to mitigate interference induced by D2D pairs (DPs) to CUs and mutual interference among DPs in a cell. Then, we formulate a sum throughput optimization problem to achieve the QoS requirements of the system. However, the computational complexity of the optimization problem is very high due to the exhaustive search for a global optimal solution. In order to reduce the complexity, we propose a greedy heuristic search algorithm for D2D communications so as to find a sub-optimal solution. Moreover, a binary power control scheme is proposed to enhance the system throughput by reducing overall interference. The performance of our proposed scheme is analyzed through extensive numerical analysis using Monte Carlo simulation. The results demonstrate that our proposed scheme provides significant improvement in system throughput with the lowest computational complexity.

Joint Uplink and Downlink Resource Allocation for D2D Communications System

In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub-problems including power allocation and channel assignment. It is proved that the objective function of power control is a convex function, in which the optimal transmission power can be obtained. The Hungarian algorithm is developed to achieve joint uplink and downlink channel assignment. The proposed scheme can improve the system capacity performance and increase the spectrum efficiency. Numerical results reveal that the performance of the proposed scheme of jointly uplink and downlink is better than that of the schemes for independent allocation.