Conventional Cellular Users Research Articles

Performance Analysis of Resource Allocation Algorithm Based on Simple Particle Swarm Optimization for Device to Device Communication System

The development of Device-to-Device (D2D) communication as an alternative communication scheme continues to grow. D2D communication enables each device to communicate directly with other devices without going through the Evolved Node B (eNB). These days, D2D communication can be implemented in conventional cellular communication, and use the same spectrum as the conventional cellular user (underlay). This underlay scheme can improve the cell’s spectrum efficiency, but the interference level that happens in the cell is increased. Because D2D communication and cellular communication use the same spectrum, there will be interference between D2D user equipment (DUE) and Cellular user Equipment (CUE). A well-designed radio resource allocation is needed to reduce the interference level, while maintaining the overall performance of the cell. In this research, Simplified Particle Swarm Optimization (SPSO) is proposed to overcome this problem. SPSO is a PSO-based algorithm with a limited number of iterations designed to halt calculations when the PSO algorithm cannot find a solution. If SPSO exceeds the limit iteration, a greedy algorithm is executed to do the allocation process. From the simulation, the SPSO algorithm can achieve 1.3310 × 108 bps, 12.3239 bps/Hz, 2.1328 × 103 bps/Watt and 92% on total sumrate, spectral efficiency, power efficiency, and system fairness respectively. These number is better if compared with the conventional greedy allocation algorithm. The total sumrate, spectral efficiency, and power efficiency are increased by 0.9%, 0.74%, and 0.95% in average datarate, spectral efficiency, and power efficiency respectively. Meanwhile the SPSO’s system fairness is decreased by 1.65% compared with the conventional greedy algorithm.

Power control scheme using particle swarm optimization method in resource allocation process on D2D underlaying communication

Fast growing number of cellular telecommunication technology resulting on the increasing number of the user equipment. This condition increased the eNodeB load. To overcome this problem, the device-to-device (D2D) underlaying communication is introduced. In underlaying scheme, the D2D user equipment (DUE) will do the communication process using the same radio resources with the conventional cellular user equipment (CUE). To avoid a severe interference between these two types of user in the system, a good resource allocation is needed. In this work, a power control scheme using particle swarm optimization (PSO) is proposed, to manage the transmit power on each user on the system. The power control scheme take place after the greedy scheduling algorithm, after all user is given a resource block (RB) to do the communication process. The power transmit for each user is managed to reach a better system capacity, and to reduce the power consumed in one communication process. From the simulation, the PSO power control can improve the sumrate and spectral efficiency up to 12.97% and 3.38% respectively. The PSO power control also can reduce the power consumed by the system up to 8.84%. The fairness happens among the CUEs also can be maintained, despite of the decreasing fairness among DUEs.

An Optimal Balanced Energy Harvesting Algorithm for Maximizing Two-Way Relaying D2D Communication Data Rate

Combining energy harvesting (EH) and device-to-device (D2D) communication underlaying 5G cellular networks is a very promising direction to improve both energy and spectral efficiencies. Unlike conventional relay-aided D2D communication that assumes one-way relaying (OWR) protocols, this paper proposes a two-way relaying (TWR) model. It aims to maximize the TWR D2D link rate that shares the uplink (UL) resources of the conventional cellular user (CU) considering the quality of service (QoS) constraints of all users. Besides, the relays are considered to harvest renewable energy (RE) from the ambient environment by relying on an attached solar panel. Also, they can harvest radio frequency (RF) energy from the received signal based on the power splitting (PS) EH protocol. Assuming that the UL resource allocation (RA) is already performed, the paper’s objective is to jointly optimize the transmission power of all users in addition to the PS factor of relays based on the well-known meta-heuristic algorithm particle swarm optimization (PSO). Also, the best relay is selected by relying on the delimited area (DA) mechanism and the balanced residual energy (BRE) leading to TWR D2D link rate maximization and better energy efficiency (EE). The performance of the proposed algorithm is investigated through the results as well as comparing its performance to two of the most recent relay-aided D2D algorithms.

Learning Driven NOMA Assisted Vehicular Edge Computing via Underlay Spectrum Sharing

Edge computing has been considered as one of the key paradigms in the fifth-generation (5G) networks for enabling computation-intensive yet latency-sensitive vehicular Internet services. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted vehicular edge computing via underlay spectrum sharing, in which vehicular computing-users (VUs) form a NOMA-group and reuse conventional cellular user's (CU's) channel for computation offloading. In spite of the benefit of spectrum sharing, the resulting co-channel interference degrades the CU's transmission. We thus firstly focus on a single-cell scenario of two VUs reusing one CU's channel, and analyze the CU's increased delay due to sharing channel with the VUs. We then jointly optimize the VUs' partial offloading and the allocation of the communication and computing resources to minimize the VUs' delay while limiting the CU's suffered increased delay. An efficient layered-algorithm is proposed to tackle with the non-convexity of the joint optimization problem. Based on our study on the single-cell scenario, we further investigate the multi-cell scenario in which a group of VUs flexibly form pairs to reuse the channels of different CUs for offloading, and formulate an optimal pairing problem to minimize the VUs' overall-delay. To address the difficulty due to the combinatorial nature of the pairing problem, we propose a cross-entropy (CE) based probabilistic learning algorithm to find the optimal VU-pairings. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms for both the single-cell scenario and multi-cell scenario. The results also demonstrate that our NOMA-assisted MEC via spectrum sharing can outperform the conventional frequency division multiple access assisted offloading scheme.

Deep Learning Based Resources Allocation for Internet-of-Things Deployment Underlaying Cellular Networks

Resources allocation (RA) is a challenging task in many fields and applications including communications and computer networks. The conventional solutions of such problems usually come with a time and memory cost, especially for massive networks such as Internet-of-Things (IoT) networks. In this paper, two RA deep network models are proposed for enabling a clustered underlay IoT deployment, where a group of IoT nodes are uploading information to a centralized gateway in their vicinity by reusing the communication channels of conventional cellular users. The RA problem is formulated as a two-dimensional matching problem, which can be expressed as a traditional linear sum assignment problem (LSAP). The two proposed models are based on the recurrent neural network (RNN). Specifically, we investigate the performance of two long short-term memory (LSTM) based architectures. The results show that the proposed techniques could be used as replacement of the well-known Hungarian algorithm for solving LSAPs due to its ability to find the solution for the problems with different sizes, high accuracy, and very fast execution time. Additionally, the results show that the obtained accuracy outperforms the state-of-the-art deep network techniques.

Latency Optimization for Cellular Assisted Mobile Edge Computing via Non-Orthogonal Multiple Access

In this article, we investigate the cellular assisted mobile edge computing (MEC) via non-orthogonal multiple access (NOMA), where a group of edge-computing users (EUs) exploit NOMA to simultaneously offload their computation-workloads to an edge-server (ES), and conventional cellular-user (CU) allows the EUs to reuse its authorized frequency channel for the NOMA-transmission. We firstly characterize the transmit-powers of the CU and EUs and then formulate a joint optimization of the EUs’ offloaded computation-workloads, offloading-duration (i.e., how long for reusing the CU's channel), as well as the ES's computation-resource allocations (for processing different EUs’ offloaded workloads), with the objective of minimizing the overall-latency in completing all EUs’ computation-requirements, subject to the CU‘s and EUs’ limited power and energy capacities as well as the ES's limited computation-resource capacity. Despite the strict non-convexity of the formulated joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. With the optimal offloading solution for the scenario of one CU, we further investigate the scenario of multiple CUs and investigate the optimal pairing of the EUs for reusing different CUs’ channels for computation-offloading. Taking into account the coupling effect due to the ES's limited computation-resource, we formulate a joint optimization of the EU-pairing and the ES's capacity allocation of the computation-resource for accommodating different EU-pairs. Despite the difficulty due to the mixed binary and non-linear non-convex programming of the formulated problem, we propose an efficient layered algorithm for solving the problem. Numerical results are provided to validate the accuracy of our proposed algorithms. We also show the performance advantage of our NOMA-assisted offloading in comparison with conventional orthogonal multiple access (OMA) based computation offloading.

Traffic Offloading in Multicast Device-to-Device Cellular Networks: A Combinatorial Auction-Based Matching Algorithm.

In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity.

Cellular-V2X Communications With Weighted-Power-Based Mode Selection

Cellular-assisted vehicular-to-everything (C-V2X) communication is a promising technology to enhance the road safety and support various infortainment services in future vehicular networks. However, the integration of vehicular communication with the conventional cellular network faces many new design challenges, as vehicles typically use different spectrum band (5.9 GHz) from the cellular systems, and they usually follow different distributions and have more stringent reliability and latency requirements, as compared with the conventional cellular users. In this paper, we study C-V2X communication where the V2X resource pool is managed by the eNodeBs and reused in different cells, with the cells divided according to the Voronoi tessellation of the eNodeBs. We investigate three connection modes for a vehicle to receive safety/entertainment information, namely, from its associated eNodeB, from a neighboring vehicle on the same road, or from a neighboring vehicle on a different road. We propose a weighted-power-based mode selection scheme which offers great flexibility to balance the load of the cellular network and improve the coverage probability. By using stochastic geometry, we derive the analytical expressions for the mode selection and coverage probabilities, which are validated by extensive simulations. Furthermore, by optimizing the weighting factors, the proposed mode selection scheme leads to significantly better performance than the conventional distance-based and maximum-power-based mode selection schemes.

Maximum and Optimal Number of Activated Links for D2D-Aided Underlaying Cellular Networks

With the rapid popularity of smart terminals, the device-to-device(D2D) communication is recognized as one of the most promising techniques in the fifth-generation(5G) communications networks due to its capabilities of substantially improving the spectral efficiency, relieving the traffic burden in the base stations(BSs), and reducing the terminals’ power consumptions, etc. By properly activating some D2D links(DLs), the sum data rate of the D2D-aided cellular networks(CNs) can be substantially improved. However, constrained by the severe interference imposed on the conventional cellular users (CUs) by the activating D2D users (DUs), the sum data rate of the D2D-aided CNs cannot be unlimitedly improved in a crude way of simply increasing the number/density of DLs. In other words, there must exist a maximum/optimal number of activating DLs in terms of the sum data rate, as revealed in this paper. By identifying the maximum/optimal number of activating DLs, the closed-form expressions for sum data rates of both cellular links(CLs) and DLs can be obtained. Numerical results show that the optimum number of activating DLs, as a function of several critical parameters such as DU’s transmit power, signal-to-interference-plus-noise ratio threshold and outage probability of CLs/DLs, etc, can be determined by implementing the proposed algorithms.

Efficient Resource-Sharing Algorithms for Mobile-Cell’s Sidehaul and Access Links

The mobile-cells (MCs) aim to enhance quality of service for the in-vehicular cellular users by mitigating vehicular penetration loss. Allocating resources for MCs is challenging because of multiple links involved, for example, sidehaul link (SL) and access link (AL). In this letter, we propose algorithms to enable resource sharing between an MC’s AL, SL, and conventional cellular users. The proposed algorithm is shown to yield high ergodic rates across all three resource-sharing links.

Optimal Resource and Power Allocation With Relay Selection for RF/RE Energy Harvesting Relay-Aided D2D Communication

Device-to-device (D2D) communication is considered as a promising technology for improving both the spectral and energy efficiencies of cellular networks by reusing the resources of conventional cellular users (CUs) for direct communication of two nearby devices in a spatial manner. When the channel between the two D2D devices is highly attenuated, it is necessary to use an intermediate relay to achieve reliable and flexible relay-aided D2D communication. In order to motivate the cooperative relays to participate, it is assumed that they can harvest energy from radio frequency (RF) signals based on the power splitting (PS) protocol as well as renewable energy (RE) sources. However, resource sharing between the cellular and relay-aided D2D links leads to mutual interference that degrades their sum rate. Considering the energy-harvesting relays (EHRs) and downlink (DL) resource sharing, this paper aims to maximize the sum rate of both the links without degrading the quality of service (QoS) requirements of all users. Our maximization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that cannot be solved in a straightforward manner. Therefore, we propose a low complexity algorithm, namely the resource and power allocation with relay selection EH-aided algorithm (RPRS-EH), which determines the reuse partners, the PS factor sub-optimal value with optimal links power allocation, and provides two different strategies for optimal relay selection. The numerical results show the behavior of the proposed algorithm under various parameters as well as its considerable performance when compared to one of the most recent algorithms in terms of the links sum rate and relay energy efficiency.

Fog Radio Access Networks With Hierarchical Content Delivery

We investigate a novel hierarchical content delivery strategy for F-RAN in this paper to accommodate the acquisition of popular multimedia files. Particularly, the studied F-RAN is assumed to be formed by base stations (BSs), cache-enabled fog user equipments (F-UEs), and conventional cellular user equipments (C-UEs), where the BSs, F-UEs, and C-UEs are spatially distributed as independent HPPPs. Furthermore, we assume the presence of a multimedia content library F:= {1,2,...,F}. To facilitate the repetitive downloading of multimedia files in F for C-UEs, a novel hierarchical content delivery strategy, which comprises of the BS transmission phase and the F-UE transmission phase, is proposed. Particularly, under the proposed hierarchical content delivery strategy, a C-UE with a random file request is jointly and hierarchically served by its associated BS and the F-UEs within a distance of R d . Furthermore, to resolve the contentions among the F-UEs, a carrier sensing-based collision avoidance scheme is adopted in the F-UE transmission phase, and only the F-UEs which have the minimal back-off timer among their respective competitors are selected to deliver the cached file. As such, the proposed hierarchical content delivery strategy can substantially enhance the quality of experience of the F-RAN. In order to analyze the performance of the proposed hierarchical content delivery strategy, we derive the network successful content delivery probability of the F-RAN based on the HPPP approximation of active F-UEs. The simulation examples are presented to verify our analytical results.

Uplink Interference Coordination Management With Power Control for D2D Underlaying Cellular Networks: Modeling, Algorithms, and Analysis

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio.

Enabling D2D Transmission Mode with Energy Harvesting and Information Transfer in Heterogeneous Networks

The concept of energy harvesting-assisted relay has been introduced to support the relaying transmission using Device-to-Device (D2D) communications for enhancing communication reliability. Motivated by the recent advance in Heterogeneous Network (HetNet) using relaying techniques, we consider the D2D communication provided by Energy Harvesting (EH) assisted relay where signal is forwarded from a Base Station (BS) to the conventional cellular user (non-D2D user) and D2D user. We first derive the outage probability by taking into account the SNR and power allocation parameters, and propose the transmission mode for D2D link as well as non-D2D link. After deriving the outage probability of the D2D-HetNet, we explore the effects of the network parameters on the outage probability and throughput.

Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider’s profit while guaranteeing maximum outage probability requirements.

Public safety users’ priority‐based energy and time‐efficient device discovery scheme with contention resolution for ProSe in third generation partnership project long‐term evolution‐advanced systems

A device-to-device (D2D) discovery scheme is key enabler for proximity-based services in third generation partnership project long-term evolution-advanced systems for public safety (PS) and general LTE scenarios. The deployment of D2D networks results in severe co-channel interference between conventional cellular users and D2D users, and faces proximity interference management challenges because of the co-existence of multiple D2D users. We propose a time and energy-efficient contention-resolving device discovery resource allocation (TEECR-DDRA) scheme that has the capability to enhance the success ratio for discovery of D2D users by reducing collisions among users. Moreover, the proposed TEECR-DDRA scheme has the ability to prioritise PS users to meet their QoS and latency requirements. Furthermore, multi-channel slotted ALOHA with energy sensing can be used to increase the probability of successful discovery of non-PS users. This ability helps to reduce the discovery time of PS users under disaster scenarios, and also reduces the energy consumption of non-PS users by minimising the number of beacon retransmissions. System-level simulations show that the proposed TEECR-DDRA scheme performs remarkably well under D2D network. Compared with the conventional random access scheme, the proposed scheme almost doubles the discovery range and significantly improves the success ratio for discovery of D2D users.

Optimal Mode Selection With Uplink Data Rate Maximization for D2D-Aided Underlaying Cellular Networks

The device-to-device (D2D) communication has been regarded as an effective technique for complementing and enhancing the conventional cellular systems owing to its capability of substantially improving both the spectral and power efficiencies of wireless networks. However, the severe interference imposed on the conventional cellular users (CUs) by the geographically close-by D2D pairs may cause a significant performance erosion in the D2D-aided underlaying cellular networks (CNs). In this paper, performance analysis for the D2D-aided underlaying CNs in terms of throughput is provided. We first derive the closed-form expressions of the coverage probability for both the conventional cellular links and the D2D links, followed by giving out the approximated expressions of the ergodic data rate for both an individual cellular/D2D link and the whole underlaying network. Furthermore, the key parameters (e.g., the density of D2D users (DUs) or CUs, and the average geographical distance between a pair of D2D peers) significantly impacting the channel capacity are adaptively adjusted for maximizing the sum data rate of the proposed underlaying networks. In addition, both theoretical analysis and simulation results reveal the attainability of the maximal throughput by optimizing the critical parameters, such as the density of DUs, provided that the scale factor between the DUs and sum users (i.e., comprising both conventional CUs and DUs) can be effectively balanced subject to the constraints specified in the proposed scheme.

SC-FDMA-based resource allocation and power control scheme for D2D communication using LTE-A uplink resource

Abstract Device-to-device (D2D) communication-enabled cellular networks allow cellular devices to directly communicate with each other without any evolved NodeB (eNB). D2D communication aims to improve the spectral efficiency and increases the overall system capacity. For future mobile networks, intelligent radio resource allocation and power control schemes are required to accommodate the increasing number of cellular devices and their growing demand of data traffic. In this paper, a combined resource allocation and power control scheme for D2D communication is proposed. In the proposed scheme, D2D communication reuses the uplink (UL) resources of conventional cellular user equipments (CUEs); therefore, we have adopted single-carrier frequency division multiple access (SC-FDMA) as UL transmission scheme. The proposed scheme uses fractional frequency reuse (FFR)-based architecture to efficiently allocate the resources and mitigate the interference between CUEs and D2D user equipments (DUEs). In order to guarantee the user fairness, the proposed scheme uses the well-known proportional fair (PF) scheduling algorithm for resource allocation. We have also proposed an intelligent power control scheme which provides equal opportunity to both CUEs and DUEs to achieve a certain minimum signal-to-interference and noise ratio (SINR) value. The performance evaluation results show that the proposed scheme significantly improves the overall cell capacity and achieves low peak-to-average power ratio (PAPR).

Optimal Resource Allocation and Power Management for Mobile-To-Mobile Communication in Cellular Network

If in a cellular network two mobile units communicate with each other without use of base station then it is termed as M2M communication and such thing can improve efficiency of spectrum. However such operation may cause interference to conventional cellular network. This paper proposes a resource allocation scheme to maximize throughput of a network while maintaining quality of service (QOS) for both M2M users as well as conventional cellular user. The proposed scheme first performs admission control and later allocates required power for each M2M pair and their respective partnered cellular units that have been admitted into network. Now in order to maximize throughput of network a suitable CU are selected as partners to admit M2M pairs. The results enclosed specifies the behavior of macro fading and shadowing effects at various CU position apart from this a cumulative distribution function for SNR has been provided along with its energy distribution and delay.