User Throughput Research Articles

Towards Finite File Packetizations in Wireless Device-to-Device Caching Networks

We consider wireless device-to-device (D2D) caching networks with single-hop transmissions. Previous work has demonstrated that caching and coded multicasting can significantly increase per user throughput. However, the state-of-the-art coded caching schemes for D2D networks are generally impractical because content files are partitioned into an exponential number of packets with respect to the number of users if both library and memory sizes are fixed. In this paper, we present two combinatorial approaches of D2D coded caching network design with reduced packetizations and desired throughput gain compared to the conventional uncoded unicasting. The first approach uses a “hypercube” design, where each user caches a “hyperplane” in this hypercube and the intersections of “hyperplanes” represent coded multicasting codewords. In addition, we extend the hypercube approach to a decentralized design. The second approach uses the Ruzsa-Szemeredi graph to define the cache placement. Disjoint matchings on this graph represent coded multicasting codewords. Both approaches yield an exponential reduction of packetizations while providing a per-user throughput that is comparable to the state-of-the-art designs in the literature. Furthermore, we apply spatial reuse to the new D2D network designs to further reduce the required packetizations and significantly improve per user throughput for some parameter regimes.

Multiple UAV-Mounted Base Station Placement and User Association With Joint Fronthaul and Backhaul Optimization

In this paper, we study a joint placement, resource allocation, and user association problem for UAV-assisted wireless networks with constrained backhaul links, where multiple UAV-mounted base stations (UBSs) are deployed to provide wireless services for ground users. We propose a novel framework to maximize the user throughput within the flight-time of UBSs and provides fairness among the users. We first obtain the optimal resource allocation schemes based on different fronthaul and backhaul conditions, and an efficient iterative algorithm is then developed to jointly optimize user association and UBS placement. The optimal UBS placement can be achieved by solving an unconstrained optimization problem which is a simplification of the initial constrained optimization problem based on the optimal resource allocation. We develop a dual-domain coordinated descent and bipartite graph matching based sub-process to identify an optimal user association that prefers the nearby UBSs, as the user association under constrained backhaul links have non-unique optimal solutions. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm, and results show that our proposed method under constrained backhaul can improve both the average throughput by 49% and the fairness among the users by 47% in comparison with the method under ideal backhaul.

A fully distributed approach for joint user association and RRH clustering in cloud radio access networks

Cloud radio access network (C-RAN) is a new centralized architecture to meet the exponential growing of demand of mobile traffic in 5G cellular wireless networks. However, C-RAN requires an efficient mechanism for the joint user association and the Remote Radio Head (RRH) clustering to improve network performance. In this paper, we investigate the problem of joint user association (UA) and RRH clustering (RC) in C-RAN. Our objective is to maximize the network utility function incurred by both network power consumption and total user throughput for both streaming and elastic traffic. The formulation of the joint optimization problem is a mixed-integer non-linear programming problem (MINLP), which is NP-hard and usually has no feasible solution. To solve it, we propose to decouple the joint problem into two sub-optimization problems: the user association (UA) sub-problem and the RRH clustering (RC) sub-problem. These two sub-problems are sequentially and iteratively solved until convergence is reached. Leveraging on the information delivered by the Call Detail Records (CDR), simulation results reveal the effectiveness of our heuristic solution for the RC sub-problem in enhancing network utility and adapting to the traffic load variation for both elastic and streaming traffic. It outperforms the performance of the state-of-the-art algorithms for RRH clustering solutions, including no-clustering and grand coalition methods. Moreover, the results show that our approach for the UA sub-problem provides close performance to the optimal UA sub-problem.

Proactive link handover deploying coordinated transmission for indoor visible light communications (VLC) networks

Abstract Visible Light Communications (VLC) is considered as an emerging technology for indoor wireless communications to achieve high-speed and secure data transmission. Instead of using radio frequency (RF) spectrum, VLC uses the visible light spectrum to perform lighting and communications functions simultaneously. Multiple access points VLC (multi-AP VLC) networks use ceiling access point (ceiling-AP) and desk access point (desk-AP) to provide both uniform and spot lighting in order to achieve full coverage and high spectral efficiency. Because mobile user equipment (UE) require seamless connectivity when moving, fast link handover between VLC access points (VLC-AP) has to be supported in the VLC networks. In this paper, we present a coordinated multi-channel transmission method (CMcT) used to improve quality of service (QoS) of cell-edge UEs and propose a novel proactive link handover scheme deploying the CMcT method for multi-AP VLC networks. Performance results obtained by computer simulation show that the proactive link handover scheme deploying the CMcT method can significantly improve user throughput and packet delay comparing with those of other link handover schemes.

Optimal Resource Allocation in Ground Wireless Networks Supporting Unmanned Aerial Vehicle Transmissions

We consider a fully-loaded ground wireless network supporting unmanned aerial vehicle (UAV) transmission services. To enable the overload transmissions to a ground user (GU) and a UAV, two transmission schemes are employed, namely non-orthogonal multiple access (NOMA) and relaying, depending on whether or not the GU and UAV are served simultaneously. Under the assumption of the system operating with infinite blocklength (IBL) codes, the IBL throughputs of both the GU and the UAV are derived under the two schemes. More importantly, we also consider the scenario in which data packets are transmitted via finite blocklength (FBL) codes, i.e., data transmission to both the UAV and the GU is performed under low-latency and high reliability constraints. In this setting, the FBL throughputs are characterized again considering the two schemes of NOMA and relaying. Following the IBL and FBL throughput characterizations, optimal resource allocation designs are subsequently proposed to maximize the UAV throughput while guaranteeing the throughput of the cellular user. Moreover, we prove that the relaying scheme is able to provide transmission service to the UAV while improving the GU's performance, and that the relaying scheme potentially offers a higher throughput to the UAV in the FBL regime than in the IBL regime. On the other hand, the NOMA scheme provides a higher UAV throughput (than relaying) by slightly sacrificing the GU's performance.

Distributed Cell Clustering Based on Multi-Layer Message Passing for Downlink Joint Processing Coordinated Multipoint Transmission

Joint processing coordinated multipoint transmission (JP-CoMP) has gained high attention as part of the effort to cope with the increasing levels of demand in the next-generation wireless communications systems. By clustering neighboring cells and with cooperative transmission within each cluster, JP-CoMP efficiently mitigates inter-cell interference and improves the overall system throughput. However, choosing the optimal clustering is formulated as a nonlinear mathematical problem, making it very challenging to find a practical solution. In this paper, we propose a distributed cell clustering algorithm that maximizes the overall throughput of the JP-CoMP scheme. The proposed algorithm renders the nonlinear mathematical problem of JP-CoMP clustering into an approximated linear formulation and introduces a multi-layer message-passing framework in order to find an efficient solution with a very low computational load. The main advantages of the proposed algorithm are that i) it enables distributed control among neighboring cells without the need for any central coordinators of the network; (ii) the computational load imposed on each cell is kept to a minimum; and, (iii) required message exchanges via backhaul result in only small levels of overhead on the network. The simulation results verify that the proposed algorithm finds an efficient JP-CoMP clustering that outperforms previous algorithms in terms of both the sum throughput and edge user throughput. Moreover, the convergence properties and the computational complexity of the proposed algorithm are compared with those of previous algorithms, confirming its usefulness in practical implementations.

Dynamic and location‐based power allocation mechanism for inter‐cell interference mitigation in 5G heterogeneous cellular network

SummaryIn this paper, a dynamic and location‐based power allocation mechanism is proposed which could be adopted at both macrocell (MC) and overlaid fixed/mobile small cells (SCs) to mitigate inter‐cell interference (ICI) effects in heterogeneous cellular networks (HetCNs). The proposed Dynamic Power Allocation based on User Location (DPAUL) mechanism allows both MCs and deployed fixed/mobile SCs to dynamically allocate transmit power to its serving base stations (BSs) based on the location of a user in the cell. The paper illustrates the mitigation of dynamic downlink interferences occurring due to the mobility of SCs and users. The mobility of cell and its users is analyzed by introducing the Cell‐User mobility (CUM) model in the network. The proposed DPAUL mechanism is compared with the authors' other ICI mitigation techniques: Dynamic Fixed Region Cooperation (DFRC) and Dynamic Power Allocation Mechanism (DPAM). The network metrics Sumrate, User throughput, Energy‐efficiency, and Outage probability are investigated with allocation of sub 6 GHz and mmWave spectrums at MCs and fSCs/mSCs, respectively.

Quality of experience‐driven resource allocation in vehicular cloud long‐term evolution networks

AbstractTo improve user experience and allocate reasonably spectrum resources, the quality of experience (QoE) for user is introduced as an indicator to express the user's satisfaction with the service provided. The QoE‐driven resource allocation optimization algorithm for orthogonal frequency‐division multiple is designed to maximize the user's QoE and optimize resource allocation in vehicular cloud (VC) long‐term evolution (LTE) networks. Therein, a novel QoE model which reflects delay, transmission rate, and service price by mean opinion score (MOS) is proposed to identify different services. Furthermore, the characteristics of delay, transmission rate, and service price are also analyzed specifically at different time slots, respectively. Simulation results show that the proposed resource allocation algorithm could achieve a better QoE in the field of user satisfaction and throughput compared with other traditional algorithms, especially when the number of vehicles is greater than 30 or 22, respectively. Meanwhile, the impact of fairness on QoE and throughput are also improved apparently when the number of vehicles is small. In end, the QoE‐mode brings performance improvements on delay, transmission rate, and service price compared with other single X mode.

Exploiting a Deep Neural Network for Efficient Transmit Power Minimization in a Wireless Powered Communication Network

In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

Energy-efficient user selection and resource allocation in mobile edge computing

Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency.

Parallel Transmission LiFi

Light fidelity (LiFi) is a relatively new wireless communication technology that exploits the optical spectrum. Compared to wireless fidelity (WiFi), LiFi is densely deployed with each access point (AP) covering an area only a few meters in diameter. Also, LiFi users are susceptible to intermittent light-path blockages. Meanwhile, the user is served by a single AP in conventional LiFi systems. This would cause frequent handovers for LiFi users, resulting in a degradation in quality of service. In this paper, parallel transmission is investigated for LiFi, which is named PT-LiFi. With a delicate design of the transmitter and the receiver, PT-LiFi enables multiple LiFi APs to serve the user simultaneously. Particularly, data transmission continues without interruption when the user is losing connectivity to some of the connected APs. Resource allocation is studied for the PT-LiFi system, and a novel load balancing method is proposed to jointly allocate resource across the APs. Results show PT-LiFi can make efficient use of the densely deployed LiFi APs and provide a flexible way of load balancing. Compared with a conventional LiFi system, the proposed method can increase user throughput by up to 150% and improve user fairness by up to 15%.

Partial Pilot Allocation Scheme in Multi-Cell Massive MIMO Systems for Pilot Contamination Reduction

Inter-cell interference has been identified as one of the major challenges of multiple-input–multiple-output (MIMO)-enabled cellular systems. This problem occurs when the same pilot sets are reused across adjacent cells to save bandwidth for data transmission. As a result, so-called pilot contamination occurs, which cannot be mitigated with an increased number of serving antennas. In this work, we proposed a partial pilot allocation scheme (PPA) to tackle the pilot contamination problem and consequently improve the uplink throughput of users in multi-cell massive MIMO systems. This was achieved by using the large-scale characteristics of the fading channel to keep users with a weak channel condition out of the effect of severe interference during the pilot allocation process. Simulation results showed that the proposed scheme outperformed both smart pilot allocation (SPA) and conventional schemes. In particular, PPA improved the uplink rate by 30% compared to the SPA—a recently proposed schema. Furthermore, our simulation results clearly showed that PPA improved the cumulative distribution function (CDF) of the signal-to-interference-plus-noise ratio (SINR) and uplink throughput.

Downlink Radio Resource Management Through CoMP and Carrier Aggregation for LTE-Advanced Network

Long Term Evolution-Advanced (LTE-A) offers several new technologies to improve the performance of the user. However, poor received signal and interference from adjacent cells in the cell-edge area can reduce the efficiency of using individual technology. Therefore, the cell-edge users have lower throughput compared to the other users in the cell and LTE-A standard. An efficient downlink radio resource management scheme is proposed in this paper by combining the coordinated multipoint transmission and reception technique along with carrier aggregation technique to achieve higher throughput for the cell-edge user and better overall performance. The proposed method jointly transmits multiple component carriers to the cell-edge user from different cells to increase the bandwidth, strengthen the received signal, and reduce the interference while it satisfies several constraints. Modified largest weighted delay first packet scheduling algorithm is deployed for resource allocation, which takes into account the delay parameters, the probability of packet loss, and data rates of the user. The obtained system-level simulation results show that the proposed method significantly enhances the throughput performances, spectral efficiency, and fairness index, compared with the existing conventional methods.

Adaptive power allocation based beamforming algorithm for multiple input and multiple output nonorthogonal multiple access system throughput optimization

AbstractThe combination of nonorthogonal multiple access (NOMA) and multiple input and multiple output (MIMO) systems is considered a promising approach for 5G mobile communication networks. In addition, techniques such as user selection and beamforming could be applied to improve some parameters of system performance. In this context, we propose a joint user selection, adaptive power allocation, and beamforming algorithm for the downlink of massive MIMO NOMA systems in order to maximize the throughput of users. To this end, we present a novel optimal beamforming solution scheme based on the adaptive computation of signal power values to maximize system throughput, considering the less interfering users. To evaluate the performance of the proposed algorithm, simulations were performed considering different power allocation algorithms.

Dynamic and energy‐efficient ICI mitigation techniques for mobility‐based 5G HetCN

In this study, two dynamic mechanisms are proposed to mitigate inter-cell interference (ICI) in the heterogeneous cellular network (HetCN) namely: Dynamic Fixed Region Cooperation (DFRC) and Dynamic Power Allocation Mechanism (DPAM). The DFRC allows serving mSCs and other interfering base stations (BSs) to cooperate during transmission of a signal to a user. The DPAM dynamically allocates power to serving BS based on the farthest user in a cell. The study illustrates downlink interferences typically linked with a HetCN. For analysis, a macrocell (MC) network distributed with heterogeneous users is overlaid with both fixed and mobile small cells (SCs). The authors also introduced the cell-user mobility model for deployed mobile SCs in the network. The effects on network performance metrics: Sumrate, Average user throughput, and Energy efficiency are investigated with an allocation of sub 6 GHz and mmWave bands at MC and deployed SCs, respectively.

Throughput Maximization by Optimizing Secondary Users in a Cooperative Cognitive Radio Network using Hard Fusion Rules

Cognitive radio network is a promising technology for enabling secondary users to utilize the licensed spectrum of the primary user without causing interference. The data trans- mitted by the secondary users through primary channel without affecting the primary user is known as channel throughput. In cooperative spectrum sensing(CSS) as the number of secondary users increases the channel throughput increases which in turn reduces the spectrum efficiency due to more spectrum wastage. Therefore in this paper, channel throughput is maximized by optimizing secondary users proposed and throughput for variable secondary users for OR and AND fusion rules is investigated. The optimal secondary users is estimated mathematically and simulation results shows the variation of throughput for variable number of secondary users.

Wirelessly Powered Cell-Free IoT: Analysis and Optimization

In this article, we propose a wirelessly powered Internet-of-Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closed-form expression of SINR as the metrics of power transfer and data transmission, respectively. To improve energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT in terms of both downlink and uplink 95% likely performances. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.

Analysis of Acquired Indoor LTE-A Data from an Actual HetNet Cellular Deployment

Increasing the user downlink throughput is always a task of utmost importance for any cellular network service provider. However, most of the research is only focused on system simulations due to environmental simplicity. In this paper, instead of focusing on theoretical simulations, we aim to find the best practical solution to increase user throughput in an actual HetNet deployment where many small cells are deployed inside a building. An inter-frequency deployment is implemented in the actual LTE-A HetNet environment. A series of data collections were conducted in an actual indoor LTE-Advanced HetNet (heterogeneous network) environment with optimization strategies such as ABS (Almost Blank Subframe) and turning off/on interfering antennas. Detailed findings and analysis are provided to better understand the complicated actual HetNet environment. This work is also important to understand the future 5G network since the small cells and HetNet will continue to play an important role in the 5G network.

On Interference Aware Power Adjustment and Scheduling in Femtocell Networks

Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces such as office buildings, subways, and academic buildings. These networks can increase user throughput, but edge users can suffer from co-channel interference and service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS works by jointly adapting femtocell transmission power, allocating user to femtocells, and scheduling resource blocks so as to increase fairness and reduce outage probability in dense femtocell networks. RRS produces a network configuration that guarantees either user or area coverage depending on the management needs. A prototype implementation confirms the benefits of RRS in a real environment. Furthermore, extensive simulations show that RRS reduces the outage probability of up to 50%, and provides better fairness, with an increase in Jain’s index of 190%, with respect to a baseline algorithm which works with fixed power and best-effort scheduling and to a previous approach to resource management in femtocell networks.

Optimization Algorithm for Spectrum Sensing Delay Time in Cognitive Radio Networks Using Decoding Forward Relay

Using decode-and-forward relaying in the cognitive radio networks, the spectrum efficiency can improve furthermore. The optimization algorithm of the spectrum sensing estimation time is presented for the cognitive relay networks in this paper. The longer sensing time will bring two aspects of the consequences. On the one hand, the channel parameters are estimated more accurate so as to reduce the interferences to the authorized users and to improve the throughput of the cognitive users. On the other hand, it shortens the transmission time so as to decease the system throughput. In this time, it exists an optimal sensing time to maximize the throughput. The channel state information of the sub-bands is considered as the exponentially distributed, so a stochastic programming method is proposed to optimize the sensing time for the cognitive relay networks. The computer simulation results using the Matlab software show that the algorithm is effective, which has a certain engineering application value.