Network Spectral Efficiency Research Articles

Multi-period traffic on elastic optical networks planning: alleviating the capacity crunch

Advances in optical data transmission technology have allowed the current expansion of bandwidth-demanding services over the Internet. Also, the emergence of orthogonal frequency-division multiplexing (OFDM) has opened the possibility of increasing the network spectral efficiency by solving the routing, modulation and spectrum assignment (RMSA) problem. Recently, investigators have examined the effects of multiple demands or multiple virtual topologies when they are requested at different time periods over a single physical substrate. That makes the RMSA harder and with many more instances. Such analysis is required because network traffic does not remain static along time, and the demand can increase considerably as new user services arise. Therefore, planning the network considering a multi-period study becomes essential, since it can prevent a case where demands may exceed the bandwidth capacity and cause request blocking in future periods. In this work, we provide a novel mixed integer linear programming (MILP) formulation to solve the RMSA problem for several t periods of demands. This model can be used not only to find the solutions to minimize the used capacity, but also as an efficient method of network planning, since it can estimate with a single formulation and a single iteration the point of resource exhaustion in each period t. The results are found for a small network, and they show the efficiency of the proposed MILP formulation. We also propose an alternative version of this formulation with predefined paths, which is less computationally demanding. The results of this study are compared to a step-by-step planning, where the strategy is a decomposition method that breaks the previous formulation into t steps. Comparing the results of the two strategies, it can be seen that the single multi-period formulation is a good strategy to solve the problem. By contrast, the step-by-step strategy may require reconfigurations and eventual interruptions in the network, from a step to another one.

Reconfigurable Intelligent Surface Assisted Coordinated Multipoint in Downlink NOMA Networks

In this letter, we investigate the amalgamation between the reconfigurable intelligent surface (RIS) technology and the joint transmission coordinated multipoint (JT-CoMP) in order to enhance the performance of a cell-edge user equipment (UE) in a two-user non-orthogonal multiple access (NOMA) group without deteriorating the performance of the NOMA cell-center UE. The RIS is adopted to construct a strong combined channel gain at the cell-edge UE, while JT-CoMP is used to mitigate the effects of inter-cell interference (ICI). In this proposed framework, we derive first a closed-form expression for the ergodic rate of the cell-edge UE, and then we evaluate the network spectral efficiency. We validate the derived expression through Monte-Carlo simulations, where we demonstrate the efficacy of the proposed framework compared to other multiple access techniques proposed in the literature.

CoMP Transmission in Downlink NOMA-Based Heterogeneous Cloud Radio Access Networks

In this paper, we investigate the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in downlink heterogeneous cloud radio access networks (H-CRANs). In H-CRAN, low-power high-density small remote radio heads (SRRHs) are underlaid by high-power low-density macro RRH (MRRH). However, co-channel deployment of the different RRHs gives rise to the problem of inter-cell interference that significantly affects system performance especially the cell-edge users. Thus, the users are first categorized into Non-CoMP users and CoMP users based on the relation between the useful signal to the dominant interference signal. The Non-CoMP user is the user equipment (UEs) having high signal-to-interference-plus-noise-ratio (INR) and hence associates with only one RRH. On the other hand, the CoMP user, cell-edge user, is the UE that experiences less distinctive received power with the best two RRHs. In the proposed CoMP-NOMA framework, each RRH schedules CoMP-UE and non-CoMP-UE over the same transmission channel using NOMA. We first design an analytical framework based on tools from the stochastic geometry to evaluate the performance of the proposed framework (CoMP-NOMA) which is based on H-CRAN in terms of the average achievable data rate for each NOMA UE. We then examine the spectral efficiency of the proposed CoMP-NOMA based H-CRAN. Simulation results are provided to validate the accuracy of the analytical models and to reveal the superiority of the proposed CoMP-NOMA framework compared with conventional CoMP orthogonal multiple access (CoMP-OMA) techniques. By reaping the benefits of both JT-CoMP and NOMA, we prove that the proposed framework can successfully deal with the inter-cell interference by using CoMP and improve the network's spectral efficiency through NOMA technique. We also show that, with an appropriate power allocation coefficient setting at the Non-CoMP-UEs, a fairness performance can be achieved between the CoMP-UEs and the Non-CoMP-UEs.

Distributed Joint Power, Association and Flight Control for Massive-MIMO Self-Organizing Flying Drones

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet . We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

Doherty PAs for 5G Massive MIMO: Energy-Efficient Integrated DPA MMICs for Sub-6-GHz and mm-Wave 5G Massive MIMO Systems

To accommodate growing user demand for faster data rates and extensive connectivity, modern wireless communication systems must evolve to support a sharply increasing number of subscribers, all requesting service at the same time. This trend encourages the broad application of multiple input/multiple output (MIMO) systems. In fact, MIMO techniques can increase data rates, coverage of service areas, and communication reliability without additional RFs. In recent proposals for 5G systems, the required separate RF chains in massive MIMO RF front ends can reach up to 256, with bandwidths of up to 800 MHz per RF chain [1], [2]. Massive MIMO is a critical technology that helps significantly in increasing network capacity and spectral efficiency, while reducing wireless network interference, ultimately improving the end-user experience.

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices. We take the network slicing technology to solve the problems of network spectral efficiency and URLLC reliability. A mixed-integer programming problem is formulated by maximizing the spectral efficiency of the system in the constraint of users’ requirements for two slices, i.e., the requirement of the eMBB slice and the requirement of the URLLC slice with a high probability for each user. By transforming and relaxing integer variables, the original problem is approximated to a convex optimization problem. Then, we combine the objective function and the constraint conditions through dual variables to form an augmented Lagrangian function, and the optimal solution of this function is the upper bound of the original problem. In addition, we propose a resource allocation algorithm that allocates the network slicing by applying the Powell–Hestenes–Rockafellar method and the branch and bound method, obtaining the optimal solution. The simulation results show that the proposed resource allocation algorithm can significantly improve the spectral efficiency of the system and URLLC reliability, compared with the adaptive particle swarm optimization (APSO), the equal power allocation (EPA), and the equal subcarrier allocation (ESA) algorithm. Furthermore, we analyze the spectral efficiency of the proposed algorithm with the users’ requirements change of two slices and get better spectral efficiency performance.

Pursuit Learning-Based Joint Pilot Allocation and Multi-Base Station Association in a Distributed Massive MIMO Network

Pilot contamination (PC) interference causes inaccurate user equipment (UE) channel estimations and significant signal-to-interference ratio (SINR) degradations. Pilot allocation and multi-base-station (BS) association have been used to combat the PC effect and to maximize the network spectral efficiency. However, current approaches solve the pilot allocation and multi-BS association separately. This leads to a sub-optimal solution. In this paper, we propose a parallel pursuit-learning-based joint pilot allocation and multi-BS association. We first formulate the pilot allocation and multi-BS association problem as a joint optimization function. To solve the optimization function, we use a parallel optimization solver, based on a pursuit learning algorithm, that decomposes the optimization function into multiple subfunctions. Each subfunction collaborates with the other ones to obtain an optimal solution by learning from rewards obtained from probabilistically testing random solution samples. A mathematical proof to guarantee the solution convergence is provided. Simulation results show that our scheme outperforms the existing schemes by an average of 18% in terms of the network spectral efficiency.

A novel beaconless routing algorithm based on EBGR algorithm for wireless sensor networks

The purpose of energy-efficient beaconless geographic routing (EBGR) algorithm is to reduce power consumption in the network. But it cannot select the best relay node in all conditions. Also, in the EBGR algorithm, the relay nodes send their data after receiving them, which creates an overhead in the network and decrease spectral efficiency in the network. We introduce an improved method to improve the performance of the EBGR algorithm. In our proposed method, we have divided the space into several subspaces. Each node prioritises the data transmission to the node that is located beneath its own space and along the source-to-destination direction. In order to reduce the slag on the grid, we have also considered adaptive delay. The advantage of this method is that if the network is ideal, the data arrives with the least delay and if the network has a lot of overhead, the delay rate will also increase.

A novel beaconless routing algorithm based on EBGR algorithm for wireless sensor networks

The purpose of energy-efficient beaconless geographic routing (EBGR) algorithm is to reduce power consumption in the network. But it cannot select the best relay node in all conditions. Also, in the EBGR algorithm, the relay nodes send their data after receiving them, which creates an overhead in the network and decrease spectral efficiency in the network. We introduce an improved method to improve the performance of the EBGR algorithm. In our proposed method, we have divided the space into several subspaces. Each node prioritises the data transmission to the node that is located beneath its own space and along the source-to-destination direction. In order to reduce the slag on the grid, we have also considered adaptive delay. The advantage of this method is that if the network is ideal, the data arrives with the least delay and if the network has a lot of overhead, the delay rate will also increase.

A Dynamic MBSFN Area Formation Algorithm for Multicast Service Delivery in 5G NR Networks

The ever-increasing demand for high-quality video contents over mobile networks is pushing Telco operators towards the design of new delivery solutions. 3GPP has defined Multimedia Broadcast/Multicast Service Single Frequency Network (MBSFN) to enable the simultaneous transmission of the same content within multiple cells over the same radio resources, with improved network scalability and spectral efficiency. This paper proposes a Dynamic MBSFN Area Formation (DMAF) algorithm that suitably selects the adjacent synchronized cells to include in any MBSFN Area of a 5G New Radio (NR) system to the purpose of increasing the system Aggregate Data Rate while avoiding user outage. The proposed algorithm dynamically creates MBSFN Areas by leveraging the multicast “subgrouping” paradigm for Scalable Video Coding (SVC) traffic delivery. Cells are grouped into different MBSFN Areas and any cell can be part of Disjoint MBSFN Area or Overlapping MBSFN Area. Each Overlapping MBSFN Area broadcasts a Base video flow at a given quality level to all users, whereas Enhancement Layers are only delivered to users with better channel conditions. Simulation results testify to the better performance achieved by the proposed algorithm w.r.t. other schemes available from the literature.

Illumination and Communication Using Visible Light Communication Under Flicker Avoidance in Dimmed LEDs Installed Indoors

In our day to day life with the increasing popularity among wirelessly connected devices, the wireless data traffic is growing exponentially. As a result, the network spectral efficiency has been saturated despite of new standards and great technological advancements. Visible Light Communication (VLC) using solid-state lighting to transmit information will be the best solution for wireless communication in the upcoming years. In promote of artificial illumination and communication the Light Emitting Diode (LED) lighting is progressively finding its way. The LED lighting lodges high switching times that are adequate to transmit data through the visible light it emanate. In addition to that it’s able to achieve dimming control through the dimming modulation schemes in VLC. Dimming LEDs used indoors provides energy saving. In this paper, the channel performance in terms of data rate and spectral efficiency for pulse width modulation (PWM) based digital dimming modulation techniques to dim the LEDs installed indoors under VLC have been computed.

On the use of non‐orthogonal multiple access for V2V message dissemination

Vehicular networks are characterised by a high density of nodes moving at a high speed and accessing the medium simultaneously which enlarges the interference, decreases the network throughput and spectral efficiency. That increases the challenge of designing a reliable communication scheme to handle the particular characteristics of this network and ensure user fairness. Here, a new vehicle-to-vehicle communication scheme is proposed. It consists of using particular vehicles (selected upon their position) to disseminate the message using non-orthogonal multiple access technique. Power domain NOMA allocates a great amount of power to the vehicle in poor conditions, which enhances the system power control; improve throughput and user fairness in comparison with the OMA-based IEEE 802.11p standard. A log-normal shadowing is considered to model the propagation channel. The simulation results show that considering a channel model where severe conditions of signal propagation are present, a more advanced receiver designs have to be proposed on the subject of channel estimation and equalisation to mitigate channel effect on the received data. However, even in a harsh fading environment, NOMA outperforms OMA in terms of network throughput, spectral efficiency, and user fairness, which makes it a very promising solution for future development of vehicular communication systems.

An Efficient Design for NOMA-Assisted MISO-SWIPT Systems with AC Computing

We consider a multiple-input single-output simultaneous wireless information and power transfer (MISO-SWIPT) system, where a power-splitting protocol is employed at users near the base station (BS) to provide both energy harvesting (EH) and information decoding. For the considered system, it is of practical interest to adopt non-orthogonal multiple access (NOMA) to improve the network spectral efficiency, while still meeting the EH requirements. In addition, an alternating current computing (ACC) logic is incorporated into EH receivers to directly use the wirelessly harvested AC power, which in turn achieves higher energy efficiency than traditional direct current computing (DCC). We formulate a problem of maximizing the spectral efficiency subject to the constraints of quality-of-service for the individual user, EH requirements, and BS’s maximum transmit power, where the beamformers and PS ratios are jointly optimized. To achieve an efficient solution to this nonconvex problem, we propose an iterative algorithm based on the inner approximation (IA) framework, where the approximate convex problem solved in each iteration can be cast as a second-order-cone program with convergence guaranteed. To further simplify the problem design, we propose a zero-forcing beamforming-based NOMA approach to partially eliminate interference, which has the potential to significantly reduce the number of variables. The extensive numerical results are presented to demonstrate the effectiveness of the proposed algorithms, compared with the baseline schemes.

Active Interference Cancellation for Full-Duplex Multiuser Networks With or Without Existence of Self-Interference

It is understood that in a network with an in-band full-duplex (FD) access point (AP) simultaneously serving multiple half-duplex clients, various kinds of interference may severely degrade the network spectral efficiency (SE). In this paper, we first address the problem of canceling the inter-client interference (ICI) from multiple uplink clients to multiple downlink clients in such a scenario. The proposed ICI nulling approach, namely, multiuser ICI cancellation (MU-IC2), enables the AP to actively and completely eliminate the ICI for the downlink clients. With this method, the gain from FD processing can be achieved much efficiently. Design guideline is given and reduced-complexity implementation for use with a large number of antennas at the AP is also derived. Furthermore, in some circumstances, notable residual self-interference (SI) exists at the AP due to the imperfection of SI cancellation during the FD operation. This may enlarge the interference observed by the downlink clients. We provide new ways, i.e., two MU-SI-IC2 approaches, to deal with this case based on the possible knowledge of SI as well. As a result, both the SI-induced interference and ICI are effectively eliminated, and the overall throughput can still be benefited from FD and kept at a high level. The simulation results show that in terms of the network SE, our proposed MU-IC2 and MU-SI-IC2 can significantly outperform some baseline schemes and the state-of-the-art active ICI mitigation methods in various scenarios.

Energy efficient ultra‐dense networks based on multi‐objective optimisation framework

Energy efficiency (EE) is considered as one of the pivotal uplink (UL) performance metrics for 5G dense networks. It consists of two conflicting objectives that are recognised as benefit-cost ratio: spectral efficiency and energy consumption. Accordingly, network design tradeoff is the key challenge for future networks. In this paper, we aim to jointly maximise network spectral efficiency and minimise power consumption with respect to the following design parameters: base station (BS) density, users' number, equipped antennas' number, and signal-to-noise power ratio without losing service quality. The performance of the ultra-dense network is characterised on the basis of the Pareto optimality concept through the following benchmarks: (i) studying impact of exhausted power on the deployed hardware elements. (ii) Validating the total EE performance through Monte-Carlo simulation within a low cost of processing time. (iii) The proposed approach is compared against single objective scheme to show the significance of the design tradeoff. Furthermore, we will introduce a detailed mathematical analysis for UL power policy and channel estimation for reliable pilot reusing. Simulation results will show that our proposed solution guarantee remarkable EE performance via reducing the number of deployed BSs without scarificing service quality.

Optimal Radius of Exclusion Zone for Dense Cognitive Small Cell Networks

Network densification via small cell deployments can enhance the network capacity in a cost-effective manner. Co-tier and cross-tier interferences form barriers to boost the network performance. In this paper, an interference coordination scheme with the aid of cognitive radio is proposed for dense small cell networks. Based on the theory of stochastic geometry, the network spectral efficiency is derived as a function of the radius of exclusion zone. Furthermore, the optimal radius of exclusion zone that maximizes system spectral efficiency is obtained by one-dimension searching. Simulation results show that the proposed interference coordination scheme with the optimal exclusion zone outperforms the existing ones in terms of system spectral efficiency.

Robust Transmission Design for Multicell D2D Underlaid Cellular Networks

This paper investigates the robust transmission design (RTD) of a multi-cell device-to-device (D2D) underlaid cellular network with imperfect channel state information (CSI). The bounded model is adopted to characterize the CSI impairment and the aim is to maximize the worst-case sum rate of the system. To protect cellular communications, it is assumed that the interference from all D2D transmitters to each base station (BS) is power-limited. It is first shown that the worst-case signal-to-interference-plus-noise ratio (SINR) of each D2D link can be obtained directly, while that of cellular links cannot be similarly found since the channel estimation error vectors of cellular links are coupled in the SINR expressions. To solve the nonconvex problem, the objective function of the original problem is replaced with its lower bound, and the resulted problem is decomposed into multiple semidefinite programming (SDP) subproblems which are convex and have computationally efficient solutions. An iterative RTD algorithm is then proposed to obtain a suboptimal solution. Simulation results show that D2D communication can significantly increase the performance of the conventional cellular systems while causing tolerable interference to cellular users. In addition, the proposed RTD algorithm outperforms the conventional non-robust transmission design greatly in terms of network spectral efficiency.

Generalized Sparse and Low-Rank Optimization for Ultra-Dense Networks

Ultra-dense network (UDN) is a promising technology to further evolve wireless networks and meet the diverse performance requirements of 5G networks. With abundant access points, each with communication, computation and storage resources, UDN brings unprecedented benefits, including significant improvement in network spectral efficiency and energy efficiency, greatly reduced latency to enable novel mobile applications, and the capability of providing massive access for Internet of Things (IoT) devices. However, such great promises come with formidable research challenges. To design and operate such complex networks with various types of resources, efficient and innovative methodologies will be needed. This motivates the recent introduction of highly structured and generalizable models for network optimization. In this article, we present some recently proposed large-scale sparse and low-rank frameworks for optimizing UDNs, supported by various motivating applications. A special attention is paid on algorithmic approaches to deal with nonconvex objective functions and constraints, as well as computational scalability.

Heterogeneous Ultra Dense Networks: Part 2

The eleven articles in this special section focus on heterogeneous ultra dense networks. With the continuous enrichment of mobile communication application scenarios in the future, the traditional macrocellular-based mobile communication network architecture will be challenged to meet the explosive growth in demand for communications services. As described in the Guest Editorial of the first part of this Feature Topic, the ultra-dense network (UDN) has become a promising technology to signifi cantly improve the network spectral efficiency and system performance. Because the massive number of small base stations have the advantages of low transmission power and flexible deployment, they help to effectively expand the coverage and throughput of the network. Hence, in heterogeneous UDN, the dense deployment of small base stations can signifi cantly improve the quality of the links and increase the network capacity. As the key technology of 5G and the advanced communication technology at the present stage, UDN has received worldwide attention and eff orts.

Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC.

Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.