Coordinated Multipoint Research Articles

Load-Aware Greedy Dynamic CoMP Clustering Mechanism for DPS CoMP in 5G Networks

Cooperative communications offered by the Coordinated Multipoint (CoMP) technique have been introduced and become an important technique in the 5G mobile networks to support very high system capacity and improve the end-user service quality, especially for the cell-edge users via the dynamic coordination of transceivers at different geographical sites. One major issue driving the performance of CoMP operation is the decision on the coopering clusters or the cluster formation strategies. In this work, an approach of CoMP clustering has been studied and proposed, the so-called load-aware greedy dynamic CoMP clustering mechanism. Our proposed CoMP clustering technique is deployed on top of the actual traffic-based load-aware Dynamic Point Selection CoMP (DPS CoMP) mechanism, which is our previously proposed mechanism. The approach is aimed at maximizing spectral efficiency with appropriate cell coordination based on well-balancing cell loads within the coordinating area of the 5G networks. System performance is numerically studied and observed for the 5G Non-Orthogonal Multiple Access (NOMA) systems embedded with our proposed mechanisms for the realistic scenarios, i.e., homogeneous network and Heterogeneous Network (HetNet) cases. Numerical results illustrate the benefit brought by our approaches in comparison with others CoMP clustering techniques as well as the potential for further improvement.

A novel low complexity beamforming scheme for coordinated multipoint networks

This article presents a quick overview of conventional precoding and beamforming techniques in the cellular networks and proposes a novel coordinated beamforming technique for coordinated multipoint (CoMP) networks, which is based upon signal-to-leakage-plus-noise ratio (SLNR) metric. The proposed scheme has reduced computational cost and offers improved performance compared to the conventional multi-cell precoding schemes in the noise-dominated environments. Simulation results reveal that proposed scheme offers a spectrally efficient, low complexity solution with improved average user throughput and sum rate in the CoMP enabled networks.

User and resource allocation in latency constrained Xhaul via reinforcement learning

The Flexible Ethernet (FlexE) is envisioned for the provisioning of different services and hard slicing of the Xhaul in 5G and beyond networks. For efficient bandwidth utilization in the Xhaul, traffic prediction for slot allocation in FlexE calendars is required. Further, if coordinated multipoint (CoMP) is used, the allocation of users to remote units (RUs) with an Xhaul path of lower latency to the distributed unit/central unit will increase the achievable user bit rate. In this paper, the use of multi-agent deep reinforcement learning (DRL) for optimal slot allocations in a FlexE-enabled Xhaul, for traffic generated through CoMP, and for offloading users among different RUs is explored. In simulation results, the DRL agent can learn to predict input traffic patterns and allocate slots with the necessary granularity of 5 Gbps in the FlexE calendar. The resulting gains are expressed in terms of the reduction of mean over-allocation of slots in the FlexE calendar in comparison to the prediction obtained from an autoregressive integrated moving average (ARIMA) model. Simulations indicate that DRL outperforms ARIMA-based prediction by up to 11.6%

Ground-to-Air Communications Beyond 5G: A Coordinated Multipoint Transmission Based on Poisson-Delaunay Triangulation

This paper designs a novel ground-to-air communication scheme to serve unmanned aerial vehicles (UAVs) through legacy terrestrial base stations (BSs). In particular, a tractable coordinated multi-point (CoMP) transmission based on the geometry of Poisson-Delaunay triangulation is developed, which provides reliable and seamless connectivity for UAVs. An effective dynamic frequency allocation scheme is designed to eliminate inter-cell interference by using the theory of circle packing. For exact performance evaluation, the handoff probability of a typical UAV is characterized, and then the coverage probability with handoffs is attained. Simulation and numerical results corroborate that the proposed scheme outperforms the conventional CoMP scheme with three nearest cooperating BSs in terms of handoff and coverage probabilities. Moreover, as each UAV has a fixed and unique CoMP BS set, it avoids the real-time dynamic BS searching process, thus reducing the feedback overhead.

Joint Waveform and Clustering Design for Coordinated Multi-Point DFRC Systems

To improve both sensing and communication performances, this paper proposes a coordinated multi-point (CoMP) transmission design for a dual-functional radar-communication (DFRC) system. In the proposed CoMP-DFRC system, the central processor (CP) coordinates multiple base stations (BSs) to transmit both the communication signal and the dedicated probing signal. The communication performance and the sensing performance are both evaluated by the signal-to-interference-plus-noise ratio (SINR). Given the limited backhaul capacity, we study the waveform and clustering design from both the radar-centric perspective and the communication-centric perspective. Dinkelbach’s transform is adopted to handle the single-ratio fractional objective for the radar-centric problem. For the communication-centric problem, we adopt quadratic transform to convexitify the multi-ratio fractional objective. Then, the rank-one constraint of communication beamforming vector is relaxed by semidefinite relaxation (SDR), and the tightness of SDR is further proved to guarantee the optimal waveform design with fixed clustering. For dynamic clustering, equivalent continuous functions are used to represent the non-continuous clustering variables. Successive convex approximation (SCA) is further utilized to convexitify the equivalent functions. Simulation results are provided to verify the effectiveness of all proposed designs.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

Co-Channel Interference Management for Heterogeneous Networks Using Deep Learning Approach

The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs.

Enhancing coordinated multi-point joint reception with dynamic bandwidth allocation

Coordinated Multipoint (CoMP) Joint Reception (JR) is a vital technology to mitigate uplink inter-cell interference (ICI) and can be facilitated by the centralized radio access network (C-RAN). In C-RAN architecture, base stations (BSs) are decoupled into the central unit (CU) and the distributed units (DUs). Meanwhile, a fronthaul (FH) is employed to connect CU and DUs. However, FH constructed by the time division multiplexing passive optical network (TDM-PON) significantly affects the performance of JR. The inherent polling mechanism of TDM-PON results in the asynchronous forwarding of JR data, which leads to high JR latency and a large amount of storage consumption. To alleviate the problem, we presented a novel dynamic bandwidth allocation (DBA) scheme, which classifies network data as JR and non-JR data and optimizes the data forwarding order of different types and DUs. A heuristic algorithm called Rearrange Sub-timeslot DBA (RS-DBA) was proposed to implement the DBA scheme. Simulation results indicate the proposed RS-DBA algorithm can reduce the average latency of JR data and storage resources consumption. In addition, the RS-DBA can make a better tradeoff between latency and efficiency.

Analytical Modeling of RSMA-Enabled User-Centric RRH Clustering in C-RAN Over Generalized Fading Channels

Network densification and coordinated multi-point (CoMP) have been shown to be promising and practical approaches to meet the dramatic increase in mobile traffic demand. Motivated by the ever-increasing popularity of rate splitting multiple access (RSMA) in wireless communications, this letter proposes an RSMA-enabled user-centric remote radio heads (RRH) clustering in cloud radio access networks (C-RAN) and then develops a statistical framework to evaluate the average sum-rate and the area spectral efficiency of this mechanism under generalized fading conditions by stochastic geometry. In addition to this framework, new insights into the tradeoff between the average sum-rate and area spectral efficiency in terms of RRH clustering radius are presented. Further, two widely utilized fading models, namely Rayleigh and Nakagami-m, are discussed to characterize the fading behavior in C-RAN. Finally, simulations are demonstrated to verify the effectiveness of the proposed analytical framework.

Multi-Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation.

Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning

This paper investigates the problem of providing ultra-reliable and power-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users’ feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users’ head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users’ accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users’ tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solve a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% power-efficient than the benchmark algorithms.

Flexible Guard Structure for Timing Synchronization in Coordinated Networks

In this letter, a novel and flexible hybrid guard duration (HGD) based on zero tail (ZT) and cyclic prefix (CP) concepts is proposed for facilitating timing synchronization (TS) in coordinated multi-point (COMP) networks. The proposed HGD harmoniously provides enough guard duration for mitigating the channel’s delay spread and facilitating TS without extending the total symbol duration while supporting both COMP and non-COMP users. That is, unlike the traditional extended CP (E-CP) approach that degrades the system’s spectral efficiency (SE) and elevates the transmission latency due to the extended symbol duration, HGD maintains the usage of the normal CP size and the total symbol duration, thereby ensuring better SE performance and relatively low latency. The proposed HGD is evaluated and compared with the traditional E-CP in terms of bit error rate, SE, and latency.

A Survey of FDD-Based Channel Estimation Schemes With Coordinated Multipoint

New enabling technologies that include millimeter wave (mmWave) communication, massive multiple-input-multiple-output (MIMO), and ultradense networks are set to transform the wireless communication systems. These emerging technologies contribute toward the evolution of fifth generation (5G) systems that promise greater connectivity, throughput, mobility, and reliability for communication networks. This article presents a comprehensive overview of techniques for channel estimation, particularly, frequency division duplex (FDD)-based estimation techniques for a communication network. An exhaustive literature survey of FDD-based estimation schemes is presented, with a comprehensive review of the coordinated multipoint (CoMP) strategies in 5G radio networks, and its reliance on acquisition of channel state information (CSI). Since accurate CSI promotes the adoption of channel precoding, and reliable signal detection in FDD-based 5G systems, therefore, channel estimation for CSI in conjunction with CoMP transmission is preferable. We also present future research directions for FDD-based channel estimation and CoMP in 5G.

CoMP-Assisted NOMA and Cooperative NOMA in Indoor VLC Cellular Systems

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters.

Radio Resource Management for Eicic, Comp, Relaying and Back-hauls Techniques in LTE-advanced Networks: Concepts and a Literature Survey

Frequency reuse in cells is one of the strategies that LTE (Long Term Evolution) uses to maximize the spectrum efficiency. However, it leads to an interference among the cells, especially at the cell edges where the probability for a cell-edge user to be scheduled on a resource block that is being transmitted by the neighbouring cell is high; consequently, the interference is high. In-order to mitigate Inter-Cell Interference (ICI), Inter-Cell Interference Coordination (ICIC) was proposed by the Third Generation Partnership Project (3GPP) standards for the LTE network, and later on, the enhanced Inter-Cell Interference Coordination (eICIC) was proposed for the LTE-Advanced network. ICIC reduces cell-edge interference on traffic channels from neighbouring cells by the use of three interference reduction schemes that works in the power and frequency domain, and they are based on lowering the power of some channels to limit their reception to the users that are close to the base station, and by reducing the chance of frequency overlap. eICIC was proposed to handle ICI in Heterogeneous Network (HetNet) deployments. It reduces the interference on both the traffic and control channels. It uses power, frequency and also time domain to mitigate intra-frequency interference in HetNets. Another technique that was proposed by the 3GPP for the LTE-Advanced network that can reduce the ICI and improve the cell average and cell-edge user throughput is the Coordinated Multi-Point (CoMP) transmission/reception technique. CoMP can increase the cell average and cell edge user throughput in both the uplink and downlink transmission by joint scheduling and data processing in multiple cells/eNBs. Another technique that was proposed by the 3GPP for the LTE-Advanced network that can improve the LTE network coverage in difficult conditions is to deploy Relay Nodes (RNs). In this paper, we survey Radio Resource Management (RRM) for some of the techniques that are used with LTE-A. The included techniques in this paper are; the ICIC, eICIC, CoMP, Relaying and Back-hauls. We start by explaining the concepts of these techniques. Then, by summarizing the radio resource management approaches that were proposed in the literature for these techniques. And finally, we provide some concluding remarks in the last section.

CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks

The huge traffic demand envisioned in 5G requires radical changes in mobile network architecture. A Centralized Radio Access Network (C-RAN) was introduced as a novel mobile network architecture, designed to effectively support the challenging requirements of future 5G networks. Coordinated Multi-point (CoMP) is one of the technologies aiming to increase user traffic by transforming inter-cell interference into useful signals to maximize cell-edge users throughput. Intra-CoMP means that cooperating RRHs are assigned to the same Base Band Unit (BBU). On the other hand, in inter-CoMP, the cooperating RRHs are assigned to different BBUs, which introduce overhead signalling over an X2 interface. This paper proposes a model for BBU placement in C-RAN deployment over a 5G optical aggregation network. The model aims to minimize the number of users undergoing inter-CoMP, therefore reducing X2 signalling overhead. First, we solve the BBU placement problem using Integer Linear Programming (ILP), which minimizes the number of BBUs and the number of used links. Second, given the output of the ILP model (i.e., BBU locations and routes), we propose a heuristic algorithm to reconfigure the BBUs (i.e., the assignment of the RRHs to their corresponding BBUs), which aims at minimizing the number of users undergoing inter-CoMP. The proposed heuristic algorithm considers minimizing end-to-end delay, the number of used wavelengths, and maximizing multiplexing gain. The results show that up to 97% of inter-CoMP users migrated to intra-CoMP users. This results in a decrease in the X2 traffic, which is mainly used for the coordination between the BBUs.

Joint Beamforming Coordination and User Selection for CoMP-Enabled NR-U Networks

The sixth-generation (6G) era is expected to provide even higher levels of massive connectivity/Internet of Things (IoT), tremendous data rate, and low latency than the 5G communication. Therefore, the future 6G wireless networks would confront with much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. Meanwhile, the high densely deployed massive IoT devices lead to fearful intercell and intracell interference issues. To address these challenges, a coordinated multipoint (CoMP) technology can be adopted. In this work, we introduce a CoMP-based NR-U network, particularly for 6G-enabled massive IoT scenarios. Under the proposed network, we introduce a spatial listen-before-talk (LBT) scheme to control mobile network operators (MNOs) to coexist with the incumbent WiFi devices orthogonally, which can significantly improve the spectrum efficiency. To suppress the strong co-channel interference among multiple MNOs, we investigate a joint beamforming coordination and user selection problem, which is NP-hard. To deal with this problem, we first adopt a fractional programming and an integer replacement method to transform the objective problem into a tractable one. Then, we introduce a semidistributed alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm to find a suboptimal solution, which only requires limited information exchange via a CoMP server and can help reduce energy consumption. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed algorithm for large-scale 6G IoT scenarios.

Distributed fronthaul-constrained joint transmission design and selection using augmented consensus-based dual decomposition

User-centric coordinated multipoint (CoMP) joint transmission (JT) is a novel technique to manage interference and enhance system performance with single frequency reuse, where user equipment (UE) communicates with their closest transmission points (TPs). Unfortunately, in coherent JT, requirement of strict network synchronization accuracy makes it difficult and expensive to be practically deployed. The noncoherent JT has therefore received growing attention since it requires less strict network synchronization accuracy compared to its coherent counterpart as it does not require the signal to be phase-aligned at the receiver. Moreover, cell-free massive MIMO, which is regarded as combining CoMP with massive MIMO systems, has recently been touted as a solution for avoiding intercell interference and provide uniform coverage over a large area. However, the operational costs of CoMP, such as the associated control signaling and communication overhead and the increase of network complexity, could prevent the practical implementation of CoMP. A distributed joint transmission CoMP (JT-CoMP) scheme is proposed herein that allows distributed design and selection of cooperating transmission nodes. To maximize user capacity, the proposed distributed consensus optimization problem assumes spectrum underlay transmission is used so that the solution can achieve non-orthogonal multiple access (NOMA) for cell-free user centric JT-CoMP systems. The proposed algorithm is different from others in the literature because it solves a design problem that involves a coupling constraint that no existing algorithm can solve. Analytical results based on spectral graph theory are given to prove its convergence and characterize its rate of convergence. The more practical scenario is further considered, where limited fronthaul capacity is also included in the problem. A successive convex approximation (SCA) method is used to solve the resulting nonconvex problem, which is shown to maximize spectral efficiency. Simulation results are provided to show that the performance of both proposed distributed algorithms (that address problem without and with fronthaul constraint) is comparable to its centralized counterpart.

Distributed Offloading in Overlapping Areas of Mobile-Edge Computing for Internet of Things

With the maturity of 5G cellular communication systems and mobile-edge computing (MEC), a large number of base stations (BSs) with edge-computing servers are densely deployed. There are extensive overlapping coverage areas among the BSs in which some heavy computational tasks from Internet of Things (IoT) devices can be divided and offloaded to multiple BSs via the coordinated multipoint (CoMP) technique for parallel processing. However, it is a challenging issue about how to make proper task offloading decisions among multiple connected BSs while satisfying delay requirements of multiple devices. To address this challenge, this article presents an efficient multidevice and multi-BSs task offloading scheme with the goal of minimizing the delay for completing the tasks of the devices. By conducting quantitative analysis of local delay and offloading delay, a nonlinear and nonconvex delay optimization offloading problem, which is based on the theory of noncooperative game, is formulated. We prove the existence of Nash equilibrium by analyzing the feature of the proposed offloading problem and further propose a distributed task offloading algorithm called DOLA. Finally, simulation experiments based on real-world data set from the Melbourne CBD area of Australia are conducted to validate the efficacy of our DOLA algorithm. Comparison experiments are also carried out to demonstrate the superiority of DOLA in comparison with some existing schemes.

Performance analysis of Heterogeneous Cloud-Radio Access Networks: A user-centric approach with network scalability

Heterogeneous cloud-radio access network (HC-RAN) is a very promising network architecture for future generation wireless communication systems. The centralized computation of HC-RAN provides flexibility to coordinate multi-point (CoMP) transmission of remote radio heads (RRHs). These RRHs can cooperatively form static or dynamic clusters which are network-centric or user-centric to serve the user equipment (UE). To meet the demands of future generation wireless networks, the user-centric approach is gaining much attention, especially in dynamic clustering. In dynamic user-centric CoMP, a set of overlapped clusters can cooperate dynamically to serve UEs. In a large network, the main challenge is the scalability of the computational complexity and fronthaul load with the increase of the number of UEs. To address this, we developed a scalable user-centric HC-RAN by utilizing dynamic cooperation clustering (DCC) framework. We presented an algorithm for joint user association, resource allocation, and cluster formation. We derived the expressions for different combining and precoding vectors to satisfy the scalability condition. We evaluated the performance of the scalable user-centric HC-RAN in terms of the achievable rate per UE at two different levels of cooperation with imperfect channel state information (CSI). The performance of the derived schemes is nearly optimal compared to the unscalable benchmark schemes and the ideal scalable system.