Search

Network softwarization (NetSoft), recognized as crucial attribute of 6G networks, promises to provide enhanced and advanced services, including future quantum-enabled consumer applications. Softwarized resource allocation is the core issue in NetSoft concept. Digital twins (DT) guarantees to generate the corresponding digital world that reflects and interacts with the original physical world seamlessly. With DT empowering, the digital replica of softwarized networks can be generated to predict, simulate, analyze the softwarized resource allocation in more economical, convenient and scalable methods.In this paper, we research the softwarized resource allocation of requested services, usually, called as slices, in DT-empowered networks for future quantum-enabled consumer applications. We focus on developing efficient softwarized resource allocation algorithm. At first, we present models of the DT-empowered networks and service requests by using graph theory and hypergraph theory. Then, we design one softwarized resource management framework, labeled as DT-Slice-Soft-6G. This framework has the functions of managing softwarized resources, calculating resource allocation solution in digital replica and sending the calculated solution back to softwarized 6G networks. Thereafter, one efficient and fine-grained softwarized resource allocation algorithm, inserted in DT-Slice-Soft-6G, is detailed. This algorithm is labeled as Heu-DT-Slice-6G and is proposed based on efficient heuristic methods. To validate the highlights of DT-Slice-Soft-6G and Heu-DT-Slice-6G, we conduct the simulation work in our self-developed simulator.

Unmanned aerial vehicle (UAV) has been utilized as an efficient data collector for Internet of Things (IoT) networks in sustainable industry 5.0. Whereas, how to sustain a stable power for the energy-constrained IoT devices (IoTDs) and to enhance the data gathering throughput of UAV-aided IoT networks via the wireless power transfer (WPT) or non-orthogonal multiple access (NOMA) is a twofold challenge. Thus, we propose to maximize the minimum UAV data collection throughput from the IoTDs via jointly optimizing the three-dimensional (3D) trajectories of two UAVs, scheduling and transmitting power of the IoTDs subject to the maximum flight velocity and minimum safe distance for the UAVs, as well as the harvested energy causality constraint for each IoTD during a finite UAV flight mission period. To tackle this non-convex problem with the strong interdependence of optimization parameters, we develop a 3D Trajectory Optimization and communication Resources Allocation Algorithm, named as TORAA, via employing the alternating optimization and successive convex approximation approaches, which alternately optimizes the UAVs’ 3D trajectories, the IoTDs’ scheduling and transmitting power sub-problems until the convergence criterion is met by the target function value. Moreover, we analyze the complexity and convergence characteristic of the TORAA. Numerous simulations have been performed to validate that the TORAA is capable of noteworthily enhancing the maximum minimum data collection throughput compared to the benchmark schemes with two-dimensional (2D) trajectories of the UAVs or constant transmitting power of the IoTDs.

Managing for local data among the increasingly popular consumer smart electronics in a secure manner while increasing the user experience is still a hard work. Federated learning (FL), can develop intelligent electronics-related applications while protecting the privacy of local data. However, considering the traditional cloud-based FL training process, consumer electronics with a limited energy budget can reduce the training efficiency. Hence, in this paper, to reduce the total latency of FL training while also meeting a targeted minimum value of loss function and meeting the long-term energy consumption among smart consumer electronics, we introduce an energy-efficient hierarchical FL algorithm and formulate a multi-objective optimization problem including diversified resource allocation and device association. Considering channel state information is unavailable for all rounds, applying the Lyapunov optimization framework, an alternative problem incorporating the significance of local models is reformulated to decrease the training latency per round and enhance long-term performance at the same time. To achieve a better solution to the device association problem, a low-complexity two-operation device association algorithm is proposed, along with resource allocation for training time control, local computing power control, and bandwidth allocation. Our proposed algorithm can achieve better learning performance while meeting the energy budget compared with multiple benchmarks, according to numerical results.

AI-enabled logistics unmanned aerial vehicles (UAVs) are progressively revealing their unique advantages for future smart cities. Nevertheless, the existing research on logistics UAV path planning lacks to simultaneously consider the UAV energy consumption constraints, the customer time windows, the impacts of wind speed and direction. This omission renders the existing models inappropriate for real-world transportation systems. Besides, the UAVs are still constrained by the limited payload and battery due to the highly automatic delivery process. Consequently, we investigate the effect of wind speed and direction on UAV flight states, establishes pertinent parameters and their resolution methods impacted by wind conditions, and delves into the logistics UAV path planning issue that concurrently considers the UAV energy consumption constraints, the customer time windows, and the impact of wind conditions. To resolve the proposed trajectory optimization issue, the large-scale neighborhood search algorithm (LNS) is amalgamated with the genetic algorithm (GA), forming the GA-LNS, to address the static problem, while dynamic planning concepts are employed in the decoding process of GA-LNS to solve the dynamic trajectory optimization problem. Simulation results demonstrate that the devised algorithms yield superior solutions within a plausible timeframe, reducing distribution costs by approximately 9% in comparison to the conventional GA. Unlike the no-wind and static scenarios, path planning that incorporates dynamic wind conditions circumvents issues related to energy constraints and customer satisfaction bias evident in the prior cases. Furthermore, the proposed algorithm can provide a high-efficiency, low-energy-consumption, and low-delay UAV planning strategy in the scenario of UAV-assisted data collection.

Efficient image transmission while preserving semantic information is crucial for many applications but poses challenges when communication channels have limited capacity. This letter presents an end-to-end semantic communication system for image transmission by exploiting semantic information, which is different from traditional approaches that emphasize pixel-level information. Specifically, identical semantic knowledge libraries are shared between the sender and receiver to associate image contents with semantics. At the sender, a deep learning-based classifier categorizes the image, and a dictionary learning method extracts features. At the receiver, a modified diffusion model-based generator reconstructs the image from the received features and category, with the objective to minimize the reconstruction error. To evaluate the semantic fidelity, we propose a semantic fidelity index (SFI) that considers both mutual information and neural network (NN) feature similarity between the original and reconstructed images. Experiments demonstrate that, by leveraging the shared semantic prior knowledge base, our approach can efficiently convey image semantics and achieve high-quality reconstruction. The proposed system provides an effective solution for semantic-preserving image communication in bandwidth-limited applications.

Comparing with previous five generation networks consisting of dedicated appliance, 6G network appliances are designed to be softwarized and isolated into software (resource and function) blocks and general-purpose hardware. Tailored services, having customized resources and function demands, can be executed by chained software blocks and co-exist in the underlying 6G general-purpose hardware. However, physical hardware is vulnerable to a wide range of security threats and attacks. When attacked, this can result in devastation and failure of tailored services. Furthermore, the quality of a tailored service will be greatly degraded. Recently, zero security trust emerges and is accepted as one contemporary security model that provides a new cybersecurity strategy to eliminate implicit trust. Thus, the soft-warized resource allocation of tailored services with zero security trust is investigated in this article. 6G networks and tailored service are firstly modeled and described. Then, the softwarized resource allocation method, abbreviated as Tail-ZeSec-6G, is detailed. When receiving one tailored service request, Tail-ZeSec-6G will check all underlying general-purpose appliance and eliminate implicit trust issues. After undergoing the security check and evaluation, Tail-ZeSec-6G will conduct a tailored resource allocation. Secure appliances with abundant softwarized resources will be selected to deploy the tailored service request, by adopting the resource allocation strategy in Tail-ZeSec-6G. When processing the tailored resource allocation, the Tail-ZeSec-6G method verifies the security of each appliance. Thus, Tail-ZeSec-6G method guarantees the deployment success and security performance. To validate the feasibility and merits of Tail-ZeSec-6G, evaluation is conducted. Finally, we conclude this article emphasizing three dominant directions.

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively integrate edge computing for the implementation of deep learning-based vehicular applications. Nevertheless, the incentive mechanism for vehicles participating in varied learning tasks, has not been well explored yet. In this paper, software-defined network (SDN) technology is adopted for the training control among vehicles, and a novel FEEL framework, namely SDN-assisted semi-decentralized FEEL (SSD-FEEL) is investigated, where multiple edge servers collectively coordinate a large number of vehicular models from different learning tasks. By exploiting the low-cost and similar learning tasks among vehicles and edge servers, SSD-FEEL incorporates more training samples, while enjoying the flexibility of edge server assisted model aggregation. Aiming at motivating vehicles to actively participate in training while improving the model accuracy of multiple learning tasks, a joint contract design and task reorganization problem, combined with the evaluation of model convergence and contract performance, is formulated. Then, we propose a two-stage optimization algorithm incorporating iterative reward allocation and task matching, where the model parameters in different tasks are reconstructed according to the matching results with the mobility constraints. Extensive experiments conducted on multiple data sets validate that the proposed algorithm can achieve higher cluster utility and outperform the conventional multi-task FEEL schemes in terms of learning performance.

In current 6G study, space-aerial-ground integrated networks (SAGIN) are attracting the eye-catching attention. Traditional ground networks and facilities (e.g., open random access networks, Open-RAN) simply provide two-dimensional (2D) and limited-coverage services at low altitudes. SAGIN are designed to overcome these limitations and promise to provide 3D and seamless services and connectivity opportunities. Meanwhile, network softwarization (NetSoft) is highlighted as dominant 6G attribute. Through incorporating NetSoft, dedicated network appliance will be isolated as software blocks and general-purpose hardware. Initiated and customized services will be realized by chained software blocks and share the same underlying hardware. Thus, the resource usage and service diversity can be simultaneously achieved by adopting NetSoft. In current literature, SAGIN and NetSoft are well investigated. However, their joint research is at early stage. Therefore, we investigate the resource allocation in softwarized SAGIN for achieving open and seamless access services. We propose the efficient resource allocation and orchestration framework, abbreviated as Slice-Soft-SAGIN. Our framework approach is designed to achieve efficient resource allocation solution per slice service. Each time receiving one tailored slicing service, the first checking available resources will be conducted by our Slice-Soft-SAGIN. When passing the preliminary resource checking, the formal resource allocation of slicing service will be executed in order: first terrestrial node, second aerial node, third satellite node. In addition, our Slice-Soft-SAGIN executes wireless (spectrum) and wired (computing and storage) types of resources when serving this slicing service. This is one crucial highlight of our Slice-Soft-SAGIN framework. With the purpose of confirming merits of Slice-Soft-SAGIN, the comprehensive evaluation work is executed. Typical works and three extra counterparts constitute the whole evaluation. We analyze the evaluation results carefully.

How to improve the coverage capability of network including connectivity and throughput is vital for enabling the Internet of Everything (IoE) in B5G/6G. However, the traditional terrestrial networks are confronted with the high-cost and inflexible challenges especially in the remote area and emergency applications. In order to solve these challenges, we consider a heterogeneous aerial network (HetAN), where some low-altitude base stations (LBSs) are deployed as access points for wireless coverage and a high-altitude base station (HBS) hovers as the hub for backhaul of LBSs. Furthermore, we apply the non-orthogonal multiple access (NOMA) to uplink transmission for the terrestrial users, which enable massive connectivity in the IoE. To maximize connectivity and throughput, we jointly optimize the LBSs’ deployment, power control, channel allocation, and rate control by fully exploiting the potential of the HetAN in wide-area coverage. For solving the formulated problem efficiently, we propose an iterative algorithm based on the methods of graph theory, bionic algorithm, and theoretical analysis. Simulation results are provided to reveal the influence of the control variables on network performance and indicate that our algorithm can greatly improve the connectivity and throughput with the other schemes.

Vehicle-to-X (V2X) with specific user-defined performance metrics emerges as one vital application scenario in sixth generation (6G) networks. According to released reports and whitepapers, 6G networks are designed to have softwarization attributes. Through softwarization, tailored V2X services, constituted by softwarized (resource and function) blocks, can be deployed on top of underlying network appliances. Chaining these softwarized blocks in predefined order is abbreviated as service function chain (SFC). However, existing SFC studies are conducted on the assumption that no performance descending of network appliance exists. Due to inherent shortcomings of softwarization, the softwarized 6G network appliances will bring about performance descending, compared with dedicated hardware. Emerging digital twin (DT) technology paves one way for studying SFC placement and scheduling without worrying about performance descending. By creating a digital replica of softwarized 6G networks, tailored SFCs can be implemented efficiently. This article investigates the SFC placement and scheduling in DT-em-powered softwarized 6G networks for emerging V2X. First, a novel business model and a problem model are presented. Next, one framework design, abbreviated as DT-SFC-6G, is proposed, including all technical details. The DT-SFC-6G design guarantees to provide efficient placement and scheduling solution per SFC request and feedback SFC solution and digital replica's state to softwarized 6G networks in time. Furthermore, experimental work of DT-SFC-6G is conducted. A variety of SFC algorithms, inserted in the DT-SFC-6G design, are evaluated in order to highlight the feasibility and merits of DT-SFC-6G. Finally, three most promising directions of SFC in DT-empowered softwarized 6G networks are selected for discussions.

Cooperative intelligent transport system (C-ITS) is one emerging application scenario in 6G. Within the content of 6G, softwarization is the dominant attribute of networks. 6G networks are required to have the intelligence and autonomy attributes, too. With softwarization and autonomy, not only the network capable of flexibly managing softwarized resources can be achieved, but also the network can learn and adapt itself with respect to the dynamic networking environment. However, multiple issues stand in the way of developing 6G networks, requiring to be addressed. In this paper, the softwarized resource management and allocation with autonomy and intelligence awareness in 6G networks for C-ITS application is researched. Firstly, key enabling technologies and problem model of 6G-enabled C-ITS are described. Then, an architecture design enabling to achieve the intelligent and softwarized resource management and allocation per service request, abbreviated as ReMaAl-AutoNet, is proposed. The proposed architecture design, based on reinforcement learning (RL), can realize the intelligent resource management and allocation by undergoing the training. Afterwards, simulations are illustrated to validate the proposed ReMaAl-AutoNet architecture. For instance, the successful ratio of ReMaAl-AutoNet has an advantage of over ten percentages than the direct counterpart without training.

It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.

Nowadays, drones are widely used in many fields because of their flexibility, scalability, low cost, and easy deployment. However, their security issues are an important concern in communication networks. Blockchain, a distributed network technology, is a promising solution in providing secure, transparent, and efficient data transmission for drone systems. Moreover, edge and cloud computing can further assist blockchain-based drone systems with stronger data storage and processing capabilities. In this article, we propose a drone-assisted and blockchain-enabled edge-cloud computing network framework (DBECN), where drones can act as moving terminals and moving base stations for different scenarios, such as crowd monitoring, disaster relief, concerts, and sparsely populated area coverage. Integrated with terrestrial edge nodes and cloud centers, the framework can also provide plenty of caching and computation resources for delay-sensitive and resource-demanding task processing, and big data analysis, caching, and processing. In particular, we present a secure and credible data sharing scheme under our DBECN framework, where we deploy blockchain in the edge nodes to provide secure and efficient data storage and sharing among different terminal-based drones.

Network virtualization (NV) is a highprofile way to solve the ossification problem of the nowadays Internet, and be able to support the diversified network naturally. In NV, Virtual Network Embedding (VNE) problem has been widely considered as a crucial issue, which is aimed to embed Virtual Networks (VNs) onto the shared substrate networks (SNs) efficiently. Recently, some VNE approaches have developed Node Ranking strategies to drive and enhance the embedding efficiency. Node Ranking Strategy rank/sort the nodes according to the attributes of the node, including both residual local attributes (CPU, Bandwidth, storage, Etc.) and the global topology attributes (Number of neighborhood Nodes, Delay to other nodes, Etc.). This paper presents an overview of Node Ranking Strategies in Virtual Network Embedding, and possible directions of VNE Node Ranking Strategy.

In this paper, we investigate the downlink performance of cell-free massive multi-input multi-output non-orthogonal multiple access (CF-mMIMO-NOMA) system with conjugate beamforming precoder and compare against the orthogonal multiple access (OMA) counterpart. A novel achievable closed-form spectral efficiency (SE) expression is derived, which characterizes the effects of the channel estimation error, pilot contamination, imperfect successive interference cancellation (SIC) operation, and power optimization technique. Then, motivated by the closed-form result, a sum-SE maximization algorithm with the sequential convex approximation (SCA) is proposed, subject to each AP power constraint and SIC power constraint. Numerical experiments indicate that the proposed sum-SE maximization algorithms have a fast converge rate, within about five iterations. In addition, compared with the full power control (FPC) scheme, our algorithms can significantly improve the achievable sum-SE. Moreover, NOMA outperforms OMA in many respects in the presence of the proposed algorithms.

Virtual network embedding (VNE) is the virtualized node and link resources allocation problem in network virtualization environment, aiming at achieving the simultaneous optimal node and link mapping assignment per VN. Currently, a large number of mapping algorithms for VNE exist in the academia. Existing algorithms mostly focus on mapping each virtual network (VN) in two ordered stages: First virtual node mapping stage and second virtual link mapping stage, leading to non-optimal VN mapping assignment. Though multiple one-stage algorithms exist, adopting either optimization theory or graph theory, they usually involve much more VN assignment calculation time. Hence, these one-stage algorithms are not suitable for dynamic network scenario. Not to mention practical VNE application. Based on the background and our gained research results, we propose another heuristic mapping algorithm VNE-ARS, completing all nodes and links per VN in one single embedding stage. Each VN embedding assignment can be completed in polynomial time, using our VNE-ARS. Main network attributes and resources are collaborated, serving as the mapping criterion of our VNE-ARS. In order to highlight our VNE-ARS strength, we conduct the evaluation experiments. We compare our VNE-ARS algorithm against existing mapping algorithms.

A downlink cell-free massive Multipleinput multiple-output (mMIMO) system is explored. To investigate the impact of hardware distortion on downlink cell-free mMIMO, this paper recalls a well-established model of hardware impairment and derives a new closedform downlink per-user Spectral efficiency (SE) expression for distributed conjugate beamforming precoding. Based on this trackable expression, two power control algorithms, namely max-total-SE and mixed Quality-of-service (QoS) algorithms, are proposed. Numerical results indicate that the downlink per-user SE is primarily limited by the hardware quality of the receiver, especially when the user corresponds to the receiver. In addition, the proposed max-total-SE algorithm has a fast convergence rate and can significantly improve the total SE compared to the Equal power control (EPC) scheme. What's more, our mixed QoS algorithm also performs well in many respects.

In this paper, the uplink performance of cell-free massive multi-input multi-output (mMIMO) system with the zero-forcing (ZF) and conjugate beamforming (CB) receivers is investigated. A novel tight approximate rate expression for the ZF receiver is derived, which provides us with a tool for easily quantifying the impacts of the multi-antenna access point (AP), estimation error, pilot contamination, and power control scheme. Then, leveraging on the trackable ZF rate expression and the pre-studied CB rate expression, two power control algorithms are proposed. In particular, the first algorithm elaborates on maximizing the total rate, subjecting to the quality-of-service (QoS) constraint and each user power constraint. This algorithm utilizes the sequential convex approximation method to solve the non-convex issues. In addition, the second aims at maximizing the rate of one user, in which the remaining users can meet their QoS constraints. It can be characterized as a geometry programming. Simulation results are provided to show that the ZF receiver is superior to the CB receiver in terms of sum rate and our proposed algorithms work well in many respects.

Network virtualization (NV) is pushed forward by its proponents as a crucial attribute of next generation network, aiming at overcoming the gradual ossification of current networks, particularly to the worldwide Internet. Through virtualization, multiple customized virtual networks (VNs), requested by users, are allowed to coexist on the underlying substrate networks (SNs). In addition, the virtualization scheme contributes to sharing underlying physical resources simultaneously and seamlessly. However, multiple technical issues still stand in the way of NV successful implementation. One key technical issue is virtual network embedding (VNE), known as the resource allocation problem for NV. This paper conducts a survey of embedding algorithms for VNE problem. At first, the NV business model for VNE problem is presented. Then, the latest VNE problem description is presented. Main performance metrics for evaluating embedding algorithms are also involved. Afterwards, existing VNE algorithms are detailed, according to the novel proposed category approach. Next, key future research aspects of embedding algorithms are listed out. Finally, the paper is briefly concluded.

Network virtualization (NV) is considered as an enabling tool to remove the gradual ossification of current Internet. In the network virtualization environment, a set of heterogeneous virtual networks (VNs), isolated from each other, share the underlying resources of one or multiple substrate networks (SNs) according to the resource allocation strategy. This kind of resource allocation strategy is commonly known as so called Virtual Network Embedding (VNE) algorithm in network virtualization. Owing to the common sense that VNE problem is NP-hard in nature, most of VNE algorithms proposed in the literature are heuristic. This paper surveys and analyzes a number of representative heuristic solutions in the literature. Apart from the analysis of representative heuristic solutions, a taxonomy of the heuristic solutions is also presented in the form of table. Future research directions of VNE, especially for the heuristics, are emphasized and highlighted at the end of this survey.

Satellite communication systems (SCS) operating on frequency bands above 10GHz are sensitive to atmosphere physical phenomena, especially rain attenuation. To evaluate impairments in satellite performance, stochastic dynamic modeling (SDM) is considered as an effective way to predict real-time satellite channel fading caused by rain. This article carries out a survey of SDM using stochastic differential equations (SDEs) currently in the literature. Special attention is given to the different input characteristics of each model to satisfy specific local conditions. Future research directions in SDM are also suggested in this paper.

Network virtualization (NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks (VNs) with different protocols to coexist on a shared substrate network (SN). One main challenge in NV is virtual network embedding (VNE). VNE is a NP-hard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in small-scaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT (Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNE-RSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNE-RSOT, a simulation against typical and state-of-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request (VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

Network virtualization is an enabling technology of running multiple virtual networks on a shared substrate network. It aims to deal with the ossification of current network architecture. As a crucial component of network virtualization, virtual network embedding (VNE) can efficiently and effectively allocates the substrate resource to proposed virtual network requests. According to the optimization strategy, VNE approaches can be classified into three categories: exact, heuristic and meta-heuristic solution. The VNE exact solution is the foundation of its corresponding heuristic and meta-heuristic solutions. This paper presents a survey of existing typical VNE exact solutions, and open problems for the future research of VNE exact solutions are proposed.

This article takes the first look at jamming attacks on both uplink training and data transmission phases in uplink cell‐free massive multi‐input multi‐output systems. Closed‐form rate expressions for that system are derived, which provide us with the opportunities to analyze the achievable rate in the presence of jamming. To compensate for the sum‐rate degradation caused by jamming, we propose an iterative sum‐rate maximization power control algorithm without redesigning the estimator and receiver. Specifically, by virtue of the Lagrange multiplier methodology, the proposed algorithm can determine the closed‐form solution to each iteration and thus, benefiting from less runtime. Simulation results reveal the effectiveness of the proposed algorithm. The jamming attack on both uplink training and data transmission phases in uplink cell‐free massive multi‐input multi‐output systems is investigated. An iterative sum‐rate maximization power control algorithm without redesigning the estimator and receiver is proposed.