Uncertain Channel State Information Research Articles

A novel multicarrier index keying‐aided non‐orthogonal multiple access systems in presence of uncertain channel state information

SummaryThis paper investigates the performance of novel multicarrier index keying‐aided non‐orthogonal multiple access (MCIK‐NOMA) system. Unlike the classical scheme, the proposed system sends the information through both index domain and constellation domain symbols thereby increases the spectral efficiency. The impact of channel state information (CSI) uncertainty on the system's performance is investigated. More specifically, the performance of MCIK‐NOMA under three different CSI conditions is analyzed: perfect CSI, MMSE‐based variable CSI, and fixed CSI uncertainty. This paper also discusses the optimum power difference needed for the successful separation of symbols at the SIC receiver by alternating the power ratio in the proposed system. The influence of selecting different active subcarriers and modulation techniques on the system's performance is studied in detail. The simulation results show that the proposed system achieves better BER and spectral efficiency and outperforms the existing systems.

Learning-Based Robust Resource Allocation for D2D Underlaying Cellular Network

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

Energy-Efficient Resource Allocation for NOMA-Based Heterogeneous 5G Mine Internet of Things

The Mine Internet of Things (MIoT), as an application paradigm of the IoT in mine environment, realizes the state perception and information interaction by connecting massive smart sensing devices deployed in mine, plays an important role in coal mine production. The fifth-generation (5G) is a key enabling technology to provide an efficient and reliable communication link guarantee for MIoT networks. To meet the increasing demand for huge amounts of data transmission, a NOMA-based heterogeneous MIoT communication system is proposed. In this paper, different types of mine smart devices can occupy the same subchannel resources for data transmission by the NOMA technology, which improves the device access and spectrum utilization of the MIoT system. We aim to maximize the energy efficiency (EE) of all small cell networks via power allocation and subchannel assignment. Under the imperfect channel state information (CSI), a joint power allocation and subchannel assignment iterative algorithm is proposed. Specifically, firstly, by considering the cross-layer interference power constraints, maximum power constraints and QoS constraints, the EE optimization problem is formulated as a mixed integer nonlinear fractional programming problem. Secondly, the uncertain CSI is modeled as an elliptical uncertainty set, and the original problem is transformed into an equivalent convex optimization form by using the Dinkelbach method. Finally, an approximate solution is obtained by using the Lagrangian dual approach. The numerical results validate the effectiveness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.

Robust Resource Allocation for Vehicular Communications With Imperfect CSI

The resource allocation in vehicle-to-everything (V2X) communications face a great challenge for satisfying the heterogeneous quality of service (QoS) requirements of the ultra-reliable safety-related services and the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. In this paper, we first consider an optimistic scenario where the distribution of uncertain channel state information (CSI) can be deterministic and accurately known at the eNB. Then, a low-complexity resource allocation approach is developed, in which the probabilistic QoS constraint of V2V is transformed into a computable optimization constraint. To deal with the scenario with unknown uncertain CSI distribution, we develop a distributionally robust resource allocation approach for converting the intractable chance constraint of V2V into a deterministic semidefinite constraint based only on the first- and second-order moments of uncertain CSI. For alleviating the conservatism of above approach, a support-based distributionally robust resource allocation approach is developed to tighten the semidefinite constraint by utilizing the support information of uncertain CSI. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

RETRACTED ARTICLE: Application of deep and robust resource allocation for uncertain CSI in English distance teaching

In order to ensure that channel state information and interference channel state information can be defined at the same time in random access wireless networks, we propose a deeply robust resource allocation architecture. The resource allocation architecture regards the goal of optimizing wireless resources as a learning problem, and uses deep neural networks to learn the best resource allocation strategy. An elliptical shape is modeled for robust resources with uncertain channel state information (CSI) depth, and a network structure composed of two cascaded deep neural network (DNN) units is proposed. First, it is a resource processing unit with uncertain CSI depth and robustness, and the second is a power control unit. Then, an alternative iterative learning algorithm is proposed for joint training of two cascaded DNN units. Finally, the simulation compares the performance of the network under the robust learning strategy and the non-robust learning strategy, and verifies the effectiveness of the proposed algorithm. Due to the continuous innovation of modern education technology, when English distance teaching cannot be carried out in the classroom face-to-face teaching, online English distance teaching becomes a necessary method. The live English distance teaching on the Internet has developed rapidly in the context of globalization. The goal of teaching is to train talents according to the requirements of the society. It has played an active role in promoting the subjective initiative of students, fully demonstrating the convenience of the information society and the balance learning resources play an important role.

Robust energy efficient maximization in wireless powered CRNs based on power splitting

In this paper, we study the optimal design for simultaneous wireless information and power transfer (SWIPT) in cognitive radio networks (CRNs), where each secondary receiver (SR) can harvest energy and decode information using the signals received from the secondary transmitter (ST). It is assumed that the ST and SR adopt orthogonal frequency division multiplexing (OFDM) modulation. Each SR uses the power splitting (PS) to coordinate the process of energy harvesting (EH) and information decoding (ID). The optimization problem of maximizing the energy efficiency of the secondary system is formulated. In particular, in order to accurately analyze the uncertain channel state information (CSI) and improve the performance of wireless networks in dynamic environments, the probability constraints are introduced. To settle this non-convex problem, the nonlinear fractional programming is utilized. Then we decompose the equivalent optimization problem into two convex subproblems, and the probability constraints of signal-to-interference-plus-noise ratio (SINR) are converted to deterministic ones by integral transformation. An efficient iterative algorithm for robust resource allocation is presented. Finally, simulation results have verified the effectiveness of the proposed algorithm.

Game based robust power allocation strategy with QoS guarantee in D2D communication network

Device to device (D2D) communication, as a proximity communication technique that leverages the spatial–temporal locality of mobile data usage to achieve one to many simultaneous transmission, provides an effective solution for unloading heavy traffic. However, due to the sharing of spectrum resources, interference management has become a major challenge for D2D communication. In this paper, a game-based robust power allocation strategy with quality of service (QoS) guarantee is designed for D2D multicast network. A Stackelberg game is proposed to characterize the actions of the base station (BS) and D2D users (DUEs). In the game, the BS is seen as the leader, whereas the D2D users are followers. On the one hand, the purpose of BS is to maximize the leader’s profit with the constraint of the maximum tolerable interference. On the other, the DUEs attempt to maximize the followers’ profit in the network system while guaranteeing the QoS requirements for DUEs, which are modeled as probability constraints. Spectrum sharing is assumed among the cellular users (CUEs) and DUEs. Because of the dynamic characteristics of wireless channel, it is difficult and expensive to acquire accurate channel state information (CSI). On this account, the uncertain channel state information is considered in the formulated problem, and the probability threshold method is utilized to convert the uncertain constraints into tractable ones. Furthermore, a distributed algorithm to determine the optimal solutions is proposed, which significantly reduces the information exchanges. Finally, numerical results validate the performances of the proposed scheme in the aspects of convergence, utility of uses, and power consumption.

Fuzzy Matching Learning for Dynamic Resource Allocation in Cellular V2X Network

Benefiting from the global deployment and fast commercialization of cellular systems, cellular-enabled vehicle-to-everything (V2X) opens up a significant prospect in the foreseeable future, for supporting proliferating vehicular services. Reliable and efficient resource allocation is a prerequisite for enjoying excellent quality of vehicular communication experiences, which yet still remains a serious challenge due to the inherent vehicle mobility and network heterogeneity. In this work, taking the rapidly varying channel state information (CSI) into consideration, we investigate the joint time-frequency allocation problem for the heterogeneous cellular V2X network, where resource sharing based on hierarchical reuse among different V2X communication types are granted. To address the high dynamics of V2X environment, a mapped fuzzy space is introduced, in which the uncertain CSI is interpreted as fuzzy numbers. This could effectively avoid the deterioration of such dynamic environment to the stability and convergence of optimization mechanisms. On this basis, we construct a two-side many-to-many fuzzy matching game (MM-FMG) to formulate the optimization problem with the dynamic and uncertain information, and further propose a vehicle-resource dynamic matching algorithm to solve this MM-FMG problem. Our developed scheme can achieve both reliable and efficient resource assignment by excluding the precise CSI, which is hence more suitable to the concerned V2X scenarios. Finally, the superior performances of our designed algorithm are demonstrated via numerical simulations.

CSI learning based active secure coding scheme for detectable wiretap channel

In this study, the authors consider the problem of secure and reliable communication with uncertain channel state information (CSI) and present a new solution named active secure coding which combines the machine-learning methods with the traditional physical-layer secure coding scheme. First, the authors build a detectable wiretap channel model by combining the hidden Markov model with the compound wiretap channel model, in which the varying of channel block CSI is a Markov process and the detected information is a stochastic emission from the current CSI. Next, the authors present a CSI-learning scheme to learn the CSI from the detected information by the Baum–Welch and Viterbi algorithms. Then the authors construct explicit secure polar codes based on the learned CSI, and combine it with the CSI-learning scheme to form the active secure polar coding scheme. Simulation results show that an acceptable level of reliability and security can be achieved by the proposed active secure polar coding scheme.

Robust Fuzzy Learning for Partially Overlapping Channels Allocation in UAV Communication Networks

With significantly dynamic characteristics of the new aerial users, the emerging cellular-enabled unmanned aerial vehicle (UAV) communication paradigm raises great challenges to current research of UAV applications. As far as the robust channel allocation is concerned, the high mobility of UAV nodes and the unexpected disturbance of external environment would render most existing methods which rely on definite information and are vulnerable to dynamic environment, become less attractive or even invalid. In this paper, we particularly investigate a cellular-enabled mesh UAV network exploiting partially overlapping channels (POCs), and propose a distributed fuzzy space based learning scheme for POCs allocation to combat the dynamic environment. Rather than the perfect channel state information (CSI) assumption, the dynamic and uncertain CSI of UAVs is characterized by fuzzy number. On this basis, the allocation process can be implemented in a mapped fuzzy space. Integrating fuzzy-logic and game based learning, we formulate the problem of POCs assignment as a fuzzy payoffs game (FPG), and demonstrate the existence of fuzzy Nash equilibrium for our designed FPG. Then, with the derived priority vector in the fuzzy space, the equilibrium solution can be achieved by the proposed algorithm. Numerical simulations demonstrate the advantages of our new scheme.

Massive Access in Secure NOMA Under Imperfect CSI: Security Guaranteed Sum-Rate Maximization With First-Order Algorithm

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This article fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty.

Computationally efficient robust design in MIMO broadcast channels with norm bounded error models

Using multiple antennas at the transmitter or receiver sides of the communications systems is a trend with no seeming end in forthcoming standards beyond 5G. In such networks, implementing a reasonably realistic beamformer means to challenge with uncertain Channel State Information (CSI). In many published works, the original design problem is a Second Order Cone Programming (SOCP) problem, and the robust counterpart, aiming to solve the design problem with data uncertainty, is a Semidefinite Programming (SDP) problem. In this paper, we study the robust beamforming design of a Broadcast Channel (BC) in a Multiple-user Multiple-Input Multiple-Output (MU-MIMO) scenario by formulating the Sum Mean Square Error (S-MSE) minimization design problem into a single SOCP robust counterpart problem. Each semi-infinite SOCP constraint is replaced with another SOCP constraint while introducing a new slack variable as well. The arithmetic computational complexity of our method is compared with the well-known and well-studied SDP design in closed form, and it is shown that the proposed method is exponentially faster in real-world design scenarios. To better verify our proposal, extensive numerical simulations are carried out, and the results show similar performances.

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this setup, by assuming multiple-input and single-output (MISO) transmission mode, a novel robust radio resource allocation is proposed where considering uncertain channel state information, the worst case approach is applied. We consider a radio resource allocation problem with the objective to maximize the total sum rate of users subject to a minimum required rate of each slice and practical limitations of C-RAN and SCMA. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where beamforming and joint codebook allocation and user association subproblems are sequentially solved. By introducing auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm is applied. Via numerical results, the performance of the proposed algorithm is investigated versus different uncertainty level for different system parameters.

Fuzzy Reinforcement Learning for Robust Spectrum Access in Dynamic Shared Networks

The persistent increases of wireless terminals have brought about diverse shared networks, where robust and efficient spectrum reuse among heterogeneous users is of critical importance while still remains as a challenging task for practical application. In this paper, we study the problem of robust spectrum access (RSA) in a canonical wireless shared network (WSN) with fully considering the inherent dynamics of the wireless environment. The non-static features of WSNs result in uncertain channel state information (CSI) and complicated coupling interference, which can’t be directly formulated as the well-accepted crisp game model, rendering most existing perfect CSI relied approaches inefficient or even unfeasible. To address this, by interpreting the estimated CSI with uncertainty as fuzzy number, a novel framework referred to as a non-cooperative fuzzy game (NC-FG) is adopted, whereby the user utility is mapped as a fuzzy value via the user-defined fuzzy utility function. Based on the derived property of the NC-FG that fuzzy Nash equilibrium (FNE) exists, a fuzzy-logic inspired reinforcement learning (FLRL) algorithm is proposed to achieve the FNE solutions of the constructed NC-FG to obtain the RSA in dynamic WSN, with which both the iterative learning and decision making procedures are implemented in a fuzzy-space, thus the sensitiveness of our scheme to environmental variations is alleviated. Finally, numerical simulations are provided to demonstrate the convergence, effectiveness, and superiority of our proposed FLRL algorithm in dynamic WSNs.

Robust Power Allocation for Femtocell Networks Using Fuzzy Estimation of Dynamic Channel States

In this paper, the power control scheme is proposed in the two-tier femtocell network, where the wireless resources are shared with the macrocell and femtocell. Since the communication environment is uncertain and complex, channel state information (CSI) is imperfect. Hence, an effective power control scheme is essential to adapt to the changing communication environment. In the power control scheme, the uncertain CSI is formulated to adapt to the dynamics of the communication environment. Since the channel gain is varied with the changing communication environment, it is crucial for the power control scheme to determine the instantaneous channel gain. The fuzzy logic system (FLS) is proposed to estimate the instantaneous channel gain. The proposed scheme attempts to overcome the limitation of the existing methods which require the channel gain distribution to be assumed. The FLS can capture the dynamics of the changing channel feature using the achievable information in the communication system. Based on the estimation of dynamic channel gain, the power optimal allocation scheme can be developed to satisfy the power level constraint and the transmitting rate requirement. To implement the proposed scheme in real-time environments, an iterative algorithm is developed with a fast convergence speed. The simulation results demonstrate that the proposed scheme is more flexible with uncertain channels when compared with the existing method.

Cooperative OFDM-IM Relay Networks With Partial Relay Selection Under Imperfect CSI

In this paper, we investigate the performance of cooperative orthogonal frequency division multiplexing with index modulation (OFDM-IM) with the low complexity greedy detection (GD). In particular, we propose a novel partial relay selection scheme whose search criteria are designed to exploit the IM subcarriers. To provide low-complexity receiver, we further examine the energy-sensing based GD design for the cooperative OFDM-IM. For the performance analysis, we derive novel upper bound and approximate closed form solutions for both the average index error probability and the average symbol error probability over Nakagami- $m$ fading channels with imperfect channel state information (CSI) at the relays and destination. Unlike the information theoretical works, in presence of positive detection error in the relays, the derived expressions provide a useful insight into the error performance of cooperative OFDM-IM under various fading conditions. The numerical and simulation results clearly present that the proposed scheme harmonizing partially selected relays and their IM subcarriers with GD can outperform the benchmark schemes, under uncertain CSI, at reduced complexity.

The BER Analysis of MRC-Aided Greedy Detection for OFDM-IM in Presence of Uncertain CSI

This letter investigates the bit error rate (BER) performance of orthogonal frequency division multiplexing index modulation, employing the maximal ratio combining-based low-complexity greedy detector (MRC-GD) and the PSK modulation. For performance analysis, we derive tight expressions for both index error probability (IEP) and BER, taking into account channel state information (CSI) uncertainty. This provides insight into various impacts of CSI uncertainty on the diversity gain and error floor of the IEP and the BER, respectively. We clearly show that under imperfect CSI, the MRC-aided GD can perform like the MRC-maximum likelihood detector, at lower complexity. Finally, simulation results are presented to verify the accuracy of derived expressions and the theoretical analysis.

Weighted Max–Min Fairness for C-RAN Multicasting Under Limited Fronthaul Constraints

We consider downlink transmission in cloud radio access networks with multiple cochannel multicasting groups served by a group of remote radio heads (RRHs), which receive information from a base band unit via finite-capacity fronthaul links. Our aim is to jointly design RRH selection and beamforming vectors such that the minimum weighted data rate among users is maximized under the constraints of maximum transmit power and fronthaul capacity at each specific RRH. The problem is intractable due to the numerical difficulties of combination and nonconvex functions. Based on a semidefinite relaxation technique, bisection search, and a branch-and-bound method, we develop an upper bound, which is also the optimal solution to the original problem if the relaxation is tight. More importantly, we propose a heuristic low-complexity iterative procedure for practical applications based on the state-of-the-art sequential convex approximation. Subsequently, we modify the proposed methods for the uncertain channel state information case. To be specific, the upper bound and its suboptimal solution are altered based on the $\mathcal {S}$ -lemma while the low-complexity algorithm is tailored by using two different approximations of intractable robust counterpart. The validity of the proposed methods in the region of limited fronthaul capacity is confirmed by numerical results.

Distributed power control in crowdsensing‐based femtocell networks with QoS provisioning

AbstractWe consider mobile crowdsensing campaigns that are practiced in the emerging data collection in femtocell networks, which are effective in improving the capacity and coverage of communication users. One of the challenging issues is how to allocate the transmission power and reduce the cross‐tier and co‐tier interferences among users. This paper proposes a Stackelberg game to characterize the actions of macrocell and femtocell users. In the game, the macrocell user is seen as the leader, whereas the femtocell users are seen as the followers. For the macrocell, it aims at maximizing the leader's profit with the constraint of the maximum tolerable interference. The femtocell users attempt to maximize the followers' profit in the network system with consideration of performance guarantee in the aspects of packet delivery rate and time delay, which are modeled as probability constraints. Spectrum sharing is assumed among the macrocell and femtocells. The uncertain channel state information is considered in the formulated problem, and the probability threshold method is utilized to convert the uncertain constraints into tractable ones. Furthermore, a power control and price bargaining algorithm is derived to get the practical operation. Finally, numerical results validate the performances of the proposed scheme in the aspects of convergence, utility of uses, and power consumption.

Design of Robust Transceiver for Precoded Uplink MU-MIMO Transmission in Limited Feedback System

This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation.