Imperfect Channel Information Research Articles

Performance evaluation of energy harvesting-enabled NOMA under imperfect [formula omitted] shadowed fading channel information and hardware impairment

Energy harvesting (EH)-enabled nonorthogonal multiple access (NOMA) is one of wireless communication technologies which has superiority of energy and spectral efficiencies. Nevertheless, EH-enabled NOMA is affected by multifarious practical factors including EH nonlinearity, channel impairments (fading, shadowing, path loss), and more especially, imperfect channel information and hardware impairment. Consequently, the paper evaluates swiftly its performance accounting for these factors by proposing explicit computational expressions for system throughput, energy efficiency, and outage probability. The findings reveal that system performance is significantly mitigated by these factors. Notwithstanding, it is improved, optimized, complete outage-avoided by selecting appropriate parameters. Furthermore, the findings indicate the advantage of NOMA to the conventional orthogonal multiple access.

MIMO NOMA with Nonlinear Energy Harvesting and Imperfect Channel Information

MIMO NOMA with Nonlinear Energy Harvesting and Imperfect Channel Information

Energy Constrained Sum-Rate Maximization in IRS-Assisted UAV Networks With Imperfect Channel Information

The focus of this work is maximizing the sum-rate of wireless unmanned aerial vehicle (UAV) networks with intelligent reflecting surfaces (IRS) in the presence of system practical limitations. More specifically, we consider that the phase compensation at the IRS is imperfect due to various factors such as device imperfections and channel estimation errors. Moreover, we consider that the IRS elements have limited switching frequency, which limits the possibility of being allocated to different UAVs over consecutive time slots when time-division multiple access (TDMA) is considered. To this end, we formulate an optimization problem where the objective is to maximize the network sum-rate subject to total energy and quality-of-service (QoS) constraints by optimizing the number of IRS elements and power allocated to each UAV. To solve the optimization problem, a low-complexity heuristic algorithm is proposed based on the quality of the estimated phase for each IRS element. The proposed approach is compared to benchmark techniques such as the uniform allocation process and genetic algorithm. The obtained results show that a significant sum-rate improvement of up to 45% can be gained using the proposed algorithm.

Fronthaul Compression for Uplink Massive MIMO Using Matrix Decomposition

Massive multiple-input-multiple-output (MIMO) is a key enabler for obtaining higher data rates in the next generation wireless technology. While it has the power to transform cellular communication, with potential for spatial diversity and multiplexing, a bottleneck that often gets overlooked is the fronthaul capacity. The fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries in-phase and quadrature (IQ) samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied, particularly in the uplink. This paper proposes an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems utilizing the convolution structure of the received signals. The proposed algorithm provides compression ratios of about 30-50×. This work provides extensive analysis of the performance of the proposed method for a plethora of practical scenarios and constraints, such as different channel parameters and models, receive antenna correlation, and under imperfect channel information. It also discusses the numerical convergence and complexity of the proposed algorithm and compares the performance against other existing compression techniques.

Performance of modified generalized quadrature spatial modulation over correlated fading channels with imperfect channel information

AbstractA generalized version of the quadrature spatial modulation (QSM) scheme is called a modified generalized QSM (mGQSM), which is proposed to enhance the spectral efficiency of QSM by considering multiple radio frequency chains. In QSM, the in‐phase and quadrature dimensions are used to transmit the data symbols. Using the QSM transmission principle, the mGQSM scheme can transmit the data symbols on different antennas ranging from to . Moreover, the proposed scheme provides an additional one bit data rate when compared to generalized QSM (GQSM). This paper presents the performance of mGQSM under imperfect channel conditions over correlated Rayleigh, Rician, and Nakagami‐m fading channels. For the computer simulations, we assume and various fading parameters for the Rician and Nakagami channels. The error performances of the mGQSM, GQSM, and QSM schemes are evaluated using the maximum‐likelihood (ML) detector. Under imperfect channel conditions, the mGQSM and QSM schemes are examined and compared. Furthermore, a low complexity ordered block minimum mean‐squared error (OB‐MMSE) detector is employed for the reduced‐codebook mGQSM (RC‐mGQSM), which achieves near‐ML performance with reduced complexity.

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach.

The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

Joint Energy-Efficient Power Allocation and Beamforming for Uplink mmWave-NOMA System

In this paper, we study the joint power allocation (PA) and beamforming (BF) design problem to maximize the energy efficiency (EE) for an uplink millimeter-wave (mmWave) system with non-orthogonal multiple access (NOMA). To track this non-convex optimization problem, we decompose it into the PA problem and BF design problem. For any feasible fixed BF, the condition of the optimal decoding order is firstly presented to reduce the decoding complexity, and then the optimal and suboptimal PA solutions with closed-form expressions are derived, respectively. On this basis, two suboptimal joint design schemes of PA and BF are proposed based on the one-dimensional search method. Furthermore, the extensions of the proposed schemes to mmWave-NOMA system with imperfect channel information and more users are also presented. Simulation results verify the effectiveness of the proposed two joint schemes, and they can achieve superior EE performance over the existing schemes and the conventional time division multiple access scheme.

Analog Beamforming in Millimeter Wave MIMO Systems

In traditional analog beamforming schemes, like the beam selection method, use the strongest path array steering vector of the channel to generate a beam pointing to the user. In multi-user systems, such schemes will result in the large interference among the users, especially when the users are closely located. In this paper, we designed an analog beamforming scheme for downlink mm-wave multi-user systems to enhance the beamforming gain and suppress the inter-user interference at the same time. A multi-objective problem is developed to beat a balance between the inter-user interference and the beamforming gain. To solve the problem, we firstly use the weighted-sum method and then -constraint method to transform the multi-objective problem into a single-objective problem. Then, the analog beamforming is made tractable with the constant-magnitude constraints with the use of semidefinite programing technique. Adding to these, the robust beamforming is designed to mitigate the effects of the channel estimation and to provide the robustness against the imperfect channel information. The simulation results shows that the -constraint method outperforms when compared with the weighted-sum method at high SNR’s for the robust multi-user analog beamforming.

Secure Beamforming for Cognitive Satellite Terrestrial Networks With Unknown Eavesdroppers

This article proposes a beamforming (BF) scheme for a cognitive satellite terrestrial network, where the base station (BS) and a cooperative terminal (CT) are exploited as green interference resources to enhance the system security performance in the presence of unknown eavesdroppers. Different from the related works, we assume that only imperfect channel information of the mobile user (MU) and earth station (ES) is available. Specifically, we formulate an optimization problem with the objective to degrade the possible wiretap channels within the private signal beampattern region, while imposing constraints on the signal-to-interference-plus-noise ratio (SINR) at the MU, the interference level of the ES and the total transmit power budget of the BS and CT. To solve this mathematically intractable problem, we propose a joint artificial noise generation and cooperative jamming BF scheme to suppress the interception. Finally, the effectiveness and superiority of the proposed BF scheme are confirmed through computer simulations.

Power control of D2D communication based on quality of service assurance under imperfect channel information

In cellular networks, proximity users establish device-to-device(D2D) links directly under the control of base stations (BSs) for data exchange, and no base station forwarding is required. For the advantages such as lower transmission power, higher data transmission rate, D2D communication has been paid more and more attention. However, the environmental impact of introducing D2D shared spectrum resources has become a major challenge. This paper studies a robust downlink power control scheme with imperfect channel state information (CSI) in D2D communication system. In order to improve the system sum rates, this paper optimizes the transmitting power of D2D users (DUEs) and the base stations through geometric analysis and convex optimization while guaranteeing the quality-of-service (QoS) requirements for both DUEs and cellular users (CUEs). Because of the dynamic characteristics of wireless channel, it is difficult and expensive to acquire CSI without direct communication with BS. On this account, we consider channel uncertainty rather than perfect or instantaneous known CSI. Aiming at enhancing the system robustness, we formulate an outage-based robust optimization problem. The original deterministic the signal-to-interference-noise-ratio (SINR) constraints are substituted with the transformed probability constraints, which transforms the random channel gain into a deterministic one with outage threshold. The simulation results show that the power allocation based on the proposed algorithm can adapt to the complex channel environment and are with better robustness.

Communication Under Channel Uncertainty: An Algorithmic Perspective and Effective Construction

The availability and quality of channel state information heavily influences the performance of wireless communication systems. For perfect channel knowledge, optimal signal processing and coding schemes have been well studied and often closed-form solutions are known. On the other hand, the case of imperfect channel information is less understood and closed-form characterizations of optimal schemes remain unknown in many cases. This paper approaches this question from a fundamental, algorithmic point of view by studying whether or not such optimal schemes can be constructed algorithmically in principle (without putting any constraints on the computational complexity of such algorithms). To this end, the concepts of compound channels and averaged channels are considered as models for channel uncertainty and block fading and it is shown that, although the compound channel and averaged channel themselves are computable channels, the corresponding capacities are not computable in general, i.e., there exists no algorithm (or Turing machine) that takes the channel as an input and computes the corresponding capacity. As an implication of this, it is then shown that for such compound channels, there are no effectively constructible optimal (i.e., capacity-achieving) signal processing and coding schemes possible. This is particularly noteworthy as such schemes must exist (since the capacity is known), but they cannot be effectively, i.e., algorithmically, constructed. Thus, there is a crucial difference between the existence of optimal schemes and their algorithmic constructability. In addition, it is shown that there is no search algorithm that can find the maximal number of messages that can be reliably transmitted for a fixed blocklength. Finally, the case of partial channel knowledge is studied in which either the transmitter or the receiver have perfect channel knowledge while the other part remains uncertain. It is shown that also in the cases of an informed encoder and informed decoder, the capacity remains non-computable in general and, accordingly, optimal signal processing and coding schemes are not effectively constructible.

Specific Absorption Rate-Aware Beamforming in MISO Downlink SWIPT Systems

This paper investigates the optimal transmit beamforming design of simultaneous wireless information and power transfer (SWIPT) in the multiuser multiple-input-single-output (MISO) downlink with specific absorption rate (SAR) constraints. We consider the power splitting technique for SWIPT, where each receiver divides the received signal into two parts: one for information decoding and the other for energy harvesting with a practical non-linear rectification model. The problem of interest is to maximize as much as possible the received signal-to-interference-plus-noise ratio (SINR) and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints by optimizing the transmit beamforming at the transmitter and the power splitting ratios at different receivers. The optimal beamforming and power splitting solutions are obtained with the aid of semidefinite programming and bisection search. Low-complexity fixed beamforming and hybrid beamforming techniques are also studied. Furthermore, we study the effect of imperfect channel information and radiation matrices, and design robust beamforming to guarantee the worst-case performance. Simulation results demonstrate that our proposed algorithms can effectively deal with the radio exposure constraints and significantly outperform the conventional transmission scheme with power backoff.

Online and robust resource allocation for D2D communications assisted by Green relays

Device-to-device (D2D) communications in cellular networks allow proximity users to communicate directly without going through the base station. Concentrating on scenarios where the direct communication between potential D2D users may not be reliable due to poor proximity and link condition, and paying attention to energy harvesting wireless communications, this study focuses on the green relay-assisted D2D communications, where long-term evolution-advanced layer-3 relays powered by renewable energy are employed. To achieve the trade-off between system capacity maximisation and user fairness, the authors formulate an offline resource block (RB) and power allocation problem under imperfect channel information with the objective of maximising the weighted sum-rate of all users. Then, based on battery dynamics, the original problem is reformulated as an online one with energy outage avoidance. To obtain distributed solutions, the online problem is further transformed into a convex one, and the rate outage-probability requirement is also converted for tractability. Finally, applying Lagrangian duality, the distributed RB and power allocation is derived, which is globally optimal to the reformulated online problem. Comparing with a baseline scheme where all users communicate directly without assistance of relays, numerical results show the superiority of the proposed approach in terms of the average achievable data rate.

Impact of Wireless Backhaul Unreliability and Imperfect Channel Estimation on Opportunistic NOMA

We propose a new opportunistic non-orthogonal multiple access (NOMA) scheme under wireless backhaul unreliability and fronthaul channel uncertainty, where fronthaul represents regular radio access link. In particular, we propose two opportunistic methods for the proposed NOMA, which allow the best transmitter selection approaches based on either a near or a far-away receiver, considering the wireless backhaul unreliability and the fronthaul fading impairment. For the performance analysis, new closed-form expressions of exact and approximated outage probabilities of the grouped receivers are derived. The theoretical analysis provides an insight into the impact of wireless backhaul unreliability and imperfect channel estimation on the behaviour of outage probabilities at NOMA receivers. Furthermore, we analytically investigate how the number of multiple transmitters in the proposed opportunistic NOMA can determine the outage floors. We show that under unreliable wireless backhauls, a dominant receiver in the proposed NOMA scheme can achieve more than 3 dB gain in outage performance, compared to the orthogonal multiple access. In addition, the outage probability at a dominant receiver is less influenced by imperfect channel information, while the outage probability at a non-dominant receiver is significantly sensitive. The analytical expressions and asymptotic results have been validated through Monte Carlo simulations, thus verifying the derived impact analysis of NOMA under wireless backhaul unreliability and imperfect channel estimation.

Multi-User Analog Beamforming in Millimeter Wave MIMO Systems Based on Path Angle Information

We aim to design an analog-only beamforming scheme for downlink multi-user mm-wave systems to optimize the beamforming gain and the inter-user interference at the same time. Traditional analog beamforming schemes, such as the beam selection method, use the array response vector corresponding to the strongest path of the channel to generate a beam pointing to the user. In multi-user systems, such schemes will lead to large inter-user interference, especially when the users are closely located. In this paper, we formulate a multi-objective problem to strike a balance between the beamforming gain and the inter-user interference. To solve the problem, we first use the weighted-sum method to transform the multi-objective problem into a single-objective problem. Then, we use the semi-definite programing technique to make the analog beamforming with constant-magnitude constraints tractable. Furthermore, to alleviate the effects of the channel estimation and feedback quantization errors, we design a robust beamforming scheme to provide robustness against imperfect channel information. We first develop a channel error model for the scattering clustered channel model, which can serve as a general channel error model for the mm-wave channels. Then, we formulate a multi-objective problem using the stochastic approach to suppress the interference and enhance the beamforming gain at the same time. The simulation results show that our proposed non-robust multi-user analog beamformer outperforms the traditional analog beamforming method when the SNR is high and our proposed robust beamformer can provide up to 109% improvement in the sum-rate compared with the beam selection method.

Precoding design for space-time codes and relay selection with a novel scheme for multiple relay systems

In this paper, we consider the joint design of precoding matrices at both the source and relay nodes in a multi-relay system based on perfect and imperfect channel information, and propose the novel relay selection scheme based on log-likelihood ratio (LLR). Moreover, the symbol error rate (SER) for relay selection system based on proposed scheme is analyzed. At first, we design the combined precoding matrices based on respectively the maximum signal-to-noise ratio (SNR) with perfect channel state information (CSI) and the minimum SER of the single-relay system with imperfect CSI. Using decomposition of matrices, the joint design problems are solved with a two-step method, i.e., the first step is to determine the optimal direction of the precoding matrices, and the second step is to transform the optimization problem into two kinds of independent sub-problems which can be solved in lower complexity. Then, different from traditional relay selection based on signal-to-noise ratio, the relay node with the maximum LLR is chosen. Simulation results show the proposed selection scheme based on LLR is superior to the scheme based on SNR in the SER performance. Furthermore, the multi-relay system using the precoding matrices joint space-time coding technology can improve SER performance of the system effectively compared with only space-time coding or only precoding system.

Constructive Interference Based Secure Precoding: A New Dimension in Physical Layer Security

Conventionally, interference and noise are treated as catastrophic elements in wireless communications. However, it has been shown recently that exploiting known interference constructively can contribute to signal detection ability at the receiving end. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). The scenario considered here is a multiple-input single-output wiretap channel with multiple Eves. This paper starts from AN design without any knowledge of Eve's CSI, builds with solutions with statistical CSI up to full CSI. Both perfect and imperfect channel information have been considered, in particular, with different extent of Eves' channel responses. The main objective is to improve the receive signal-to-interference and noise ratio at IR through exploitation of AN power in an attempt to minimize the total transmit power, while hindering detection at the Eves. Numerical simulations demonstrate that the proposed constructive AN precoding approach yields superior performance over conventional AN schemes in terms of transmit power. Critically, they show that, while the statistical constraints of conventional approaches may lead to instantaneous IR outages and security breaches from the Eves, the instantaneous constraints of our approach guarantee both IR performance and secrecy at every symbol period.

A Worst-Case Robust Combination Method of Beam-Forming and Orthogonal Space–Time Block Coding

In this paper, a new robust multi-input multi-output system is proposed in a perturbed wireless channel which is to model imperfect channel information at the source side when beam-forming and orthogonal space–time block coding is utilized. The channel perturbation is bounded by a predefined variation based on worst-case robust design. Beam-forming is used to improve the performance of the system expressed by the upper bound of pairwise error probability of symbols. In this paper firstly, the maximum value of pairwise error probability is obtained in a closed form when channel perturbation is kept below a threshold. Then the beam-forming matrix is designed to minimize the pairwise error probability subject to a predefined maximum transmitting power. This approach provides near optimal results due to using the upper bound of pairwise error probability. It shows good performance based on the symbol error rate criterion compared with other existing methods of the multiple input multiple output system.

Performance analysis of cross layer design with imperfect channel information in distributed antenna systems

In this paper, based on the imperfect channel state information (CSI), a cross layer design (CLD) scheme is developed for distributed antenna system (DAS) by combining adaptive modulation (AM) at the physical layer and automatic repeat request (ARQ) at the data link layer. The performance of DAS with CLD is investigated over composite fading channel which considers large-scale path loss and small-scale Rayleigh fading. With the performance analysis, the probability density function of the estimated signal-to-noise ratio (SNR) is derived, and then, the switching thresholds under a target packet error rate constraint are further derived. According to these results, and using numerical calculation, the closed form analytical expressions of average packet error rate and spectrum efficiency of DAS with CLD are, respectively, achieved, which will provide better evaluation way for the DAS performance. To decrease the performance loss caused by the conventional single estimation in the presence of imperfect CSI, the multi-estimation method is proposed to increase the system performance by exploiting previous channel estimation information. Numerical results corroborate our theoretical analysis, and the simulation is in consistence with the theoretical result. Moreover, the system performance can be increased by decreasing the estimation error and/or path loss. Especially, the multi-estimation method can enhance the performance effectively and enable the system to tolerate large estimation errors.

Downlink MAC Scheduler for 5G Communications With Spatial Focusing Effects

Driven by the demand for supporting the rapidly increasing wireless traffic, the next-generation communication system, i.e., the 5G system, needs to accommodate a massive number of users and judiciously manage the interference. One promising candidate, the time reversal (TR) system, uses a large bandwidth and designs transmitting waveforms, such that the environment acts as a matched filter and the transmitted signal adds up coherently at the intended users. Therefore, the energy is focused only at the intended users with reduced interference to others. The other candidate, massive MIMO system, utilizes a large number of antennas to focus on the energy to the users and reduce the mutual interference. However, the massive number of users poses a limit on system performance due to increasing interuser interference, and the system has to make a judicial selection of transmitting users. In this paper, we propose a scheduler that maximizes the system weighted sum rate while satisfying the minimum rate requirements of the transmitting users. The optimization problem is transformed into a mixed integer quadratically constrained quadratic programming with linear time complexity. We also investigate the impact of imperfect channel information on the proposed scheduler algorithm and reveal similar channel estimation error distribution between the TR and massive MIMO system. We evaluate the performance of the proposed scheduler in different scenarios and the results show that the proposed scheduler has several desirable characteristics, including low time complexity, suitable on versatile system structure, and robustness against imperfect channel information.