Multi-carrier Scenario Research Articles

A Single-Loop Algorithm for Weighted Sum Rate Maximization in Multiuser MIMO Systems With Per-Antenna Power Constraints

This paper investigates the linear precoder design for multiuser multiple-input-multiple-output (MIMO) systems, in which the problem of maximizing weighted sum rate (WSR) subject to per-antenna power constraints (PAPC) is considered. This problem is hard to solve due to its nonconvexity and the existence of multiple quadratic constraints. Conventional methods to tackle this problem, such as the iterative weighted minimum mean squared error (WMMSE) algorithm, typically consist of two nested loops and thus suffer from high computational complexity. By leveraging the inherent separability of the PAPC constraint set, we propose a low-complexity single-loop algorithm for solving the WSR maximization problem under PAPC, in which each updating step is done efficiently with closed form. Theoretically, we prove the convergence of the proposed algorithm to stationary solutions. Then we extend the proposed algorithm to the multi-carrier scenario where the power constraints are shared over the subcarriers. Complexity analysis and numerical results show that the proposed single-loop algorithm maintains the same WSR performance as existing methods but dramatically reduces the computational complexity.

Improved Channel Estimation and Removal of Fading for MIMO-OFDM Systems

Many inference algorithms develop the time-domain connection of the direct by employ a Kalman filter base on a first-order (or sometimes second-order) estimate of the time-varying channel amid a norm based on link toning (CM), or on the Minimization of Asymptotic Variance (MAV). To decrease the complexity of the high-dimensional RW-KF for combined opinion of the multi-path multifaceted amplitudes, we suggest using an inferior dimensional RW-KF that estimate the compound amplitude of each path separately. We demonstrate that this amounts to a simplification of the joint multi-path Kalman increase formulation through the Woodbury’s identities. Hence, this innovative algorithm consists of a superposition of self-determining single-path single-carrier KFs, which be optimized in our earlier studies. This examination allow us to settle in the optimization to the authentic multi-path multi-carrier scenario, to afford logical formulas for the mean-square error presentation and the best tuning of the future estimator in a straight line as a function of the physical parameter of the canal (Doppler frequency, signal-to-noise-ratio, power delay profile). These logical formula are known for the first-, second-, and third-order RW models used in the KF. The future per-path KF is exposed to be as well-organized as the accurate KF (i.e., the joint multi-path KF), and outperforms the autoregressive-model-based KFs future in the literature.

BLOCKCHAIN-ENABLED FOG RESOURCE ACCESS AND GRANTING

Fog computing is a new computing paradigm for meeting ubiquitous massive access and latency-critical applications by moving the processing capability closer to end users. The geographical distribution/floating features with potential autonomy requirements introduce new challenges to the traditional methodology of network access control. In this project, a blockchain-enabled fog resource access and the granting solution is proposed to tackle the unique requirements brought by fog computing. The smart contractual concept is introduced to enable dynamic, and automatic credential generation and delivery for an independent offer of fog resources. Negotiated transaction mechanism supports the fog resource provider to dynamically publish an offer and facilitates the choice of the preferred resource by the end-user. There is no central authority needed to verify your identity, and relieve the processing pressure brought by massive access and singlepoint failure. Our solution can be extended and used in multi-access and especially multi-carrier scenarios in which centralized authorities are absent.

Joint Power and Data Allocation in Multi-Carrier Full-Duplex Relaying Networks Operating With Finite Blocklength Codes

In this paper, we study a full-duplex (FD) relaying network operating with finite blocklength (FBL) codes. Based on Polyanskiy’s FBL model, we characterize the FBL reliability of the relaying network under both decode-and-forward (DF) and amplify-and-forward (AF) relaying schemes. Based on the characterisation, we provide reliability-optimal designs via optimal power allocation for both schemes in a single-carrier scenario. In particular, we prove that under the FD DF relaying scheme the (tightly approximated) overall error probability is convex in the transmit power at the relay. In addition, we show that minimizing the overall error probability of the FD AF relaying is equivalent to maximizing the overall signal to interference plus noise ratio (SINR), which is further proved to be pseudo-concave. Then, the designs for a single-carrier scenario are further extended to a multi-carrier scenario with a joint power and data allocation among carriers. In particular, for either the FD DF or FD AF relaying scheme, a joint optimization problem is reformulated to a single problem maximizing the reliability via finding and achieving the optimal SINRs, while auxiliary variables are introduced in FD AF relaying to facilitate the reformulation. Based on mathematical analysis, we respectively construct convex approximations and subsequently propose iterative algorithms, with which the error probability is reduced iteratively until an eventual convergence to an efficient suboptimal value. Hence, a corresponding suboptimal data and power allocation solution can be constructed for the multi-carrier scenario. Via numerical analysis, we validate our analytical model and the proposed allocation algorithms. The FD DF and FD AF relaying schemes are compared with direct transmission in both single-carrier and multi-carrier scenarios, and the benefits of applying FD relaying schemes and joint optimization among multiple carriers are observed.

Joint subcarrier and power allocation with proportional fairness in multicarrier nonorthogonal multiple access systems

SummaryIn this paper, we investigate joint subcarrier and power allocation in multicarrier nonorthogonal multiple access (MC‐NOMA) systems aims to guarantee rate proportional fairness among all users. To this end, we adopt the sum logarithmic rate function as the objective and formulate a resource optimization problem which is a mixed‐integer nonlinear programming (MINLP) problem. Based on variable relaxation and replacement, we convert this problem into a convex optimization problem. On this basis, the expressions of joint subcarrier and power allocation solutions are obtained by using convex optimization theory and the Karush–Kuhn–Tucker (KKT) condition. However, power and subcarrier allocation solutions are highly coupled, and it is difficult to derive both solutions simultaneously. Fortunately, when the subcarrier allocation strategy is given, the optimal power allocation solution can be derived by solving the equations. Furthermore, we develop an iterative resource allocation algorithm to implement joint subcarrier and power allocation among users. Numerical results reveal that the proposed algorithm is effective in general multiuser and multicarrier scenarios, and a substantial performance improvement can be achieved by the proposed algorithm in contrast with the traditional orthogonal multiple access (OMA) schemes. In specific, the proposed algorithm achieves 63% and 59% gains in terms of utility and fairness performance, respectively.

Blockchain-enabled fog resource access and granting

Fog computing is a new computing paradigm for meeting ubiquitous massive access and latency-critical applications by moving the processing capability closer to end users. The geographical distribution/floating features with potential autonomy requirements introduce new challenges to the traditional methodology of network access control. In this paper, a blockchain-enabled fog resource access and granting solution is proposed to tackle the unique requirements brought by fog computing. The smart contract concept is introduced to enable dynamic, and automatic credential generation and delivery for an independent offer of fog resources. A per-transaction negotiation mechanism supports the fog resource provider to dynamically publish an offer and facilitates the choice of the preferred resource by the end user. Decentralized authentication and authorization relieve the processing pressure brought by massive access and single-point failure. Our solution can be extended and used in multi-access and especially multi-carrier scenarios in which centralized authorities are absent.

Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks

In-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA's ICI suppression ability and the system capacity performance.

Radio-Frequency Impairments Compensation in Ultra High-Throughput Satellite Systems

The quest for massive satellite throughput is relentless, motivating the transmission of broadband signals at multiple Giga-Baud rates. Building low-cost, low-complexity user terminals is essential. These competing drivers conspire to cause analog radio-frequency (RF) components to exceed their tolerance limits. Specifically, analog mixers, anti-aliasing filters and amplification in quadrature frequency-conversion architectures create direct-current (DC) offset and in-phase/quadrature (I/Q) imbalance that is strong and frequency-selective. This paper provides two characterization models of analog RF impairments when the frequency offset is present. Novel digital compensation algorithms with immunity to frequency offset are presented, categorized into equalization with image-rejection capability and image cancellation. Adaptive techniques are utilized that operate without a priori knowledge of RF impairments. Two optimization methods are proposed for the multicarrier scenarios that are useful when using known data samples for factory calibration or in decision-directed mode during field re-calibration. Special consideration is given to estimating the frequency offset during initial factory calibration. Extensive computer simulations reveal that the proposed compensators are robust to frequency offset, provide lossless attenuation of imbalance-induced image and remove DC offset, under numerous scenarios. Cases include when the desired carrier is plagued by image interference from more than one source and on-board high-power amplifier is operated near saturation.

Hybrid beamforming for downlink multiuser millimetre wave MIMO‐OFDM systems

In this study, the authors consider multi-user millimetre wave (mmWave) multiple-input multiple-output (MIMO) downlink communications for multi-carrier scenarios, e.g. orthogonal frequency-division multiplexing (OFDM) a common analogue precoder and combiner for the transmitter and receiver to maximise spectral efficiency is the main challenge for multi-carrier MIMO systems, they propose two methods to solve this problem. The first method designs the analogue precoder and combiner based on the channel average of all subcarriers, which helps reduce computational complexity. Moreover, the channels of all subcarriers can be viewed as a third-order tensor, and the second method is based on tensor unfolding, which makes full use of the channel information of all subcarriers. When the common analogue precoder and combiner are fixed, they consider designing a digital precoder to maximise the signal-to-leakage-plus-noise ratio of each user in every subcarrier, which leads to closed-form solutions compared with the block diagonal method. Simulation results demonstrate that the performance of the proposed methods is close to that of the fully digital precoding method, and increasing the number of RF transceiver chains helps improve the performance of the proposed methods.

Maximum Likelihood Joint Angle and Delay Estimation from Multipath and Multicarrier Transmissions with Application to Indoor Localization over IEEE 802.11ac Radio

In this paper, we tackle the problem of joint angle and delays estimation (JADE) of multiple reflections of a known signal impinging on multiple receiving antennae. Based on the importance sampling (IS) concept, we propose a new non-iterative maximum likelihood (ML) estimator that enjoys guaranteed global optimality and enhanced high-resolution capabilities for both single- and multi-carrier models. The new ML approach succeeds in transforming the original multi-dimensional optimization problem into multiple two-dimensional ones thereby resulting in huge computational savings. Moreover, it does not suffer from the off-grid problems that are inherent to most existing JADE techniques. By exploiting the sparsity feature of a carefully designed pseudo-pdf that is intrinsic to the new estimator, we also propose a novel approach that enables the accurate detection of the unknown number of paths over a wide range of practical signal-to-noise ratios (SNRs). Computer simulations show the distinct advantage of the new ML estimator over state-of-the art JADE techniques both in the single- and multi-carrier scenarios. Most remarkably, they suggest that the proposed IS-based ML JADE is statistically efficient as it almost reaches the Camer-Rao lower bound (CRLB) even in the adverse conditions of low SNR levels. Using real-world channel measurements collected from four access points (APs) with IEEE 802.11ac standard’s setup parameters in an indoor environment, we also show that the proposed ML estimator achieves a localization performance below 15 cm accuracy.

Physical-Layer Multicasting Design for Downstream G.fast DSL Transmission

This paper studies the physical-layer multicasting design for downstream G.fast digital subscriber line (DSL) transmission, which corresponds to a multi-user multi-tone (i.e., multi-carrier) scenario. The design goal is to maximize the weighted-sum-group-rate (WSGR) under per-line power constraints. First, as an information-theoretic upper bound, full-rank precoding-based multicasting is considered with joint channel coding across tones. For a single multicast group, this problem corresponds to a non-linear convex semidefinite program (SDP), which is coupled across tones. To reduce the computational complexity, a Lagrange dual decomposition method is developed. This approach is then extended toward multiple multicast groups based on difference-of-convex (DC) programming. Furthermore, a practical multicasting scheme is considered based on rank-one single-stream precoding and independent per-tone channel coding. For this case, instead of relying on computationally complex semidefinite relaxation, a successive convex approximation-based trust-region algorithm is developed. Finally, the simulations of a G.fast cable binder show that the practical multicasting scheme operates close to the information-theoretic multicasting upper bound.

Power and Interference Regulated Water-Filling for Multi-Tier Multi-Carrier Interference Aware Uplink

This letter considers the optimum transmission power allocation strategy for maximizing the aggregate communication rate of a desired tier in a multi-tier network for a multi-carrier scenario. Users of the tier of interest form a multiple access channel with their base station. They share the same resources with other tiers, and cause inter-tier interference. Optimal power control for users of the tier of interest is found by maximizing their sum data rate while limiting the power transmitted per user and interference caused per tier. The power allocation is found to be a power and interference regulated water-filling solution which is a combination of conventional power-filling with interference-filling. The superiority of our proposed algorithm over orthogonal frequency division multiple access and equal power allocation schemes is demonstrated.

Joint Subcarrier Pairing and Power Allocation for Cooperative Nonorthogonal Multiple Access

In this correspondence, a downlink cooperative nonorthogonal multiple access transmission scheme is considered in the multicarrier scenario, where the user equipment (UE) with strong channel conditions acts as a relay to help the UE with weak channel conditions. A joint optimization of subcarrier pairing and power allocation is formulated as a mixed-integer nonconvex problem, with the objective to minimize the transmit power of the base station and the relay (strong UE) under the quality of service requirements of both UEs. We first explore the inherent property of power allocation for a single subcarrier pair. Then, in light of the property, we employ the Lagrange dual method and the Hungarian algorithm, in an asymptotically optimal manner, to solve the joint subcarrier pairing and power allocation problem. Numerical results show that our proposed algorithm outperforms other schemes with or without cooperation.

High-Power Multicarrier Generation for RF Breakdown Testing

Testing of satellite components for high RF power breakdown effects, such as multipactor and corona or passive-intermodulation, is a topic of growing interest in the aerospaceindustry. Switching from the classical single carrier approach to the more realistic multicarrier scenario is very challenging from the experimental point of view. The multicarrier signals, amplified by several RF power amplifiers, need to have controlled phase, amplitude, and frequency in each carrier. Fine tuning of the signal generator phases is required in order to compensate the phase drift occurring in the active elements of the test bed. This paper presents an efficient and low-cost technique to generate multicarrier signals, valid for testing vacuum breakdown phenomena in satellite components.

Cooperative Multicasting in Renewable Energy Enhanced Relay Networks—Expending More Power to Save Energy

Power and on–off control problems are examined for renewable energy enabled base-stations (BSs) and relay nodes (RNs) in cooperative multicast networks. Renewable energy is utilized at BSs and RNs to reduce the overall grid energy cost. By considering a practical energy consumption model and the statistics of the renewable energy arrival, the optimal transmit powers are first determined by minimizing the expected grid energy consumption subject to an average outage probability constraint at MUs. The optimal solution is found via line search in the general case and is obtained in closed-form at high SNR. In addition, an on–off control policy is also proposed to further reduce the basic operational energy costs. The joint on–off and power control problems are solved approximately using two sequential deflation techniques, namely, the subset-search and the convex-relaxation-based approaches. The power control problem is also extended to the multicarrier scenario with unequal transmit powers and is solved using successive convex approximation. Simulations using the photovoltaic energy arrival model are provided to demonstrate the effectiveness of the proposed schemes. The results show that expending more power at RNs allows for more efficient use of renewable energy and, thus, increases energy-savings.

Licensed-Assisted Access LTE: coexistence with IEEE 802.11 and the evolution toward 5G

LAA is a new operation mode of LTE in the unlicensed spectrum, which will be featured in LTE Release 13. Under LAA, licensed carriers will be aggregated with unlicensed carriers in order to opportunistically enhance downlink user throughput while still offering seamless mobility support. In order to coexist with WiFi, some of the new functionalities required of LAA LTE include a mechanism for channel sensing based on listen-before-talk, discontinuous transmission on a carrier with limited maximum transmission duration, and multicarrier transmission across multiple unlicensed channels. This article presents a detailed overview of the design agreements for LAA, the impact of unlicensed spectrum operation on the LTE physical layer architecture, and the scope of additional enhancements beyond LTE Release 13. A range of simulations for indoor and multicarrier scenarios show that fair coexistence between LAA and WiFi can be achieved, and that deployment of LAA can provide a boost in WiFi performance.

Optimal Resource Sharing and Network Beamforming in Multi-Carrier Bidirectional Relay Networks

Considering a bidirectional collaborative scheme, we study the problem of resource sharing between two transceiver pairs in a multi-carrier scenario. One pair, referred to as the primary pair, is considered to be the owner of the spectral resources, meaning that the rates of its users must be guaranteed to be greater than a predefined threshold. It is assumed that the other pair, called the secondary pair, owns the relay infrastructure. Considering no direct link between the transceivers in each pair, the primary network allows the secondary pair to use the spectral resources in order to establish a bidirectional communication between its transceivers. In exchange for this cooperation, the primary pair utilizes the relay infrastructure, thereby enabling a two-way communication between its transceivers. Assuming amplify-and-forward relaying scheme in each subchannel, the relays collectively build two network beamformers each of which enables communication between one pair of transceivers. Aiming to optimally calculate the parameters of the two networks, we study two different approaches. The first approach relies on maximizing the secondary network average sum-rate subject to two spectral power masks for the two networks, while providing a minimum sum-rate to the primary pair in a multi-relay scenario. In the second approach, we consider a constraint on the total power consumed in each network over all subchannels, while maximizing the sum-rate of the secondary transceivers. In this approach, we provide two iterative convex search solutions, one for a single-relay case and one for a multi-relay scenario.

Multi-Gateway Data Predistortion for Non-Linear Satellite Channels

Joint on-board amplification of multiple carriers reduces hardware and weight, enabling cost-efficient satellite architectures. However, on-board power amplification is inherently a non-linear operation and the distortion effects drastically increase when the high-power amplifier (HPA) is operated in multiple-carrier mode due to the generated inter-modulation products. In order to achieve the desired on-board power efficiency and to reduce the non-linear distortion, specific countermeasures need to be put in place. Emerging multi-gateway satellite systems where, in the most general case, each carrier is uplinked independently from a different gateway, would gain further flexibility by adopting a multicarrier architecture. While advanced predistortion techniques are already available for the single-gateway scenario, no solution has been proposed for the multi-gateway multicarrier scenario. In this work, we propose novel distributed predistortion techniques to improve spectral and power efficiency in multi-gateway non-linear satellite channels. Further, we analyze the multiple-carrier predistortion parameter estimation error with respect to noise sensitivity, proposing a robust version of the indirect learning method. Distributed processing becomes sensitive to synchronization amongst the actors, and we present an evaluation of the sensitivity of proposed techniques to imperfections.

Blind Equalization in OFDM Systems by Channel Shortening and Channel Diversity Exploitation

We propose two alternative approaches for blind channel shortening to design time domain finite impulse response equalizer (TDE) in multicarrier single input, multiple output systems. The first approach we utilize the constant envelop of the transmitted signal called constant modulus (CM). Second approach is based on decision directed algorithm, where we update time domain equalizer using decision directed cost function. These cost function have been widely studied for single carrier systems, implementing them in multicarrier scenario is of great importance. Simulations shows they outperform classical multicarrier equalization by restoration of redundancy (MERRY) and Carrier nulling algorithm (CNA) that utilizes redundancies inherent in carriers in terms of bit error rate. General Terms Channel equalization algorithms in wireless communication.

Simplified Random-Walk-Model-Based Kalman Filter for Slow to Moderate Fading Channel Estimation in OFDM Systems

This study deals with multi-path channel estimation for orthogonal frequency division multiplexing systems under slow to moderate fading conditions. Advanced algorithms exploit the channel time-domain correlation by using Kalman filters (KFs) based on an approximation of the time-varying channel. Recently, it was shown that under slow to moderate fading, near-optimal channel multi-path complex amplitude estimation can be obtained by using the integrated random walk (RW) model as the channel approximation. To reduce the complexity of the high-dimensional RW-KF for joint estimation of the multi-path complex amplitudes, we propose using a lower dimensional RW-KF that estimates the complex amplitude of each path separately. We demonstrate that this amounts to a simplification of the joint multi-path Kalman gain formulation through the Woodbury's identities. Hence, this new algorithm consists of a superposition of independent single-path single-carrier KFs, which were optimized in our previous studies. This observation allows us to adapt the optimization to the actual multi-path multi-carrier scenario, to provide analytic formulas for the mean-square error performance and the optimal tuning of the proposed estimator directly as a function of the physical parameters of the channel (Doppler frequency, signal-to-noise-ratio, power delay profile). These analytic formulae are given for the first-, second-, and third-order RW models used in the KF. The proposed per-path KF is shown to be as efficient as the exact KF (i.e., the joint multi-path KF), and outperforms the autoregressive-model-based KFs proposed in the literature.