Downlink Rate Research Articles

NASA's high-rate Ka-band downlink system for the NISAR mission

This paper provides a description and analyses of NASA's (National Aeronautics and Space Administration) high-rate Ka-band downlink system for the upcoming NISAR (NASA-ISRO Synthetic Aperture Radar) mission developed at the Jet Propulsion Laboratory (JPL). NISAR is a collaborative Earth-Science mission between NASA and the Indian Space Research Organisation (ISRO), which features an L-band SAR instrument and an S-band SAR instrument. The simultaneous dual-frequency radar system at peak rates will produce data at gigabit-per-second speeds, which drives the data-volume requirements. The key driving requirement for the Payload Communication Subsystem is to provide a minimum of 35 Terabits per day of radar science data to the ground. NASA's high-rate transmitter on the flight system is a software-defined radio based on the Universal Space Transponder platform, providing an offset quadrature phase shift key modulated waveform with Low-Density Parity Check encoding of the data transfer frames. Two transmitters used in a dual-polarization configuration with each transmitter providing two giga-symbols per second (Gsps) of coded data provides an aggregate rate of four Gsps. In this system, using the ISRO-provided 70-cm high-gain antenna, only 1 W of signal power is necessary on each polarization to overcome propagation losses and achieve a successful RF link. Several Near Earth Network (NEN) ground station sites (Alaska in the United States, Svalbard in Norway, and Punta Arenas in Chile) are baselined for the space-to-Earth communications link. Each ground station will also feature multiple upgrades to support NISAR's transmission starting with new Ka-band antennas, and new electronic units such as wideband downconverters and high-rate receivers. In addition, a baseband data processor called Data Acquisition Processor and Handling Network Environment (DAPHNE), newly developed by the NEN, provides data storage and connectivity to backhaul networks. With NISAR's unprecedented large volume of data (nearly 5 Petabytes over the mission duration), a cloud system enables science data processing cost reduction to the mission. The NASA downlink system described herein will be the first operational use of Gsps-class downlink rates on an Earth-Science mission.

Device-Aware Cell Association in Heterogeneous Cellular Networks: A Matching Game Approach

Heterogeneous cellular networks (HCNs) provide a promising paradigm for supporting different types of devices that have diverse quality of service (QoS) requirements, such as Internet of Things devices (IoTDs) and human-to-human devices (H2HDs). In this paper, a distributed cell association algorithm is developed to consider the dissimilar association requirements for IoTDs and H2HDs coexisting in HCNs. The device association process is formulated as a multi-objective optimization problem to minimize the uplink (UL) transmit power for IoTDs and maximize the downlink (DL) data rate for H2HDs while considering the various devices’ QoS requirements. To solve this problem, an approach dependent on matching theory is proposed to model the interactions between devices and base stations in the network. Using this approach, a distributed algorithm is developed to provide the device association solution. The proposed algorithm is then proved to converge to a stable matching. Simulation results validate the effectiveness of the proposed algorithm in improving the UL power performance for IoTDs as well as DL rate performance for H2HDs compared to other association algorithms.

Joint Antenna Selection and Power Allocation for an Energy-efficient Massive MIMO System

A joint antenna selection and power allocation scheme is proposed for a downlink massive multiple-input multiple-output system under perfect channel state information. Based on a tractable lower bound of the achievable downlink rate with linear zero-forcing precoding, a nonconvex energy efficiency optimization problem is formulated with nonlinear constraints. Since the optimization variables are highly interrelated, it is impractical to directly derive the closed-form solution to the original problem. To solve this problem, an effective iterative algorithm that maximizes energy efficiency is proposed according to the Lagrangian dual method, where the optimal number of transmit antennas and power allocation are solved iteratively until convergence. Numerical results demonstrate the effectiveness of the proposed algorithm.

Automated Self-Optimization in Heterogeneous Wireless Communications Networks

Traditional single-tiered wireless communications networks cannot scale to satisfy exponentially rising demand. Operators are increasing capacity by densifying their existing macro cell deployments with co-channel small cells. However, cross-tier interference and load balancing issues present new optimization challenges in channel sharing heterogeneous networks (HetNets). One-size-fits-all heuristics for allocating resources are highly suboptimal, but designing ad hoc controllers requires significant human expertise and manual fine-tuning. In this paper, a unified, flexible, and fully automated approach for end-to-end optimization in multi-layer HetNets is presented. A hill climbing algorithm is developed for reconfiguring cells in real time in order to track dynamic traffic patterns. Schedulers for allocating spectrum to user equipment are automatically synthesized using grammar-based genetic programming. The proposed methods for configuring the HetNet and scheduling in the time–frequency domain can address ad hoc objective functions. Thus, the operator can flexibly tune the tradeoff between peak rates and fairness. Far cell edge downlink rates are increased by up to 250% compared with non-adaptive baselines. Alternatively, peak rates are increased by up to 340%. The experiments illustrate the utility and future potential of natural computing techniques in software-defined wireless communications networks.

Resource allocation in OFDM-based wireless powered communication networks with SWIPT

This paper considers a multiuser orthogonal frequency division multiplexing (OFDM)-based wireless powered communication networks (WPCNs) with simultaneous wireless information and power transfer (SWIPT). In particular, both uplink and downlink communications are considered by dividing the time for transmission into downlink and uplink communication phases. In the downlink, SWIPT is adopted at the users with nonlinear energy harvesting model, and in the uplink, users use the harvested energy to perform uplink data transmission. We propose a suboptimal resource allocation scheme to maximize the weighted downlink and uplink sum rate by optimizing the time, subcarrier and power allocation. It is shown that the proposed scheme can well balance between the downlink rate and the uplink rate.

Improving Tradeoff Among Downlink Rates of Service Providers in a VWN by Using NOMA

In this letter, we improve the tradeoff among the downlink (DL) rates of service providers (SPs), provided by a mobile virtual network operator (MVNO) using non-orthogonal multiple access (NOMA) technique. In this respect, NOMA provides a suitable opportunity for the MVNO to share resources among SPs more efficiently. Regarding interaction among the DL rates provided for SPs we formulate our resource allocation problem as a multiobjective optimization problem (MOOP) while taking into account user fairness. By using the weighted Tchebycheff technique, we are able to solve the formulated MOOP by considering different tradeoffs among DL rates of different SPs. Our numerical results show that by using NOMA, each SP gains a higher rate compared with orthogonal frequency-division multiple access.

Minimum Rate Maximization for Wireless Powered Cloud Radio Access Networks

This paper studies the optimization of signal processing strategies for downlink and uplink of a cloud radio access network (C-RAN) that serves wireless powered users with non-linear energy harvesting (EH) circuits. On the downlink, a baseband processing unit (BBU) sends radio frequency signals to the users through a set of remote radio heads (RRHs), which is centrally managed by the BBU via finite-fronthaul links. Then, each user splits the received signal for information decoding and EH by utilizing the power splitting circuit. By using the harvested energy, each user communicates with the BBU via the RRHs on the uplink. In this paper, we tackle a problem of maximizing the minimum uplink rate of the users subject to the minimum downlink rate constraint as well as the per-node transmit power and fronthaul capacity constraints. To overcome the non-convexity of the problem, we propose an iterative algorithm based on a successive convex approximation method, which obtains a locally optimal solution. Numerical results confirm the effectiveness of the proposed techniques for C-RAN systems with battery-limited users.

On the Capacity and Straggler-Robustness of Distributed Secure Matrix Multiplication

Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typical of concern. In this paper, we first study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product AB of two finite field private matrices A and B from an information-theoretic perspective. In this problem, the user exploits the computational resources of N servers to compute the matrix product but simultaneously tries to conceal the private matrices from the servers. Our goal is twofold: (i) to maximize the downlink communication rate, and (ii) to minimize the effective number of server observations needed to determine AB, while preserving security, where we allow for up to l ≤ N servers to collude. To this end, we propose two schemes - an aligned secret sharing scheme (A3S) and a secure cross subspace alignment (SCSA) scheme. For A3S, we optimize the partitioning of matrices A and B in order to either optimize objective (i) or (ii) as a function of the system parameters (e.g., N and l). A proposed inductive approach gives us analytical, close-to-optimal solutions for both (i) and (ii). The SCSA, on the other hand, is shown to be (rate) capacity-optimal for the general J-sided distributed secure matrix multiplication problem Π j=1 J M j . We show this by developing a recursive information-theoretic upper bound (converse) on the downlink rate for the J-sided secure matrix multiplication problem. With respect to (i), both A3S and SCSA, significantly outperform the state-of-the-art in terms of (a) communication rate, (b) maximum tolerable number of colluding servers, and (c) computational complexity. Overall SCSA (A3S) is the preferred choice when the focus is on the downlink (uplink).

Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders.

Impacts of practical channel impairments on the downlink spectral efficiency of large-scale distributed antenna systems

Channel impairments are major limiting factors in the performance of large-scale antenna systems.In this paper, we analyze the impacts of practical channel impairments caused by pilot contamination, Doppler shift, and phase noise on the downlink spectral efficiency of large-scale distributed antenna systems (L-DASs) with maximum ratio transmission (MRT) and zero-forcing (ZF) beamforming, in which per user power normalization is considered.Using a joint channel model that allows study of the simultaneous impacts of these channel impairments, we derive accurate and tractable closed-form approximations for the ergodic achievable downlink rate, thereby enabling spectral efficiency analysis of L-DASs and an efficient evaluation of the impacts of the channel impairments. It is shown that channel impairments reduce the downlink spectral efficiency and have a significant impact on ZF beamforming. The asymptotic user rate limit is also determined, from which we analyze the asymptotic performance of L-DASs with channel impairments. The analytical results show that MRT and ZF beamforming achieve the same asymptotic performance limit even with channel impairments. It is also found that the use of a large-scale antenna array at the base station sides can weaken the impacts of channel impairments.

The study of new features for video traffic classification

Network traffic classification is important for the management of network resource and the support quality of multimedia services. To realize the fine-grained classification of typical Internet video traffic, this paper studies and analyses the characteristics of video flow change in transmission process and the statistic characteristics of its main protocol data. According to different service models from the network services and the users’ demand of video quality, we propose two new sets of features for video traffic classification, including: downlink rate probability distribution model and main protocol data statistics. The experimental results show that the two sets of features can improve the performance of classification compared to existing methods.

Bandwidth Partitioning and Downlink Analysis in Millimeter Wave Integrated Access and Backhaul for 5G

With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mm-wave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mm-wave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul. In this paper, we develop an analytical framework for IAB-enabled cellular network using which its downlink rate coverage probability is accurately characterized. Using this framework, we study the performance of three backhaul bandwidth (BW) partition strategies: 1) equal partition: when all SBSs obtain equal share of the backhaul BW; 2) instantaneous load-based partition: when the backhaul BW share of an SBS is proportional to its instantaneous load; and 3) average load-based partition: when the backhaul BW share of an SBS is proportional to its average load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs.

Downlink Performance of Superimposed Pilots in Massive MIMO Systems

In this paper, we investigate the downlink throughput performance of a massive multiple-input multiple-output system that employs superimposed pilots for channel estimation. The component of downlink (DL) interference that results from transmitting data alongside pilots in the uplink (UL) is shown to decrease at a rate proportional to the square root of the number of antennas at the BS when the least-squares channel estimate is employed in a matched-filter precoder. The normalized mean-squared error (NMSE) of the channel estimate is compared with the Bayesian Cramer-Rao lower bound that is derived for the system, and the former is also shown to diminish with increasing number of antennas at the base station. Furthermore, we show that staggered pilots are a particular case of superimposed pilots and offer the downlink throughput of superimposed pilots while retaining the UL spectral and energy efficiency of regular pilots. We also extend the framework for designing a hybrid system, consisting of users that transmit either regular or superimposed pilots, to minimize both the UL and DL interference. The improved NMSE and DL rates of the channel estimator based on superimposed pilots are demonstrated by means of simulations.

On the Impact of the Mutual Reactance on the Radiated Power and on the Achievable Rates

In this brief, we consider the mutual reactance of hypothetical isotropic radiators and the influence of the mutual reactance on antenna arrays with zero mutual resistance. We compute the mutual reactance of hypothetical isotropic radiators using passivity, which has not been considered in wireless communications literature. We show that when an antenna array with zero mutual resistance is excited by ideal current sources, the mutual reactance does not have any influence on the radiated power. However, practical radio frequency amplifiers are not ideal current sources, but can often be modeled as linear sources. Then the radiated power depends on the mutual reactance. Furthermore in receive mode, the mutual reactance introduces noise correlations between the antennas. Numerical results show the impact of neglecting the mutual reactance on the radiated power and on the ergodic up- and downlink rates in an independent and identically distributed channel for a base station using a uniform linear array consisting of hypothetical isotropic radiators spaced by $\lambda /2$ , or a uniform circular array consisting of 3 $\lambda /2$ -dipoles.

Training size optimization with reduced complexity in cell-free massive MIMO system

Training sequence is used in multiple antenna systems to estimate channel state information and mitigate channel distortion between transmitter and receiver. However, the training sequence or pilot must be limited to a certain size in order to reduce the impact of overhead loss due to limited channel coherence length in mobile users. In this paper, we proposed to use training size optimization in cell-free massive MIMO system. In addition, we proposed and compared the performance of different training size optimization algorithms, namely exhaustive search optimization, bisection optimization and min–max optimization, with each method has different level of calculation complexities. The results showed that in general, all of the 3 training length optimization methods improved the downlink rate compared to the conventional pilot length method. We also showed that the training optimization methods are more effective when the coherence length is small or the number of users is very large. In the case of large number of users or small coherence length, the exhaustive search has the best median downlink rate, followed closely by min–max optimum and finally the bisection method. Even though the exhaustive search optimization has the best downlink rate, we showed that the proposed reduce optimization complexity methods has significantly less calculation complexity. In addition, the median downlink rate performance of min–max optimization method is only slightly less than that of the exhaustive search method for various number of users and coherence length.

Rate selection based medium access control for full-duplex asymmetric transmission

The realization of full-duplex wireless communication is predictable. And asymmetric transmission is a practical and low-cost application scenario, where full-duplex access point (FD_AP) can communicate with two users simultaneously to receive and send packets. While, in an asymmetric transmission, the transmit power of uplink sender decides the uplink and downlink rates because of the inter-client interference, which accordingly restricts the throughput. Besides, the size of packets in uplink and downlink is generally unequal. Therefore, a WIFI network with a FD_AP and half-duplex users is studied in this paper, and a medium access control (MAC) protocol based on power control and rate selection (PCRS) is proposed. PCRS MAC employs a received signal strength based rate selection strategy to select different rates and power for uplink and downlink transmission. Then, FD_AP can establish efficient and reliable full-duplex asymmetric transmission. Simulation results show that PCRS can improve the throughput and the probability of successful asymmetric communication as compared to the distributed coordination function (DCF) and a simple full-duplex MAC protocol without PCRS. Besides, PCRS MAC also maintains a high level of fairness.

Optimal Beamforming and Time Allocation for Partially Wireless Powered Sensor Networks With Downlink SWIPT

Wireless powered sensor networks (WPSNs) have emerged as a key development towards the future self-sustainable Internet of Things (IoT) networks. To achieve a good balance between self-sustainability and reliability, partially WPSNs with a mixed power solution are desirable for practical applications. Specifically, most of the sensor nodes are wireless powered but the key sensor node adopts traditional wire/battery power for reliability. As a result, this paper mainly investigates optimal design for the partially WPSNs in which simultaneous wireless information and power transfer (SWIPT) is adopted in the downlink. Two scenarios with space division multiple access (SDMA) and time division multiple access (TDMA) in the uplink are considered. For both the SDMA-enabled and TDMA-enabled partially WPSNs, joint design of downlink beamforming, uplink beamforming and time allocation is investigated to maximize the uplink sum rate while guaranteeing the quality-of-service (i.e., satisfying the downlink rate constraint) at the key sensor node. After analyzing the feasibility of uplink sum rate maximization problems and the influence of the downlink rate constraint, semi-closed-form optimal solutions for both SDMA-enabled and TDMA-enabled WPSNs are proposed with guaranteed global optimality. Complexity analysis is also provided to justify the advantage of the proposed solutions in low complexity. The effectiveness and optimality of the proposed optimal solutions are finally demonstrated by simulations.

Optimal Base Station Deployment with Downlink Rate Coverage Probability Constraint

The uncertainty in user locations and channel conditions makes the deployment and performance analysis of wireless networks challenging. Stochastic geometry has emerged as a powerful tool for analyzing the performance of wireless networks, assuming certain stochastic models for the distribution of both users and base stations (BSs). In this letter, seeking further precision, we derive the downlink rate coverage probability of a wireless network with deterministically known BS locations . Then, we use this result to optimize the placement of BSs while keeping the downlink rate coverage probability above a certain threshold.

Resource allocation of wireless backhaul in heterogeneous network based on the large-scale MIMO

The small cell network and large-scale MIMO system are considered as two important technologies of 5G communication system. In this paper, one two-layer heterogeneous network composed of a dense small cell layer covering the large cell layer is proposed. To enable the base station in large cell to effective estimate the channel, the network adopts R-TDD protocol. The base station in large cell uses the large-scale MIMO technology, which not only serves the user and equipment units in large cell but provides AP in small cell with wireless backhaul. These APs in small cell serve their associated users only. The base station adopts RZF and projection technology at the time of downlink transmission to avoid interference with the uplink transmission, and adopts LMMSE detection technology at the time of uplink transmission to eliminate interference. As a result, we have obtained the deterministic expression that traverses the uplink and downlink rates, and then optimize bandwidth and time slot allocation by these. In addition, the simulation results show that these deterministic rate expressions are accurate, and the method proposed for resource allocation may effectively improve system performance.

DFT-Based Hybrid Beamforming Multiuser Systems: Rate Analysis and Beam Selection

This paper considers the discrete Fourier transform (DFT) based hybrid beamforming multiuser system and studies the use of analog beam selection schemes. We first analyze the uplink ergodic achievable rates of the zero-forcing (ZF) receiver and the maximum-ratio combining (MRC) receiver under Ricean fading conditions. We then examine the downlink ergodic achievable rates for the ZF and maximum-ratio transmitting (MRT) precoders. The long-term and short-term normalization methods are introduced, which utilize long-term and instantaneous channel state information (CSI) to implement the downlink power normalization, respectively. Also, approximations and asymptotic expressions of both the uplink and downlink rates are obtained, which facilitate the analog beam selection solutions to maximize the achievable rates. An exhaustive search provides the optimal results but to reduce the time-consumption, we resort to the derived rate limits and propose the second selection scheme based on the projected power of the line-of-sight (LoS) paths. We then combine the advantages of the two schemes and propose a two-step scheme that achieves near optimal performances with much less time-consumption than exhaustive search. Numerical results confirm the analytical results of the ergodic achievable rate and reveal the effectiveness of the proposed two-step method.