Knowledge Of Channel Statistics Research Articles

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Power and Rate Adaptation for URLLC With Statistical Channel Knowledge and HARQ

Power and Rate Adaptation for URLLC With Statistical Channel Knowledge and HARQ

Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

A dual phase genetic algorithm for improved initial access in 5G millimeter wave communication

Initial access (IA) in 5G millimeter wave (mmWave) communication is the problem of establishing a directional link between the base station (BS) and the user equipment (UE). For a multiple-input multiple-output (MIMO) system, where both the BS and UE have many antennas, finding the optimal beams can be prohibitively expensive in terms of delay and computation. In this work, we propose a meta-heuristic approach which is a modified dual-phase genetic algorithm. Since it is a meta-heuristic approach, it is generic and hence does not require extensive modifications to apply to different scenarios, it also does not require context information such as prior knowledge of channel state or statistics of user behavior. The proposed method is using iterative search for the optimal beams, but switch to a different fine-grained search phase on later iterations in order to quickly converge to the local optimum. The effect of this approach is analyzed in terms of capacity achieved vs number of transmit and receive antennas at BS and UE, codebook size, outage probability, total transmitted power, and other parameters specific to this particular dual-phase method. The proposed work has shown improved performance when compared to the existing similar work done in Souto et al. (2019) in terms of capacity achieved (2.12%), reduced power consumption (8.57%), and reduced IA delay (35% to 50%).

Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge.

Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead.

Cooperative Mode Switching-Based Cognitive NOMA With Transmit Antenna and User Selection

The paper presents performance analysis of a multiuser Cooperative Non-Orthogonal Multiple Access (CNOMA) network that is underlay to a primary network. A selected secondary Full-Duplex (FD) Near User (NU) cooperates with a selected secondary Far User (FU) by apportioning the Interference Temperature Limit (ITL) with an ITL Apportioning Parameter (ITLAP). This Cooperative Mode (CM) is exercised only when NU meets its own performance criteria, else the network switches to a Non-Cooperative Mode (NCM). The network is therefore made adaptive in such a way that performance of the NU is same as in a network without the FU. Performance of the network is further improved using Maximum Transmit Power-based Transmit Antenna Selection (MTP-TAS), Best NU-Best FU (BNBF) selection, and optimal power allocation, thereby also motivating NOMA even in power-constrained underlay environments where transmit powers are not always high. The selection mechanisms are designed in a way that additional overheads for Channel State Information (CSI) estimation and feedback are not imposed. Analytical expressions for outage probability are presented for both end users, assuming only statistical knowledge of channel gains under the assumption of imperfect Successive Interference Cancellation (SIC) at the NU. Outage minimizing optimal values of the NOMA Power Allocation Parameter (NPAP) and ITLAP are derived that minimize FU outage. Simulations validate the derived expressions, and demonstrate superiority of the proposed scheme over FD NU-based conventional NOMA as well as conventional OMA signalling.

Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks—Part I

Analyzing massive amounts of data using complex machine learning models requires significant computational resources. The conventional approach to such problems involves centralizing training data and inference processes in the cloud, i.e., in data centers. However, with the proliferation of mobile devices and increasing application of the Internet-of-Things (IoT) paradigm, very large amounts of data are collected at the edges of wireless networks, and due to privacy constraints and limited communication resources, it is undesirable or impractical to upload this data from mobile devices to the cloud for centralized learning. This problem can be solved by distributed learning at the network edge, by which edge devices collaboratively train a shared learning model using real-time mobile data. The avoidance of raw-data uploading not only helps to preserve privacy but may also alleviate network-traffic congestion and minimize latency. With that said, distributed training still requires a substantial amount of information exchange between devices and edge servers over wireless links. In the process, wireless impairments such as noise, interference, and imperfect knowledge of channel states can significantly slow down distributed learning (e.g., convergence speed) and degrades its performance (e.g., learning accuracy). This makes it crucial to optimize wireless network performance so as to support the efficient deployment of distributed learning algorithms. On the other hand, distributed learning algorithms provide a powerful tool-set for solving complex problems in wireless communication and networking. One important framework, called federated learning (FL), enables users to collaboratively learn a shared model while helping to preserve local data privacy. The application of FL can endow edge devices with capabilities of user behavior prediction, user identification, and wireless environment analysis. As another example, distributed reinforcement learning is capable of leveraging distributed computation power and data to solve complex optimization and control problems that arise in various use cases, such as network control, user clustering, resource management, and interference alignment. To cover this paradigm of distributed learning over wireless networks, this two-part Special Issue features papers dealing with two main research challenges: a) optimization of wireless network performance for efficient implementation of distributed learning in wireless networks, and b) distributed learning for solving communication problems and optimizing network performance.

Content Delivery Over Broadcast Erasure Channels With Distributed Random Cache

We study the content delivery problem between a transmitter and two receivers through erasure links, when each receiver has access to some random side-information about the files requested by the other user. The random side-information is cached at the receiver via the decentralized content placement. The distributed nature of receiving terminals may also make the erasure state of two links and indexes of the cached bits not perfectly known at the transmitter. We thus investigate the capacity gain due to various levels of availability of channel state and cache index information at the transmitter. More precisely, we cover a wide range of settings from global delayed channel state knowledge and a non-blind transmitter (i.e. one that knows the exact cache index information at each receiver) all the way to no channel state information and a blind transmitter (i.e. one that only statistically knows cache index information at the receivers). We derive new inner and outer bounds for the problem under various settings and provide the conditions under which the two match and the capacity region is characterized. Surprisingly, for some interesting cases the capacity regions are the same even with single-user channel state or single-user cache index information at the transmitter.

Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions

We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for any coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multi-path fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters.

Energy-Efficient Time-Domain Equilibrium Scheduling and Optimization Scheme for Energy Harvesting-Powered D2D Communication

Energy Harvesting- (EH-) powered Device-to-Device (D2D) Communication underlaying Cellular Network (EH-DCCN) has been deemed as one of the basic building blocks of Internet of Things due to its green energy efficiency and adjacent communication. But available energy will be one of the biggest obstacles when implementing EH-DCCN due to the immaturity of EH technology and the volatility of environmental energy resources. To improve energy utilization, this study investigates an efficient scheduling and power allocation scheme about transmission load equilibrium in the time domain. Accordingly, a short-term Sum Energy Efficiency (stSEE) maximization problem for EH-powered D2D communication is modelled, while ensuring a fundamental transmission rate requirement of cellular users. Consequently, the optimization problem is a nonconvex mixed integer nonlinear programming problem. Thus, we propose a two-layer convex approximation iteration algorithm which can obtain a feasible quasioptimal solution for the stSEE problem. Simultaneously, a two-step heuristic algorithm in a slot-by-slot fashion is also developed to acquire a suboptimal solution without requiring statistical knowledge of channel and energy arrival processes. Simulated analysis indicates that the short-term scheduling strategy can obtain better performances in terms of energy efficiency and transmission rate than conventional real-time scheduling scheme. Besides, the maximum scheduled number of EH-D2D pairs underlaying one cellular user under different EH efficiency is analysed, which can give us a theoretical reference about the deployment of future EH-DCCN.

On proportional fairness of uplink spectral efficiency in cell‐free massive MIMO systems

SummaryThis paper is concerned with the proportional fairness (PF) of the spectral efficiency (SE) maximization of uplinks in a cell‐free (CF) massive multiple‐input multiple‐output (MIMO) system in which a large number of single‐antenna access points (APs) connected to a central processing unit (CPU) serve many single‐antenna users. To detect the user signals, the APs use matched filters based on the local channel state information while the CPU deploys receiver filters based on knowledge of channel statistics. We devise the maximization problem of the SE PF, which maximizes the sum of the logarithm of the achievable user rates, as a jointly nonconvex optimization problem of receiver filter coefficients and user power allocation subject to user power constraints. To handle the challenges associated with the nonconvexity of the formulated design problem, we develop an iterative algorithm by alternatively finding optimal filter coefficients at the CPU and transmit powers at the users. While the filter coefficient design is formulated as a generalized eigenvalue problem, the power allocation problem is addressed by a gradient projection (GP) approach. Simulation results show that the SE PF maximization not only offers approximately the achievable sum rates as compared to the sum‐rate maximization but also provides an improved trade‐off between the user rate fairness and the achievable sum rate.

Statistical Channel Knowledge-Based Distributed Power Allocation for Analog Network Coding in High SNR

A novel distributed power allocation scheme for analog network coding (ANC) in high signal-to-noise ratio (SNR) is proposed by using statistical channel knowledge at the transmitters only. The scheme considers nonorthogonal amplified-and- forward (NAF) protocol. In the derivation, a general Rayleigh fading multiple hop network is accurately approximated by a simpler two hop network, and the closed-formed expression for the outage probability of the simplified two hop network is obtained and optimized with respect to the power allocation. Numerical results show that the derived power allocation scheme has significant performance gain over the equal power scheme. In some cases, it approaches the optimal scheme.

A Comparative Study of Wireless Sensor Network Using Cooperative Protocol

In this paper, a novel dispensed energy-efficient clustering strategy for heterogeneous wireless sensor network with fresh cooperative protocol is suggested and assessed. The cluster-heads are chosen by a likelihood issued from the ratio between cooperative energy picks and both Channel State Knowledge (CSK) and Residual Energy Knowledge (REK) consideration of node and the average energy of the network. Matching energy dissipation of Bluetooth low energy, ZigBee and ANT protocols are supplied, in which a particular scope low power wireless sensor node periodically transmits a data packet to base station. Various tests are carried out to test the performance of the CC 2520, CC 2540 and CC 2570 modules. The average network lifetime, the throughput and the packet size calculations are described. It is supposed that the differentiation offered in this paper would interest implementation engineers in choosing a proper protocol.

Rethinking Secure Precoding via Interference Exploitation: A Smart Eavesdropper Perspective

Based on the concept of constructive interference (CI), multiuser interference (MUI) has recently been shown to be beneficial for communication secrecy. A few CI-based secure precoding algorithms have been proposed that use both the channel state information (CSI) and knowledge of the instantaneous transmit symbols. In this article, we examine the CI-based secure precoding problem with a focus on smart eavesdroppers that exploit statistical information gleaned from the precoded data for symbol detection. Moreover, the impact of correlation between the main and eavesdropper channels is taken into account. We first modify an existing CI-based precoding scheme to better utilize the destructive impact of the interference. Then, we point out the drawback of both the existing and the new modified CI-based precoders when faced with a smart eavesdropper. To address this deficiency, we provide a general principle for precoder design and then give two specific design examples. Finally, the scenario where the eavesdropper's CSI is unavailable is studied. Numerical results show that although our modified CI-based precoder can achieve a better energy-secrecy trade-off than the existing approach, both have a limited secrecy benefit. On the contrary, the precoders developed using the new CI-design principle can achieve a much improved tradeoff and significantly degrade the eavesdropper's performance.

Statistically-Aided Codebook-Based Hybrid Precoding for Millimeter Wave Channels

In this paper, we propose practical yet effective statistically-aided codebook-based hybrid precoding schemes for massive multiple-input multiple-output systems in millimeter wave bands. Particularly, we develop novel low-overhead hybrid precoding algorithms for selecting the baseband digital and radio frequency analog precoders from statistically skewed DFT-based codebooks. The proposed algorithms aim at maximizing the spectral efficiency based on minimizing the chordal distance between the optimal unconstrained precoder and the hybrid beamformer and maximizing the signal to the interference noise ratio for the single-user and multi-user cases, respectively. We investigate the performance of the proposed algorithms by considering the mutual information of the analog beamforming procedure (the common stage among the proposed algorithms) as a performance evaluation metric. We derive lower and upper bounds on the mutual information of the channel given the proposed algorithms. Moreover, we show that the performance gap between the lower and upper bounds depends heavily on how many DFT columns are aligned to the largest eigenvectors of the transmit antenna array response of the millimeter wave channel or equivalently the transmit channel covariance matrix when only statistical channel knowledge is available at the transmitter. Then, we show that the proposed algorithms are asymptotically optimal as the number of transmit antennas M goes to infinity and the millimeter wave channel has a limited number of paths P, i.e., P <; M. Further, we verify the performance of the proposed algorithms numerically where results illustrate that the spectral efficiency of the proposed algorithms can approach that of the optimal precoder in certain scenarios. Furthermore, these results show that the proposed hybrid precoding schemes have superior spectral efficiency performance while requiring lower (or at most comparable) channel feedback overhead in comparison with the prior art.

Centralized and Decentralized Cache-Aided Interference Management in Heterogeneous Parallel Channels

We consider the problem of cache-aided interference management in a network consisting of $K_{\mathrm {T}}$ single-antenna transmitters and $K_{\mathrm {R}}$ single-antenna receivers, where each node is equipped with a cache memory. Transmitters communicate with receivers over two heterogenous parallel subchannels: the P-subchannel for which transmitters have perfect instantaneous knowledge of the channel state, and the N-subchannel for which the transmitters have no knowledge of the instantaneous channel state. Under the assumptions of uncoded placement and separable one-shot linear delivery over the two subchannels, we characterize the optimal degrees-of-freedom (DoF) to within a constant multiplicative factor of 2. We extend the result to a decentralized setting in which no coordination is required for content placement at the receivers. In this case, we characterize the optimal one-shot linear DoF to within a factor of 3.

Adaptive Bootstrap Design for Hybrid Terrestrial Broadcast and Mobile Communication Networks

The bootstrap in ATSC 3.0 is expected to act as a universal wake-up signal for various wireless systems in addition to broadcast network. However, the signaling decoding performance of the standardized bootstrap degrades significantly in channels of fast time-variation and strong multipath. Furthermore, part of available bandwidth is reserved to ensure the compatibility with mobile communication network (MCN), which puts a limitation on the performance improvement. In this paper, to make the best of the available bandwidth, we introduce the bandwidth-concerned version information to enable an adaptive bandwidth configuration for the proposed bootstrap. Moreover, a 2-D signaling scheme is used to increase signaling capacity by selecting different gold sequences in frequency domain (FD) and simultaneously applying cyclic shift in time domain (TD). At the receiver side, we first provide improved estimators of symbol timing offset (STO) and fine frequency offset (FFO) for the bootstrap with special TD structure. Meanwhile, a learning-based binary classifier taking the output of STO estimator as training data is provided to generate an SNR-independent threshold for spectrum sensing without requiring the knowledge of channel conditions nor noise estimator. Afterwards, an inverse fast Fourier transform (IFFT)-based algorithm is used to decode FD signaling in the presence of unknown TD signaling, which allows 3-bit higher signaling capacity. Numerical analysis and simulation results demonstrate that the proposed adaptive bootstrap design as well as corresponding receiver algorithms significantly outperform the standardized one in terms of synchronization, detection and signaling decoding.

Performance Analysis of Device-to-Device Aided Multicasting in General Network Topologies

We consider a Device-to-Device (D2D) aided multicast channel, where a base station (BS) wishes to convey a common message to many receivers and these receivers cooperate with each other. We analyze the performance of a two-phase cooperative multicasting scheme requiring only statistical channel knowledge at the BS. Our analysis reveals that, as the number of receivers K grows, the two-phase scheme guarantees an average multicast rate of $\frac {1 }{ 2} \log _{2}(1 + \beta \ln \text {K})$ with high probability for any $\beta where $\beta ^\star $ depends on the network topology. This scheme undergoes a phase transition at threshold $\beta ^\star \ln \text {K}$ where transmissions are successful/unsuccessful with high probability when the Signal to Noise Ratio (SNR) is above/below this threshold. We also analyze the multicast outage rate when a target joint decoding probability is fixed. Finally, we propose two enhanced schemes by optimally allocating the time resource between two phases and combining received signals from two phases. 1 1 A part of the results presented in the current manuscript has been presented at the IEEE International Symposium on Information Theory 2018 [1] . The results are published in Chapter 3 of the P.h.D. Thesis of the first author [2] .

A Simple Two-Level Channel Estimation Scheme for OFDM System

Due to its robustness to multipath delay spread and impulse noise, and also elevated spectral efficiency like characteristics, OFDM technology has been commonly adopted in contemporary wireless communication systems like LTE. The heart of an OFDM system is Channel estimator and the efficiency of channel estimation (CE) has a direct impact on the bit error rate performance of the system also the CE performance itself depends on effective estimation of fading channel hence the channel estimation problem has to be addressed. The popular LS based CE method is accepted for its simplicity but is found to be vulnerable to noise whereas the simple MMSE based CE that provides fine-tuned estimation still suffers from complexity as it depends on the knowledge of channel statistics (KCS). Hence a two level channel estimation technique that is a combination of kalman filter and thresholding scheme which denoises the LS initial estimates is proposed. The kalman filter tracks the estimates with initial data as LS estimates and threshold helps in selecting the significant data thus denoising the estimates and also filters the redundancy in estimated data. On comparing the existed CE techniques with that of the proposed scheme, the MSE performance of proposed scheme is found to be optimal and simple to that of the LMMSE technique.

QoS-Guaranteed Adaptive Bandwidth Allocation for Mobile Multiuser Scalable Video Streaming

Bandwidth allocation plays an important role to guarantee the quality of service (QoS) in streaming real-time scalable video to multiuser over wireless time-varying channels. In this letter, an adaptive bandwidth allocation (ABA) scheme is presented to maximize the bandwidth utilization while provisioning each individual user’s QoS. Cooperated with traffic rate adaptation, it jointly addresses the spatial diversity among multiuser and the temporal fluctuation of the individual channel condition. A continuous-time Markov decision processes model is introduced to formulate the QoS-guaranteed ABA problem as constrained stochastic optimization. Based on performance sensitivity analysis, a policy iteration algorithm is developed to find the optimal ABA policy online without any prior knowledge of channel statistics. The adaptability to unknown environments and high computation efficiency make it feasible to implement in running-time. Simulations results demonstrate the effectiveness of the proposed method.