Channel Knowledge Research Articles

Training‐based frequency synchronisation and highly frequency selective channel estimation for OFDM/OQAM systems

This work addresses frequency synchronisation and channel estimation for highly frequency selective orthogonal frequency division multiplexed systems with offset quadrature amplitude modulation. A two-step approach is proposed, wherein a coarse carrier frequency offset (CFO) estimator, which does not require channel knowledge, is designed in the first step by exploiting the correlation between the received and training signals. A time-domain model is derived for the joint estimation of fine CFO (FCFO) and highly frequency selective channel in the second step. In contrast to the frequency domain model in the literature, the derived time-domain model does not require the channel to be frequency flat. Based on the time-domain model, the weighted least squares and minimum mean square error estimators are investigated to jointly estimate the FCFO and frequency selective channel. The Cramer-Rao lower bounds (CRLBs) are derived for the proposed estimators, and are used to evaluate the performance of the joint FCFO and channel estimation in the presence of inter-symbol-interference due to the overlapping of adjacent time-domain orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) symbols. Simulation results demonstrate achievability of the CRLB bounds and performance gain of the proposed estimators over the state-of-the-art existing estimators.

A Noncoherent Massive MIMO System Employing Beamspace Techniques

Noncoherent detection schemes are an appealing and low-complexity alternative in multi-user massive MIMO uplink systems compared to classical coherent detection algorithms, since no actual channel knowledge is required at the receiver. For noncoherent multi-user detection to function, the induced power at the base station is utilized to separate the different users. However, spatial separation is impossible when the users are located in the far-field of the receiving antenna array. Consequently, noncoherent detection fails in such scenarios. To this end, beamspace techniques can be applied, focusing the energy of the incident wave to a smaller subset of the receive antennas and enabling again the noncoherent detection scheme. This paper analyzes the beamspace capabilities of a dielectric lens and an analog beamforming network applied at the receiver. Furthermore, a sub-array architecture is proposed, relaxing the design requirements for practical implementation. It is shown that noncoherent detection in combination with beamspace techniques performs comparably to channel-estimation-based detection. In addition, the sub-array architecture revealed a significant performance enhancement accompanied by a reduced user separability.

Multi-Cell Interference Exploitation: Enhancing the Power Efficiency in Cell Coordination

In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting from full base station (BS) coordination, we first propose a fully-coordinated scheme to exploit beneficial effects of both inter-cell and intra-cell interference, based on sharing both channel state information (CSI) and data among the BSs. To reduce the coordination overhead, we then propose a partially-coordinated scheme where only intra-cell interference is designed to be constructive while inter-cell is jointly suppressed by the coordinated BSs. Accordingly, the coordination only involves CSI exchange and the need for sharing data is eliminated. To further reduce the coordination overhead, a third scheme is proposed, which only requires the knowledge of statistical inter-cell channels, at the cost of a slight increase on the transmission power. For all the proposed schemes, imperfect CSI is considered. We minimize the total transmission power in terms of probabilistic and deterministic optimizations. Explicitly, the former statistically satisfies the users’ signal-to-interference-plus-noise ratio (SINR) while the latter guarantees the SINR requirements in the worst case CSI uncertainties. Simulation verifies that our schemes consume much lower power compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell coordination.

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] .

Very-High-Frequency Single-Input–Multiple-Output Acoustic Communication in Shallow Water

Very-high-frequency (VHF) underwater acoustic communication is a relatively unexplored terrain, but its importance is expected to increase with the growing demands for larger bandwidths and higher data rates. Knowledge of VHF propagation channels is a prerequisite to achieve high data throughput. In this paper, we present time-variant angle-resolved impulse responses for a set of 250-kHz shallow-water channels. The measurements were performed at high resolution using 64 hydrophones arranged in a line array. The measured channels are characterized by delay spreads of several milliseconds, Doppler spreads of tens of hertz, and angular spreads up to 30 °. The angular spread is not separable from the delay-Doppler spread, and the impulse response depends heavily on the direction of arrival. Different beamforming and multichannel equalizer strategies are compared for a single-input-multiple-output configuration. A method combining subarray beamforming and multichannel equalization delivers in both noise- and interference-limited scenarios, transferring data at 78.125 kb/s over ranges up to 770 m.

A general framework for joint estimation-detection of channel, nonlinearity parameters and symbols for OFDM in IoT-based 5G networks

Despite being a strong candidate also for future cellular networks, OFDM’s main drawback is the high peak-to-average power ratio. This requires transmitters to deploy high dynamic range power amplifiers which are difficult to manufacture and thereby expensive. It is particularly problematic in future IoT-based 5G networks, in which a lot of presumably low-cost low-power devices transmit data to a high-quality receiver. In order to make such transmitters as simple and cheap as possible, we consider receiver-side nonlinearity compensation and symbol detection. In particular, we study the problem of joint maximum-likelihood estimation-detection of channel and nonlinearity parameters and symbols using frequency-domain comb-type pilots in multi-path fading OFDM systems, and propose an iterative optimization algorithm to solve it. We also calculate the Cramér-Rao lower bound for a general type of memoryless nonlinearity and show that the proposed algorithm attains it, for high signal-to-noise ratios. We then show by numerical evaluations that the algorithm converges fast and the difference of its performance with the one of the genie-aided scenario, with perfect a priori knowledge of nonlinearity parameters and channel, is negligible.

BUILDING REGIONAL INNOVATION CAPACITY: LINKING KNOWLEDGE-INTENSIVE INNOVATIVE ENTREPRENEURSHIP AND INNOVATION GOVERNANCE

This article examines the processes of building innovation capacity, within a regional innovation system. We analyse a case study of technological development in a region, leading us to propose a conceptual model to explain how and why the development of a common resource pool of scientific and technological knowledge in turn leads to regional innovation capacity. The model visualises our proposition that a process of governance enables actors to exploit a set of regional resources (incentives, networks, and global relations), whereby collectively creating industrial opportunities. We thereby use the model to predict that the success and directionality of specific technology in the region is dependent on establishing an organisational structure for exploiting said resources collectively. This contributes to understanding the governance of innovation systems because our proposed organisational structure, once established, will protect and channel knowledge and resources to the heterogeneous participating actors (regional government, universities, and firms).

Binary-Tree Encoding for Uniform Binary Sources in Index Modulation Systems

The problem of designing bit-to-pattern mappings and power allocation schemes for orthogonal frequency-division multiplexing (OFDM) systems that employ subcarrier index modulation (IM) is considered. We assume the binary source conveys a stream of independent, uniformly distributed bits to the pattern mapper, which introduces a constraint on the pattern transmission probability distribution that can be quantified using a binary tree formalism. Under this constraint, we undertake the task of maximizing the achievable rate subject to the availability of channel knowledge at the transmitter. The optimization variables are the pattern probability distribution (i.e., the bit-to-pattern mapping) and the transmit powers allocated to active subcarriers. To solve the problem, we first consider the relaxed problem where pattern probabilities are allowed to take any values in the interval [0,1] subject to a sum probability constraint. We develop (approximately) optimal solutions to the relaxed problem by using new bounds and asymptotic results, and then use a novel heuristic algorithm to project the relaxed solution onto a point in the feasible set of the constrained problem. Numerical analysis shows that this approach is capable of achieving the maximum mutual information for the relaxed problem in low and high-SNR regimes and offers noticeable benefits in terms of achievable rate relative to a conventional OFDM-IM benchmark.

Robust Beamforming and Time Allocation for Full-Duplex Wireless-Powered Communication Networks

We propose a joint optimization of beamforming and time allocation for full-duplex (FD) wireless-powered communication networks (WPCNs). In FD-WPCNs, an FD-enabled hybrid access point (HAP) having multiple antennas broadcasts radio frequency (RF) energy to users, and concurrently each user transmits its own information to the HAP. Due to a lack of fixed power supplies at the users, it is difficult for the HAP to have accurate channel state information (CSI) of all links. Therefore, in this letter, a robust algorithm is proposed to maximize the weighted sum rate of the users against the uncertainty of the CSI using the relationship between the weighted sum rate and the weighted sum of mean-square error. Numerical results demonstrate that the proposed algorithm provides robustness against imperfect channel knowledge and benefits of utilizing multiple antennas in the FD-WPCNs.

Joint Iterative Channel Estimation and Frequency-Domain Turbo Equalization for Single-Carrier Spatial Modulation

Single-carrier frequency-domain turbo equalization (SC-FDTE) has gained widespread adoption in the emerging broadband spatial modulation (SM) systems operating in frequency-selective channels, where the channel model considered is a quasi-static Rayleigh fading channel. In this paper, a new class of robust FDTE designs based on the minimum mean-square error (MMSE) criterion is conceived for broadband single-carrier SM (SC-SM) systems relying on realistic imperfect channel knowledge. First, a robust time-domain soft-decision feedback (TDSDF)-aided FDTE is proposed to cope with channel estimation errors at the receiver. Furthermore, its robust frequency-domain soft-decision feedback (FDSDF)-aided counterpart is derived to offer a low-complexity approximate solution. Finally, by exploiting the carefully selected reliable soft-decision output of the channel decoder as pilots, we refine the resultant decision-directed channel estimation. As a benefit, the performance of the two robust FDTEs can be further improved. Both our simulation results and our extrinsic information transfer (EXIT) chart analysis demonstrate that the proposed robust FDTEs achieve significant performance improvements over the conventional FDTEs.

Generalized Degrees of Freedom of the Symmetric Cache-Aided MISO Broadcast Channel With Partial CSIT

We consider the cache-aided MISO broadcast channel (BC) in which a multi-antenna transmitter serves $K$ single-antenna receivers, each equipped with a cache memory. The transmitter has access to partial knowledge of the channel state information. For a symmetric setting, in terms of channel strength levels, partial channel knowledge levels and cache sizes, we characterize the generalized degrees of freedom (GDoF) up to a constant multiplicative factor. The achievability scheme exploits the interplay between spatial multiplexing gains and coded-multicasting gain. On the other hand, a cut-set-based argument in conjunction with a GDoF outer bound for a parallel MISO BC under channel uncertainty is used for the converse. We further show that the characterized order-optimal GDoF is also attained in a decentralized setting, where no coordination is required for content placement in the caches.

Non-iterative blind linearization algorithm for DML-based multi-IF-over-fiber mobile fronthaul systems.

We propose and experimentally demonstrate a novel digital post-linearization algorithm for the mobile fronthaul system employing bandwidth-efficient multiple intermediate frequency-over-fiber technology and a low-cost directly-modulated laser (DML). The proposed linearization algorithm is distinguished for blind adaption, the absence of training signal, or prior knowledge of the transmission channel. Particularly, it provides a closed-form solution for the estimation of optimal linearizer coefficients, leading to a non-iterative mode without convergence restrictions. In our experiments, one and nine 200MHz 64-QAM OFDM IF signals (or channels) are transmitted over a 10km single-mode fiber link using a DML. In both cases (one and nine IF channels), the linearization performance of the proposed algorithm is experimentally validated in terms of the reduction (>8 dB) of the adjacent channel leakage ratio, decrease (>10 dB) of error vector magnitude (EVM), and an increase (>9 dB) of EVM-compliant input RF power range.

On the Degrees-of-Freedom of Two-Unicast Wireless Networks With Delayed CSIT

We characterize the degrees-of-freedom (DoF) region of a class of two-unicast wireless networks under the assumption of delayed channel state information at the transmitters. We consider a layered topology with arbitrary connectivity, and we introduce new outer-bounds on the DoF region of such networks through the graph-theoretic notion of bottleneck nodes. Such nodes act as informational bottlenecks only under the assumption of delayed channel state information. We also present new transmission schemes that achieve the outer-bounds. We show that unlike the instantaneous channel state information model, the sum DoF of two-unicast networks with delayed channel knowledge can take an infinite set of values. In this paper, we compare our results to the best previously known outer-bounds, and we show that the gap can be arbitrary large in favor.

Differential pulse‐amplitude modulation signalling for free‐space optical communications

To improve the bandwidth efficiency of free-space optical systems and at the same time to reduce the impact of the background noise, a differential M -ary pulse-amplitude modulation ( M -PAM) signalling scheme that uses two laser transmitters is proposed. The authors first consider the condition that the receiver perfectly knows the instantaneous channel coefficient and compare the performance of the proposed differential PAM with the conventional PAM signalling and show the improved performance when the background noise level is relatively high. Second , the practical situation where the receiver has to estimate the channel for signal detection is considered. The authors propose an estimation scheme based on the characteristics of the differential PAM signalling while requiring no pilot symbol transmission. The proposed data-aided channel estimation is performed on a sequence of received PAM symbols. The authors also show that for a sufficiently large observation window, the proposed estimation method allows achieving a performance close to the perfect channel knowledge.

Exploiting Gaussian Mixture Model Clustering for Full-Duplex Transceiver Design

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator.

LI Cancellation and Power Allocation for Multipair FD Relay Systems With Massive Antenna Arrays

Massive antenna arrays are capable of canceling out the loop interference (LI) at the relay station in multipair full-duplex (FD) relay networks even without LI channel knowledge if the number of antennas is allowed to grow without a bound. For large but finite number of antennas, however, channel estimation-based LI cancelation is required. In this letter, we propose a pilot protocol for LI channel estimation by exploiting the channel coherence time difference between static and moving transceivers in a multipair FD relay system. To maximize the end-to-end achievable rate, we also design a novel power allocation scheme to adjust the transmit power of each link at the relay. The analytical and numerical results show that the proposed novel pilot protocol and power allocation scheme jointly improve spectral and energy efficiency significantly with realistic coherence time differences.

Spectral Efficiency of Multipair Massive MIMO Two-Way Relaying With Imperfect CSI

We consider a two-way half-duplex relaying system where multiple pairs of single-antenna users exchange information assisted by a multiple-antenna relay. Taking into account the practical constraint of imperfect channel knowledge, we study the achievable sum spectral efficiency (SE) of the amplify-and-forward protocol, assuming that the relay employs maximum ratio processing. We derive a closed-form expression for the sum SE for arbitrary system parameters and a large-scale approximation for the sum SE when the number of relay antennas $M$ becomes sufficiently large. In addition, we study how the transmit power reduces with $M$ to maintain a desired SE. Our results show that by using a large number of relay antennas, the transmit powers of the user, relay, and pilot symbol can be scaled down proportionally to $1/M^\alpha$ , $1/M^\beta$ , and $1/M^\gamma$ for certain combinations of $\alpha$ , $\beta$ , and $\gamma$ , respectively. This elegant power scaling law reveals a fundamental tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, capitalizing on the new expressions for the sum SE, novel power allocation schemes are designed to further improve the sum SE.

Optimal tracking of doubly-selective radio channels for OFDM based modern wireless systems

In wireless communications, knowledge of the radio channel is essential for channel equalization at the receiver side. Time-variant frequency-selective (doubly-selective) radio channels are typically tracked over time by using basis expansion model (BEM). The BEM models time variations by using the Doppler bandwidth, caused by the relative movement of receiver and transmitter/scatterers in a multipath environment. Tracking accuracy of the BEM relies on the use of the basis functions and the information of the Doppler bandwidth, e.g. use of discrete prolate spheroidal (DPS) sequences provides good accuracy but require high computational effort to generate DPS sequences. We present optimum BEM based on complex exponentials (CEs), computationally simple basis functions, which is able to track doubly-selective radio channels with accuracy better than other BEM variants. The analytical approach is confirmed by the simulation results for mobility up to 700 km/h in LTE-downlink. A novel method is also presented which does not require prior knowledge of the Doppler bandwidth for tracking doubly-selective radio channels yet providing results close to the proposed optimum BEM.

Fully Decentralized Approximate Zero-Forcing Precoding for Massive MIMO Systems

We analyze the downlink of a massive multiuser multiple input, multiple output (MIMO) system where antenna units at the base station are connected in a daisy chain without a central processing unit and only possess local channel knowledge. For this setup, we develop and analyze a linear precoding algorithm for suppressing interuser interference. It is demonstrated that the algorithm is close to zero-forcing precoding in terms of performance for a large number of antennas. Moreover, we show that with careful scheduling of processing across antennas, requirements for interconnection throughput are reduced compared with the fully centralized solution. Favorable tradeoff between performance and interconnection throughput makes the daisy chain a viable candidate topology for real-life implementations of base stations in MIMO systems where the number of antennas is very large.

Pursuance of mm-Level Accuracy: Ranging and Positioning in mmWave Systems

This paper aims at investigating the feasibility of ranging and positioning in millimeter (mm)-level accuracy by adopting millimeter-wave (mmWave) and three-dimensional massive antenna arrays. The agent to be localized is equipped with massive uniform rectangular array (URA) and multiple anchors are considered to pursue a higher accuracy, where a far-field environment is assumed for phased massive URA. By considering both a static scenario where the agent with an unknown coordinate is stationary and a dynamic scenario where the agent is moving, the fundamental limits of time-based ranging and positioning are derived by Cr $\acute{\text {a}}$ mer–Rao bound in a multipath model with and without a priori channel knowledge associated with mmWave transmission and massive URA. The relationship between the fundamental bound of range estimation and that of position estimation is theoretically clarified and the lower bound of position dilution of precision is derived. By investigating both models, the effect of the agent's movement to ranging and positioning accuracy is observed. Numerical results show that the proposed localization network achieves a precise mm-level accuracy for ranging and positioning.