Dimension Of Signal Space Research Articles

Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension

With the increasingly obvious trend of satellite communication and network integration, especially the emergence of inter satellite links, the interconnection between space nodes has become the development trend and inevitable requirement of future space communication. However, problems, such as long communication distance, large transmission delay and loss, limited network resources and frequent switching of transmission links, limit the ability of spatial information transmission. Firstly, based on the idea of ring planning and design, this paper uses the joint design of beamforming between nodes to design the physical layer network coding in the relay to realize the reliable transmission of information. Secondly, according to the diversity of relay signal space resources and network node information exchange, a joint design method of relay compression matrix and node precoding vector is studied, which breaks through the existing configuration constraints. In this scheme, the computational complexity is reduced by compressing and precoding the matrix to ensure reliable decoding while obtaining spatial alignment gain and degrees of freedom. Simulated and real data results demonstrate the superiority and effectiveness of the proposed method.

Joint Optimization of Received Signal Power and Signal Space Dimensions for MIMO Broadcast Channels

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR.

Analytical Modeling of Nonlinear Fiber Propagation for Four Dimensional Symmetric Constellations

Coherent optical transmission systems naturally lead to a four dimensional (4D) signal space, i.e., two polarizations each with two quadratures. In this paper we derive an analytical model to quantify the impact of Kerr nonlinearity on such 4D spaces, taking the interpolarization dependency into account. This is in contrast to previous models such as the GN and EGN models, which are valid for polarization multiplexed (PM) formats, where the two polarizations are seen as independent channels on which data is multiplexed. The proposed model agrees with the EGN model in the special case of independent two-dimensional modulation in each polarization. The model accounts for the predominant nonlinear terms in a WDM system, namely self-phase modulation and and cross-phase modulation. Numerical results show that the EGN model may inaccurately estimate the nonlinear interference of 4D formats. This nonlinear interference discrepancy between the results of the proposed model and the EGN model could be up to 2.8 dB for a system with 80 WDM channels. The derived model is validated by split-step Fourier simulations, and it is shown to follow simulations very closely.

Hybrid Constellations for Dual-Polarized Wireless Communications

This paper considers constellation designs for 2×2 systems with a coherent dual-polarized antenna configuration. Such an architecture enables a four dimensional (4D) signal space that exploits polarization, and this paper considers hybrid constellations formed as a combination of amplitude/phase states and polarization states. For coherent detection, phase shift keying (PSK) is combined with polarization shift keying (PolSK) to produce constant-power PSK+PolSK constellations that consistently outperform PSK+PSK. Next, quadrature amplitude modulation (QAM) and amplitude phase shift keying (APSK) are combined with PolSK to construct hybrid QAM+PolSK and APSK+PolSK constellations, and constrained capacity results indicate the viability of coded modulation based on these signal spaces. For non-coherent detection, differential PSK (DPSK) is combined with PolSK to yield DPSK+PolSK signaling that does not require phase alignment at the receiver and outperforms DPSK+DPSK, both with and without the adverse effect of carrier frequency offset and symbol time offset. The methods are verified in over-the-air testing with universal software radio peripherals (USRPs) integrated in a dual-polarized testbed. Error performances of all proposed 4D hybrid constellations in an OFDM framework are analyzed under both quasi-AWGN and multipath fading scenarios.

Development of Hankel‐SVD hybrid technique for multiple noise removal from PD signature

Detection and measurement of partial discharge (PD) phenomena combined with the separation and identification of PD sources is the way to achieve effective insulation integrity assessment. However, during measurement, PD signals are coupled with interferences (discrete spectral, pulsive, and white noises). Recovering PD signals from such interferences would improve PD source separation (thus identification), but still remains a challenging task. Several denoising methods have been proposed to suppress interferences. However, using a universal method to achieve interference removal is probably impossible, as the characteristics of the interferences are distinct. This study proposes a novel low-rank H-Matrix-based singular value decomposition (SVD) filter (H-SVD) that removes different types of interferences. Denoising is done by projecting the measured pulse in a lower dimensional signal space. To assess the effectiveness of the proposed method, H-SVD filter is first applied to simulated PD data and later on real-time PD data with the introduction of three different types of synthetic noises. The results of the evaluation metrics confirm that H-SVD has significant performance improvements compared to existing state-of-the-art PD denoising methods.

Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks

In this paper, we propose a multi-dimensional sparse-coded ambient backscatter communication (MSC-AmBC) system for long-range and high-rate massive Internet of things (IoT) networks. We utilize the characteristics of the ambient sources employing orthogonal frequency division multiplexing (OFDM) modulation to mitigate strong direct-link interference and improve signal detection of AmBC at the reader. Also, utilization of the sparsity originated from the duty-cycling operation of batteryless RF tags is proposed to increase the dimension of signal space of backscatter signals to achieve either diversity or multiplexing gains in AmBC. We propose optimal constellation mapping and reflection coefficient projection and expansion methods to effectively construct multi-dimensional constellation for high-order backscatter modulation while guaranteeing sufficient energy harvesting opportunities at these tags. Simulation results confirm the feasibility of the long-range and high-rate AmBC in massive IoT networks where a huge number of active ambient sources and passive RF tags coexist.

Huffman Coding-Based Adaptive Spatial Modulation

Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.

Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces

In this paper, we consider the potential of data-transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which conceptually goes beyond contemporary massive MIMO technology, that arises from our vision of a future where man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". We consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array. Under the condition that the surface-area is sufficiently large, the received signal after a matched-filtering (MF) operation can be closely approximated by a sinc-function-like intersymbol interference (ISI) channel. We analyze the capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show that the number of independent signal dimensions per m deployed surface is 2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2 for two and three dimensional terminal-deployments. Lastly, we consider implementations of the LIS in the form of a grid of conventional antenna elements and show that, the sampling lattice that minimizes the surface-area of the LIS and simultaneously obtains one signal space dimension for every spent antenna is the hexagonal lattice. We extensively discuss the design of the state-of-the-art low-complexity channel shortening (CS) demodulator for data-transmission with the LIS.

Low-complexity ESPRIT-based DOA estimation for MIMO radar with L-shaped array

ABSTRACTAiming at the problem of two-dimensional (2D) angles estimation for monostatic multiple-input–multiple-output radar with L-shaped array, a novel reduced-dimensional (RD) direction of arrival estimation (DOA) method based on estimation of signal parameters by rotational invariance techniques (ESPRIT) algorithm is proposed. First, through the RD matrix design and RD transformation, the higher dimensional received data can be transformed into a lower dimensional signal space. Then the signal space can be achieved via the eigen-value decomposition. Last, conjoint with the ESPRIT algorithm, 2D spatial angle parameters with automatic pairing can be jointly estimated. The proposed method can remove data redundancy of high dimensional received data at the greatest degree and enhance sensors efficiency without costing the aperture of array, with a result of better performance than 2D-ESPRIT algorithm. Moreover, compared with the multiple signal classification (MUSIC)-like algorithms, the proposed method has the better overall performance and is more available for a lower computation complexity and higher instantaneity of application. Simulation results verify that the correctness of theoretical analysis and the effectiveness of proposed algorithm.

User Selection Algorithms for Block Diagonalization Aided Multiuser Downlink mm-Wave Communication

In order to meet the ever increasing data rate demands, the next generation of wireless communication systems is being designed for exploiting the large amounts of unused spectrum in the millimeter-wave (mm-wave) band. Since operating at mm-wave frequencies imposes several challenges, such as high-path loss, as well as both spatial and temporal channel sparsity, there is a significant research interest focused on designing feasible solutions for establishing reliable and high-throughput links at mm-wave frequencies. In this paper, we consider a cellular system relying on hybrid beamforming aided base station (BS) as well as user equipment and study the user selection problem, which has not been hitherto studied in the literature. More specifically, we study the problem of selecting $K^\prime $ -users by the BS for communication out of $K$ -users whilst ensuring that the sum rate is maximized. Specifically, we propose a user selection algorithm, which relies on the knowledge of both the channel gains and the angle of departure (AoD) of the channel paths spanning to the various users, which is termed the AoD aided user selection (AoD-US). Furthermore, we devise a pair of subspace metrics based on: 1) the angle between the subspaces spanned by the BS array response vectors; 2) the ratio of interference and signal space dimensions of various users, in order to reduce the user search space in AoD-US. This modified user selection algorithm is termed the AoD aided user selection with user set pruning (AoD-US-P). Furthermore, we study the attainable sum-rate performance of the block-diagonalization aided downlink and show that the proposed selection algorithms guarantee both multiuser diversity and multiplexing gains. Additionally, the proposed algorithms are studied in the round-robin scheduling scenario, where all the $K$ -users are scheduled for achieving fairness. Our simulation results revealed that the AoD-US-P achieves nearly the same performance as that achieved by the AoD-US despite having a small user set, while both are observed to outperform the channel power-based selection scheme.

Modulation Designs for Visible Light Communications With Signal-Dependent Noise

This paper proposes efficient modulation design methods for single-carrier and multicarrier visible light communication systems with signal-dependent shot noise (SDSN). Improved constellations are obtained through solving optimization problems aiming at minimizing the system symbol error rate, subject to lighting constraints. For single-carrier systems, maximum likelihood based detection, is applied. Constellation and threshold values are optimized jointly. The proposed method outperforms both the uniform signaling and the square-root signaling. It is also observed that the uniform signaling outperforms the square-root signaling through the medium-to-high signal to noise region, while the latter performs better in lower SNR region. For multicarrier systems, we formulate a new channel model with the SDSN taken into account. By maximizing the minimum Euclidean distance between the symbols, improved constellations are designed under different lighting constraints. Furthermore, the multicarrier part of design embraces the DC-informative design framework, and thus achieve high power efficiency. The performance gain of our DC-informative optical OFDM (DCIO-OFDM) over OFDM-QPSK ranges from 5 to 30dB even in moderate dimensional signal space.

Direct Sum Simplex Signal Space Codes for DNA Nucleotides and Amino Acid Codons

A genomic symbol to signal conversion concept is introduced and demonstrated on nucleotide base pairs and amino acid codons. Binary encodings of the presence or absence of bioinformatic attributes (e.g., physical, chemical, functional, structural, etc.) onto Euclidean signal space points of $+1$ or $-1$ is performed for individual base pairs or blocks of DNA symbols such as amino acid codons. The symbol to Euclidean space mapping is further encoded into an expanded digital representation based on a direct sum $M$ -ary tree structure called a $\sigma$ -tree. For bioinformatic query sequences, the $\sigma$ -tree provides a rapid search mechanism to find near neighbor similarity sets within warehouses of heterogeneous bioinformatic data. Bioinformatic utility will be achieved in data mining systems based on the proposed symbol-to-signal mapping method through trend analysis of bioinformatic attribute data within similarity set aggregates. Query specific feature/attribute similarity sets are revealed by high dimensional Euclidean signal space clusters. The proposed binary encoded signal vectors provide an equal energy Euclidean space simplex code in the sense of channel coding signal constellation design. Use of an equal energy simplex code prevents biases from being introduced among different attributes during the symbol sequence to signal sequence mapping. The inappropriate use of a multienergy, low dimension, signal point mapping is replaced with a same-energy, high dimension, signal point mapping. The $M$ -ary digital encoding and associated near neighbor search method is extensible to nucleotide sequence blocks of any length, producing digitally expressed signal space simplex representations on a hypersphere of high dimension. Moreover, the direct sum structure empowers computation and memory efficiencies that grow only linearly with increasing sequence length and signal set dimensionality. The bioinformatic attribute encoding method and efficient $\sigma$ -tree expanded digital representation introduced here will provide a foundation for constructing novel genomic driven data mining systems.

Low RF-Complexity Millimeter-Wave Beamspace-MIMO Systems by Beam Selection

Communications in millimeter-wave (mm-wave) spectrum (30-300 GHz) have experienced a continuous increase in relevance for short-range, high-capacity wireless links, because of the wider bandwidths they are able to provide. In this work, we introduce a new mm-wave frequency transmission scheme that exploits a combination of the concepts of beamspace multi-input multi-output (B-MIMO) communications and beam selection to provide near-optimal performances with a low hardware-complexity transceiver. While large-scale MIMO approaches in mm-wave are affected by high dimensional signal space that increases considerably both complexity and costs of the system, the proposed scheme is able to achieve near-optimal performances with a reduced radio-frequency (RF) complexity thanks to beam selection. We evaluate the advantages of the proposed scheme via capacity computations, comparisons of numbers of RF chains required and by studying the trade-off between spectral and power efficiency. Our analytical and simulation results show that the proposed scheme is capable of offering a significant reduction in RF complexity with a realistic low-cost approach, for a given performance. In particular, we show that the proposed beam selection algorithms achieve higher power efficiencies than a full system where all beams are utilized.

Multi-User Linear Precoding for Multi-Polarized Massive MIMO System Under Imperfect CSIT

The space limitation and channel acquisition prevent Massive MIMO from being easily deployed in a practical setup. Motivated by current deployments of LTE-Advanced, the use of multi-polarized antenna elements can be an efficient solution to address the space constraint. Furthermore, the dual-structured precoding, in which a preprocessing based on the spatial correlation and a subsequent linear precoding based on the short-term channel state information at the transmitter (CSIT) are concatenated, can reduce the feedback overhead efficiently. By grouping and preprocessing spatially correlated mobile stations (MSs), the dimension of the precoding signal space is reduced and the corresponding short-term CSIT dimension is reduced. In this paper, to reduce the feedback overhead further, we propose a dual-structured multi-user linear precoding, in which the subgrouping method based on co-polarization is additionally applied to the spatially grouped MSs in the preprocessing stage. Furthermore, under imperfect CSIT, the proposed scheme is asymptotically analyzed based on random matrix theory. By investigating the behavior of the asymptotic performance, we also propose a new dual-structured precoding in which the precoding mode is switched between two dual-structured precoding strategies with 1) the preprocessing based only on the spatial correlation and 2) the preprocessing based on both the spatial correlation and polarization. Finally, we extend it to 3D dual-structured precoding.

Extended Sparse Multipath Channel Capacity Estimation Based on Adaptive Array Configuration

A virtual representation of the sparse multipath wireless channel model is proposed based on a physical wireless channel, the channel corresponds to a delay in Doppler scattering angle, uniform sampling in signal space dimensions. This virtual representation model, through the antenna array configuration extension on MIMO channel capacity was estimated. The simulation results show that the three typical antenna array configuration enough that the performance of channel optimal at all SNR conditions can be approximated, reconfigurable arrays can achieve channel capacity expansion purposes.

A Novel Channel Estimation Algorithm for Underwater Acoustic Systems

To decrease the computational complexity and improve the performance of channel estimation for underwater acoustic (UWA) sparse multipath channels, a sparse least square (SLS) channel estimation algorithm is proposed. The proposed algorithm combines advantages of both generalized akaike information criterion (GAIC) estimation and estimation using effective order of channel impulse response (CIR). Known pilot data is used to estimate effective order of CIR and then the position of taps of CIR is estimated. In order to adapt to the environment with low SNR and reduce the dimension of signal space, adaptive threshold is also used. Simulation results indicate that the proposed method has good performance of channel estimation and low complexity.

Rotated Multi-D Constellations in Rayleigh Fading: Mutual Information Improvement and Pragmatic Approach for Near-Capacity Performance in High-Rate Regions

This paper studies the mutual information improvement attained by rotated multidimensional (multi-D) constellations via a unitary precoder G in Rayleigh fading. At first, based on the symmetric cut-off rate of the N-D signal space, we develop a design criterion with regard to the precoder G. It is then demonstrated that the use of rotated constellations in only a reasonably low dimensional signal space can significantly increase the mutual information in high-rate regimes. Based on parameterizations of unitary matrices, we then construct good unitary precoder G in 4-D signal space using a simple optimization problem, which involves only four real variables and it is applicable to any modulation scheme. To further illustrate the potential of multi-D constellation and to show the practical use of mutual information improvement, we propose a simple yet powerful bit-interleaved coded modulation (BICM) scheme in which a (multi-D) mapping technique employed in a multi-D rotated constellation is concatenated with a short-memory high-rate convolutional code. By using extrinsic information transfer (EXIT) charts, it is shown that the proposed technique provides an exceptionally good error performance. In particular, both EXIT chart analysis and simulation results indicate that a turbo pinch-off and a bit error rate around 10-6 happen at a signal-to-noise ratio that is well below the coded modulation and BICM capacities using traditional signal sets. For example, with code rates ranging from 2/3 to 7/8, the proposed system can operate 0.82 dB-2.93 dB lower than the BICM capacity with QPSK and Gray labeling. The mutual information gain offered by rotated constellations can be therefore utilized to design simple yet near Shannon limit systems in the high-rate regions.

Asynchronous code-division random access using convex optimization

Many applications in cellular systems and sensor networks involve a random subset of a large number of users asynchronously reporting activity to a base station. This paper examines the problem of multiuser detection (MUD) in random access channels for such applications. Traditional orthogonal signaling ignores the random nature of user activity in this problem and limits the total number of users to be on the order of the number of signal space dimensions. Contention-based schemes, on the other hand, suffer from delays caused by colliding transmissions and the hidden node problem. In contrast, this paper presents a novel pairing of an asynchronous non-orthogonal code-division random access scheme with a convex optimization-based MUD algorithm that overcomes the issues associated with orthogonal signaling and contention-based methods. Two key distinguishing features of the proposed MUD algorithm are that it does not require knowledge of the delay or channel state information of every user and it has polynomial-time computational complexity. The main analytical contribution of this paper is the relationship between the performance of the proposed MUD algorithm in the presence of arbitrary or random delays and two simple metrics of the set of user codewords. The study of these metrics is then focused on two specific sets of codewords, random binary codewords and specially constructed algebraic codewords, for asynchronous random access. The ensuing analysis confirms that the proposed scheme together with either of these two codeword sets significantly outperforms the orthogonal signaling-based random access in terms of the total number of users in the system.

Cross-Layer Based Analysis of Multi-Hop Wireless Networks

In this work, we revisit classical packet radio networks with a modern treatment of physical-layer (PHY) procedures, medium-access (MAC) and geographic channel-driven routing protocols. Our network model assumes that nodes are randomly distributed on the plane according to a homogeneous spatial Poisson process, using which we provide a novel representation of interference statistics resulting from packet collisions. Using this representation, we develop a cross-layer analysis methodology which allows multi-hop routing protocols to be treated using generic information-theoretic models for the underlying PHY/MAC procedures. These models inherently characterize modern procedures such as channel code rate adaptation, incremental redundancy and packet combining/capture. These models further allow for the assessment of the tradeoff between spatial throughput, measured in bit-meters per signal-space dimension, the range of each transmission and the average transmission delay. A generic formulation based on system parameters, such as system bandwidth, propagation models, etc., is given to analyze this tradeoff in an operational setting which can be used to build system simulators for such networks. Finally, from a purely PHY perspective, the results of this work show that coding and incremental retransmission provide a means for reliable communication coupled with a completely decentralized multiple-access strategy.

Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> High-rate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Training-based methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional training-based channel estimation methods, typically comprising linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing (CCS), can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional least-squares-based training methods. </para>