Low-complexity Approach Research Articles

IQ Imbalance Compensation for Generalized Frequency Division Multiplexing Systems

Generalized frequency division multiplexing (GFDM) with a flexible structure is one of the promising candidates for the fifth generation wireless communication system. This letter presents a compensation scheme of in-phase and quadrature (IQ) imbalance caused by radio frequency front-end for GFDM. The motivation behind this letter is to relax the requirement on time-invariant channel in the process of estimating IQ imbalance parameters. A low computational complexity approach is proposed to directly estimate IQ imbalance parameters without any prior knowledge of channel. Meanwhile, the structure of training sequences that avoid channel estimation is derived. Simulation results confirm that the proposed method can reduce the effects of IQ imbalance and provide reliable performance even over severe IQ imbalance environment.

On generalized downlink beamforming with NOMA

Recently, nonorthogonal multiple access (NOMA) has been studied to increase the spectral efficiency of downlink in a multiuser system by exploiting the notion of superposition coding with successive interference cancellation (SIC). NOMA can be employed with downlink beamforming for downlink transmissions from a base station (BS) equipped with an antenna array, which results in NOMA beamforming. In this paper, we formulate a multiuser NOMA beamforming problem as a semidefinite programming (SDP) problem and generalize it in order to include the conventional (multiuser) beamforming. A low-complexity approach to decide SIC sets for the generalized NOMA beamforming is studied using the correlation between channel vectors for better performance. From analysis and simulation results, we show that the (generalized) NOMA beamforming can outperform the conventional beamforming, especially under limited scattering environments.

Robust Indoor Mobile Localization with a Semantic Augmented Route Network Graph

In recent years, using smartphones to determine pedestrian locations in indoor environments is an extensively promising technique for improving context-aware applications. However, the applicability and accuracy of the conventional approaches are still limited due to infrastructure-dependence, and there is seldom consideration of the semantic information inherently existing in maps. In this paper, a semantically-constrained low-complexity sensor fusion approach is proposed for the estimation of the user trajectory within the framework of the smartphone-based indoor pedestrian localization, which takes into account the semantic information of indoor space and its compatibility with user motions. The user trajectory is established by pedestrian dead reckoning (PDR) from the mobile inertial sensors, in which the proposed semantic augmented route network graph with adaptive edge length is utilized to provide semantic constraint for the trajectory calibration using a particle filter algorithm. The merit of the proposed method is that it not only exploits the knowledge of the indoor space topology, but also exhausts the rich semantic information and the user motion in a specific indoor space for PDR accumulation error elimination, and can extend the applicability for diverse pedestrian step length modes. Two experiments are conducted in the real indoor environment to verify of the proposed approach. The results confirmed that the proposed method can achieve highly acceptable pedestrian localization results using only the accelerometer and gyroscope embedded in the phones, while maintaining an enhanced accuracy of 1.23 m, with the indoor semantic information attached to each pedestrian’s motion.

Dual-dissimilarity measure-based statistical video cut detection

Video cut detection is an essential process of temporal continuity-based video applications such as video segmentation, video retargeting, and frame rate up-conversion. The performance of these applications highly depends on the performance of cut detection. This paper proposes an effective and low-complexity approach for detecting video cuts. The proposed method uses two simple dissimilarity measures for video cut detection: inter-frame luminance variation and temporal variation of inter-frame variations over several frames. The first is used to detect abrupt changes, and the second is used to reduce the influence of disturbances, e.g., object or camera motion. The proposed method is comprised of the following three steps. First, it computes the two dissimilarity measures. Then, it combines them using Bayesian estimation and linear regression. Finally, it decides on the possibility of cuts using the combined dissimilarity measure. Experimental results show that the average F1 score of the proposed method was up to 0.252 (37.0%) higher than those of the benchmark methods. Moreover, the algorithmic simplicity of the proposed method reduced the average computation time per pixel by up to 99.8%, when compared with state-of-the-art methods. Thus, the proposed method is superior to existing methods in terms of computational complexity and detection accuracy.

A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints

We consider caching in cellular networks in which each base station is equipped with a cache that can store a limited number of files. The popularity of the files is known and the goal is to place files in the caches such that the probability that a user at an arbitrary location in the plane will find the file that she requires in one of the covering caches is maximized. We develop distributed asynchronous algorithms for deciding which contents to store in which cache. Such cooperative algorithms require communication only between caches with overlapping coverage areas and can operate in asynchronous manner. The development of the algorithms is principally based on an observation that the problem can be viewed as a potential game. Our basic algorithm is derived from the best response dynamics. We demonstrate that the complexity of each best response step is independent of the number of files, linear in the cache capacity and linear in the maximum number of base stations that cover a certain area. Then, we show that the overall algorithm complexity for a discrete cache placement is polynomial in both network size and catalog size. In practical examples, the algorithm converges in just a few iterations. Also, in most cases of interest, the basic algorithm finds the best Nash equilibrium corresponding to the global optimum. We provide two extensions of our basic algorithm based on stochastic and deterministic simulated annealing which find the global optimum. Finally, we demonstrate the hit probability evolution on real and synthetic networks numerically and show that our distributed caching algorithm performs significantly better than storing the most popular content, probabilistic content placement policy and Multi-LRU caching policies.

A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints

A promising means to increase efficiency of cellular networks compared to existing architectures is to proactively cache data in the base stations. The idea is to store part of the data at the wireless edge and use the backhaul only to refresh the stored data. Data replacement will depend on the users' demand distribution over time. As this distribution is varying slowly, the stored data can be refreshed at off-peak times. In this way, caches containing popular content serve as helpers to the overall system and decrease the maximum backhaul load [1-5]. Our goal in this paper is on developing low-complexity distributed and asynchronous content placement algorithms. This is of practical relevance in cellular networks in which an operator wants to optimize the stored content in caches (i.e., base stations) while keeping the communication in the network to a minimum. In that case it will help that caches exchange information only locally.

Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.

Efficient multiview video plus depth coding for 3D-HEVC based on complexity classification of the treeblock

High efficiency video coding standard-based 3D video (3D-HEVC) has been extended from HEVC to improve the coding efficiency of multiview video plus depth (MVD). Similar to the joint model of HEVC, a computationally expensive exhaustive mode decision is performed to find the least rate-distortion cost for each treeblock in 3D-HEVC. Furthermore, additional coding tools have been added to 3D-HEVC for improving the coding efficiency of the dependent texture video and depth map. Those tools achieve the highest possible coding efficiency, but also bring a significant computational complexity which limits 3D-HEVC from real-time applications. In order to reduce computational complexity, we propose an efficient multiview video plus depth coding algorithm for 3D-HEVC that adaptively utilizes the complexity classification of the treeblock. The coding complexity model of a treeblock is first analyzed according to the prediction mode and coding mode from the corresponding treeblocks in the reference views. Based on the complexity classification model of the treeblock, we propose two efficient low-complexity approaches, including fast mode size decision and adaptive motion search range selection. Extensive experimental results demonstrate that the proposed MVD coding algorithm can achieve the average computational saving about 60.1% with negligible rate-distortion performance loss in comparison with the original 3D-HEVC encoder.

Low-Complexity Precoding for Sum Rate Maximization in Downlink Massive MIMO Systems

We propose a precoding scheme to improve the downlink sum rate for a multicell massive multiple-input multiple-output (MIMO) system. We first present a low-complexity approach based on dirty paper coding and zero-forcing that combines a reduced form of QR decomposition and an orthogonal projection. We formulate a downlink sum rate optimization problem that takes both intracell and intercell interference into account, and then we use the convex conjugate to transform the problem into an unconstrained dual problem to find an optimal solution by applying a quasi-Newton algorithm with low complexity per iteration. We prove that the proposed algorithm exhibits faster convergence than other methods, and the numerical results verify that the proposed precoding design outperforms conventional precoding methods in multicell massive MIMO systems.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

A Scalable Performance–Complexity Tradeoff for Constellation Randomization in Spatial Modulation

It is widely recognized that traditional single radio frequency (RF)-chain-aided spatial modulation (SM) does not offer any transmit diversity gain. As a remedy, constellation randomization (CR), relying on transmit prescaling (TPS), has been shown to provide transmit diversity for single-RF-chain-aided SM. In this paper, we propose a low-complexity approach to SM with the aid of constellation randomization (SM-CR) that considerably improves the transmit diversity gain of SM at a reduced computational burden compared with conventional SM-CR. While conventional SM-CR performs a full search among a set of candidate TPS factors to achieve the maximum minimum Euclidean distance (MED) in the received SM constellation, here, we propose a thresholding approach, where, instead of the maximum MED, the TPS aims to satisfy a specific MED threshold. This technique offers a significant complexity reduction with respect to the full maximization of SM-CR, since the search for TPS is terminated once a TPS set is found that satisfies the MED threshold. Our analysis and results demonstrate that a scalable tradeoff can be achieved between transmit diversity and complexity by appropriately selecting the MED threshold, where a significant complexity reduction is attained, while achieving a beneficial transmit diversity gain for the single-RF SM.

A Low-Complexity Approach to Computation of the Discrete Fractional Fourier Transform

This paper proposes an effective approach to the computation of the discrete fractional Fourier transform for an input vector of any length N. This approach uses specific structural properties of the discrete fractional Fourier transformation matrix. Thanks to these properties, the fractional Fourier transformation matrix can be decomposed into a sum of three or two matrices, one of which is a dense matrix, and the rest of the matrix components are sparse matrices. The aforementioned dense matrix has unique structural properties that allow advantageous factorization. This factorization is the main contributor to the reduction in the overall computational complexity of the discrete fractional Fourier transform computation. The remaining calculations do not contribute significantly to the total amount of computation. Thus, the proposed approach allows to reduce the number of arithmetic operations when calculating the discrete fractional Fourier transform.

Joint Design of User Association and Power Allocation With Proportional Fairness in Massive MIMO HetNets

Although the massive MIMO enabled heterogeneous networks (HetNets) can intensify the spectral efficiency benefits, the proper user association and resource allocation are both crucial to achieve desirable performance since the power consumption of base station (BS) scales with the large number of antennas. This paper aims to investigate joint design of user association and power allocation with loads constraint and transmit power constraint for the massive MIMO HetNets by considering proportional fairness about the spectral efficiency under imperfect channel state information. First, we derive a closed-form lower bound on the ergodic spectral efficiency with linear zero-forcing beamforming, based on which, a mixed-integer nonlinear programming problem is formulated. It is difficult to efficiently obtain an exact solution since it is non-convex and combinational. To solve this NP-hard problem, an effective algorithm with guaranteed convergence is proposed, where the original problem is decomposed into the corresponding subproblems, which can be solved by low complexity approaches, respectively. Numerical results show that how the number of antennas and the number of BSs affect the spectral efficiency, and our proposed algorithm outperforms other algorithms in terms of the spectral efficiency and load balancing.

Joint relay and jammer selection in Internet of Things systems

In this article, we consider the physical layer security issue in Internet of Things systems, in which there exist a sensing transceiver pair, a number of candidate nodes, and an eavesdropper. The transceiver pair needs to select a jammer node and a relay node among the candidate nodes so as to preserve the secrecy of the communications. Considering the diversity of candidate channels and the limited available power, it is infeasible to scan all the nodes and find the optimal one. We formulate this jammer and relay selection problem as an optimal stopping problem under a fixed sensing order. Then, through applying dynamic programming solution, we propose a low-complexity approach to obtain the optimal sensing order. The performance of the proposed selection scheme is evaluated through numerical results.

Outage Constrained Robust Beamforming for Secure Broadcasting Systems With Energy Harvesting

In this paper, we investigate simultaneous wireless information and power transfer systems for multiuser multiple-input single-output secure broadcasting channels. Considering imperfect channel state information, we introduce a robust secure beamforming design, where the transmit power is minimized subject to the secrecy rate outage probability constraint for legitimate users and the harvested energy outage probability constraint for energy harvesting receivers. The original problem is non-convex due to the presence of the probabilistic constraints. With the aid of Bernstein-type inequalities, we transform the outage constraints into the deterministic forms. Based on a successive convex approximation (SCA) method, we propose a low-complexity approach, which reformulates the original problem as a second-order cone programming problem. Also, we prove the convergence of the SCA-based iterative algorithm. Simulation shows that the proposed scheme outperforms the conventional method with lower complexity.

Rotation-Based Design and Synthesis of Quantum Circuits

In this paper, a new synthesis method for Boolean irreversible functions has been proposed which is based on rotation quantum operators. The primary motivation is that the implementation of these operators is commonly available in quantum technologies. We introduce an approach for synthesizing Boolean irreversible functions in quantum logic which is based on quantum rotation and CNOT operators and uses a prespecified fixed design where by changing the rotation angles a new functionality can be achieved. So this design can be viewed as a programmable array of rotation gates. If reduction in quantum cost is desired, after this step the optimization approach may be applied to reduce the number of gates by using some rules, which leads to a reduction of up to 48% in the quantum cost after optimization compared to the original nonoptimized design obtained from the synthesis method. The paper also introduces efficient designs with rotation operators for some important gates in the quantum circuit libraries. Our main contribution is introducing a novel and low complexity approach for synthesizing irreversible functions using elementary rotation operators with a regular fixed design. Also having as many functions as desired with the same inputs, the execution time just increases by one for each additional function and the circuit width is increased by one for each additional function. It is proved that our approach can always be applied to each Boolean function. We show the correctness of our approach and make an upper bound of the quantum cost of the circuit.

Large-Scale MIMO Detection Using MCMC Approach With Blockwise Sampling

In this paper, a low-complexity approach for the large-scale (underdetermined) multiple-input multiple-output (MIMO) detection is proposed using the Markov chain Monte Carlo (MCMC) algorithm in conjunction with blockwise sampling. Klein’s algorithm is employed in each sub-system to draw multidimensional samples for an MCMC detector in iterative detection and decoding (IDD). From analysis, we find that the lattice reduction (LR) technique cannot improve the performance of the proposed MCMC-based approach under low-correlated channel environment. In addition, due to blockwise sampling, the proposed method exhibits a faster convergence speed when running a Markov chain and provides a near-optimal performance for the detection of underdetermined MIMO systems. Complexity analysis and simulation results show that the proposed approach outperforms the conventional LR-based Klein randomized successive interference cancellation (SIC) detection with a relatively low complexity.

Low-Complexity and Context-Aware Estimation of Spatial and Temporal Activity Parameters for Automotive Camera Rate Control

Rate control in video compression adjusts the encoding parameters to reach a certain target bitrate for the encoded video. State-of-the-art rate controllers for hybrid video coding typically employ content-dependent video bitrate models and video quality metrics (VQMs). To capture the content characteristics, temporal and spatial video activity measures are determined from the raw video using computationally complex algorithms that require access to the uncompressed source video. In automotive deployments, however, full access to the uncompressed source video and the internal functions of video encoders is typically not possible. As a remedy, in this paper, we present a low-complexity approach to estimate the temporal activity (TA) and spatial activity (SA) measures for videos that are captured by a front-facing camera of a vehicle, based on the context information of the vehicle. To this end, we exploit information about the dynamics of the vehicle and other vehicles in the field-of-view of the front-facing camera. We apply the estimated TA and SA values to a video bitrate model and an objective VQM and use these models to solve the rate control problem to determine the optimal encoding settings for given bitrate constraints. The proposed low-complexity solution offers a similar accuracy in achieving rate constraints and similar perceptual quality characteristics as a solution that uses the computed TA and SA values, with the advantage that no access to the uncompressed source video stream or the internal functions of the video encoder is required.

A low-complexity image compression approach with single spatial prediction mode and transform

The well-known low-complexity JPEG and the newer JPEG-XR systems are based on block-based transform and simple transform-domain coefficient prediction algorithms. Higher complexity image compression algorithms, obtainable from intra-frame coding tools of video coders H.264 or HEVC, are based on multiple block-based spatial-domain prediction modes and transforms. This paper explores an alternative low-complexity image compression approach based on a single spatial-domain prediction mode and transform, which are designed based on a global image model. In our experiments, the proposed single-mode approach uses an average 20.5 % lower bit-rate than a standard low-complexity single-mode image coder that uses only conventional DC spatial prediction and 2-D DCT. It also does not suffer from blocking effects at low bit-rates.

HF/UHF dual band RFID transponders for an information-driven public transportation system

The limited communication range of HF radio frequency identification (RFID) smart cards does not allow to automatically assess the alighting point of passengers on public transportation. This work considers HF/UHF dual band RFID transponders for electronic ticketing in an information-driven public transportation system. The UHF RFID interface is intended to enable a remote detection of passengers inside a vehicle and thus to assess the alighting point of passengers. A review of the specific operating conditions for the UHF RFID link is given and associated privacy issues are addressed. We introduce a low-complexity approach to safeguard the privacy of passengers. Following a discussion of the architecture of appropriate HF/UHF dual band transponder ICs, the focus is set on questions of the circuit implementation. With regard to the progressing CMOS technology scaling, we can observe potential risks of overvoltage stress for the UHF RFID front-end at high input powers. Two RF voltage limiters that prevent overvoltages are presented. Experimental results of circuit prototypes in a 40 nm CMOS technology demonstrate the functionality of two UHF RFID front-ends that incorporate the RF voltage limiters.