This paper proposes low complexity resource allocation in frequency domain non-orthogonal multiple access where many devices access with a base station. The number of the devices is assumed to be more than that of the resource for network capacity enhancement, which is demanded in massive machine type communications (mMTC). This paper proposes two types of resource allocation techniques, all of which are based on the MIN-MAX approach. One of them seeks for nicer resource allocation with only channel gains. The other technique applies the message passing algorithm (MPA) for better resource allocation. The proposed resource allocation techniques are evaluated by computer simulation in frequency domain non-orthogonal multiple access. The proposed technique with the MPA achieves the best bit error rate (BER) performance in the proposed techniques. However, the computational complexity of the proposed techniques with channel gains is much smaller than that of the proposed technique with the MPA, whereas the BER performance of the proposed techniques with channel gains is only about 0.1dB inferior to that with the MPA in the multiple access with the overloading ratio of 1.5 at the BER of 10-4. They attain the gain of about 10dB at the BER of 10-4 in the multiple access with the overloading ration of 2.0. Their complexity is 10-16 as small as the conventional technique.

Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.

This letter presents an efficient technique to reduce the computational complexity involved in training binary convolutional neural networks (BCNN). The BCNN training shall be conducted focusing on the optimization of the sign of each weight element rather than the exact value itself in convention; in which, the sign of an element is not likely to be flipped anymore after it has been updated to have such a large magnitude to be clipped out. The proposed technique does not update such elements that have been clipped out and eliminates the computations involved in their optimization accordingly. The complexity reduction by the proposed technique is as high as 25.52% in training the BCNN model for the CIFAR-10 classification task, while the accuracy is maintained without severe degradation.

This paper proposes a novel low complexity lattice reduction-aided iterative receiver for overloaded MIMO. Novel noise cancellation is proposed that increases an equivalent channel gain with a scalar gain introduced in this paper, which results in the improvement of the signal to noise power ratio (SNR). We theoretically analyze the performance of the proposed receiver that the lattice reduction raises the SNR of the detector output signals as the scalar gain increases, when the Lenstra-Lenstra-Lova's (LLL) algorithm is applied to implement the lattice reduction. Because the SNR improvement causes the scalar gain to increase, the performance is improved by iterating the reception process. Computer simulations confirm the performance. The proposed receiver attains a gain of about 5dB at the BER of 10-4 in a 6×2 overloaded MIMO channel. Computational complexity of the proposed receiver is about 1/50 as much as that of the maximum likelihood detection (MLD).

In this paper, we devise low-complexity uplink detection algorithms for Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt the Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. In addition, to reduce the order of Landweber Method by introducing a set of coefficients, we propose reduced order Landweber Method (ROLM) algorithm. An analysis on the convergence and the complexity is provided. Numerical results demonstrate that the proposed algorithms outperform the existing algorithm.

In many communications applications, maximum-likelihood decoding reduces to solving an integer least-squares problem, which is NP-hard in the worst case. It has recently been shown that over a wide range of dimensions and SNRs, the branch and bound (BB) algorithm can be used to find the exact solution with an expected complexity that is roughly cubic in the dimension of the problem. However, the computational complexity becomes prohibitive if the SNR is too low and/or the dimension of the problem is too large. The dichotomous coordinate descent (DCD) algorithm provides low complexity, but its detection performance is not as good as that of the BB detector. Two methods are developed to bound the optimal detector cost to reduce the complexity of BB in this paper. These methods are DCD-based detectors for MIMO and multiuser detection in the scenario of a large number of transmitting antennas/users. First, a combined detection technique based on the BB and DCD algorithms is proposed. The technique maintains the advantages of both algorithms and achieves a good trade-off between performance and complexity compared to using only the BB or DCD algorithm. Second, since the first feasible solution obtained from the BB search is the solution of the decorrelating decision feedback (DF) method and because DCD results in better accuracy than the decorrelating DF solution, we propose that the first feasible solution of the BB algorithm be obtained by the box-constrained DCD algorithm rather than the decorrelating DF detector. This method improves the precision of the initial solution and identifies more branches that can be eliminated from the search tree. The results show that the DCD-based BB detector provides optimal detection with reduced worst-case complexity compared to that of the decorrelating DF-based BB detector.

In wireless communication systems, OFDM technology is a communication method that can yield high data rates. However, OFDM systems suffer high PAPR values due to the use of many of subcarriers. The SLM and the PTS technique were proposed to solve the PAPR problem in OFDM systems. However, these approaches have the disadvantage of having high complexity. This paper proposes a method which has lower complexity than the conventional PTS method but has less performance degradation.

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm is developed to approximate the RLS-based online NMF algorithm with low complexity. The proposed algorithm is evaluated in terms of audio source separation, and the results show that the performance of the proposed algorithms are superior to that of the conventional online NMF algorithm with significantly reduced complexity.

In this letter, we investigate the problem of feedforward timing estimation for burst-mode satellite communications. By analyzing the correlation property of frame header (FH) acquisition in the presence of sampling offset, a novel data-aided feedforward timing estimator that utilizes the correlation peaks for interpolating the fractional timing offset is proposed. Numerical results show that even under low signal-to-noise ratio (SNR) and small rolloff factor conditions, the proposed estimator can approach the modified Cramer-Rao bound (MCRB) closely. Furthermore, this estimator only requires two samples per symbol and can be implemented with low complexity with respect to conventional data-aided estimators.

Many selected mapping (SLM) schemes have been proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal sequences. In this paper, an efficient selection (ES) method of the OFDM signal sequence with minimum PAPR among many alternative OFDM signal sequences is proposed; it supports various SLM schemes. Utilizing the fact that OFDM signal components can be sequentially generated in many SLM schemes, the generation and PAPR observation of the OFDM signal sequence are processed concurrently. While the u-th alternative OFDM signal components are being generated, by applying the proposed ES method, the generation of that alternative OFDM signal components can be interrupted (or stopped) according to the selection criteria of the best OFDM signal sequence in the considered SLM scheme. Such interruption substantially reduces the average computational complexity of SLM schemes without degradation of PAPR reduction performance, which is confirmed by analytical and numerical results. Note that the proposed method is not an isolated SLM scheme but a subsidiary method which can be easily adopted in many SLM schemes in order to further reduce the computational complexity of considered SLM schemes.

In this paper, an iterative optimal method is proposed to design the prototype filters for a fast filter bank (FFB) with low complexity, aiming to control the optimum ripple magnitude tolerance of each filter according to the overall specifications. This problem is formulated as an optimization problem for which the total number of multiplications is to be minimized subject to the constrained ripple in the passband and stopband. In the following, an iterative solution is proposed to solve this optimization problem for the purpose of obtaining the impulse response coefficients with low complexity at each stage. Simulations are conducted to verify the performance of the proposed scheme and show that compared with the original method, the proposed scheme can reduce about 24.24% of multiplications. In addition, the proposed scheme and the original method provide similar mean square error (MSE) and the mean absolute error (MAE) of the frequency response.

Coordinated multi-point (CoMP) transmission and reception is a promising technique for interference mitigation in cellular systems. The scheduling algorithm for CoMP has a significant impact on the network processing complexity and performance. Performing exhaustive search permits centralized scheduling and thus the optimal global solution; however, it incurs a high level of computational complexity and may be impractical or lead to high cost as well as network instability. In order to provide a more realistic scheduling method while balancing performance and complexity, we propose a low complexity centralized scheduling scheme that adaptively selects users for single-cell transmission or different CoMP scheme transmission to maximize the system weighted sum capacity. We evaluate the computational complexity and system-level simulation performance in this paper. Compared to the optimal scheduling method with exhaustive search, the proposed scheme has a much lower complexity level and achieves near optimal performance.

Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has higher spectral efficiency than the standard cyclic prefix OFDM (CP-OFDM) OFDM by replacing the random CP with the known training sequence (TS), which could be also used for synchronization and channel estimation. However, TDS-OFDM requires suffers from performance loss over fading channels due to the iterative interference cancellation has to be used to remove the mutual interferences between the TS and the useful data. To solve this problem, the novel TS based OFDM transmission scheme, referred to as the unified time-frequency OFDM (UTF-OFDM), is proposed in which the time-domain TS and the frequency-domain pilots are carefully designed to naturally avoid the interference from the TS to the data without any reconstruction. The proposed UTF-OFDM based flexible frame structure supports effective channel estimation and reliable channel equalization, while imposing a significantly lower complexity than the TDS-OFDM system at the cost of a slightly reduced spectral efficiency. Simulation results demonstrate that the proposed UTF-OFDM substantially outperforms the existing TDS-OFDM, in terms of the system's achievable bit error rate.

In time-varying frequency selective channels, to obtain high-rate joint time-frequency diversity, linear dispersion coded orthogonal frequency division multiplexing (LDC-OFDM), has recently been proposed. Compared with OFDM systems, single-carrier systems may retain the advantages of lower PAPR and lower sensitivity to carrier frequency offset (CFO) effects, which motivates this paper to investigate how to achieve joint frequency and time diversity for high-rate single-carrier block transmission systems. Two systems are proposed: linear dispersion coded cyclic-prefix single-carrier modulation (LDC-CP-SCM) and linear dispersion coded zero-padded single-carrier modulation (LDC-ZP-SCM) across either multiple CP-SCM or ZP-SCM blocks, respectively. LDC-SCM may use a layered two-stage LDC decoding with lower complexity. This paper analyzes the diversity properties of LDC-CP-SCM, and provides a sufficient condition for LDC-CP-SCM to maximize all available joint frequency and time diversity gain and coding gain. This paper shows that LDC-ZP-SCM may be effectively equipped with low-complexity minimum mean-squared error (MMSE) equalizers. A lower complexity scheme, linear transformation coded SCM (LTC-SCM), is also proposed with good diversity performance.

This letter addresses antenna ordering to improve the performance of the MIMO detectors in [4], where two low complexity MIMO detectors have been proposed based on either fully-connected or ring type pair-wise Markov random field (MRF). The former was shown to be better than the latter, while being more complex. The objective of this letter is to make the performance of the detector based on ring-type MRF (with complexity of O(2M 22m)) close to or better than that of fully-connected MRF (with complexity of O(M (M-1)22m)), by applying appropriate antenna ordering. The simulation results validate the proposed antenna ordering methods.

This paper presents an efficient algorithm, NEAR, that allows sensor nodes to acquire their off-duty eligibility. Any node only needs to calculate the coverage degrees of the intersections on its sensing boundary, and cooperates with its neighbors to know if it is redundant or not. The computing complexity of NEAR is only O(nlogn).

This paper proposes a novel scheme to reduce the complexity of existing transmit diversity solutions to time domain synchronous OFDM (TDS-OFDM). The space shifted constant amplitude zero autocorrelation (CAZAC) sequence based preamble is proposed for channel estimation. Two flexible frame structures are proposed for adaptive system design as well as cyclicity reconstruction of the received inverse discrete Fourier transform (IDFT) block. With regard to channel estimation and cyclicity reconstruction, the complexity of the proposed scheme is only around 7.20% of that of the conventional solutions. Simulation results demonstrate that better bit error rate (BER) performance can be achieved over doubly selective channels.

This paper proposes a low-complexity signal detection algorithm for spatially correlated multiple-input multiple-output (MIMO) channels. The proposed algorithm sets a minimum mean-square error (MMSE) detection result to the starting point, and searches for signal candidates in multi-dimensions of the noise enhancement from which the MMSE detection suffers. The multi-dimensional search is needed because the number of dominant directions of the noise enhancement is likely to be more than one over the correlated MIMO channels. To reduce the computational complexity of the multi-dimensional search, the proposed algorithm limits the number of signal candidates to O(NT) where NT is the number of transmit antennas and O( ) is big O notation. Specifically, the signal candidates, which are unquantized, are obtained as the solution of a minimization problem under a constraint that a stream of the candidates should be equal to a constellation point. Finally, the detected signal is selected from hard decisions of both the MMSE detection result and unquantized signal candidates on the basis of the log likelihood function. For reducing the complexity of this process, the proposed algorithm decreases the number of calculations of the log likelihood functions for the quantized signal candidates. Computer simulations under a correlated MIMO channel condition demonstrate that the proposed scheme provides an excellent trade-off between BER performance and complexity, and that it is superior to conventional one-dimensional search algorithms in BER performance while requiring less complexity than the conventional algorithms.

In this study, a computationally efficient ranging scheme exploiting a minimum mean square error (MMSE) and a matrix-pencil (MP) technique is proposed for the IEEE 802.15.4a chirp spread spectrum (CSS) system. Based on the characteristics of the CSS signal, a practical methodology for the MMSE process is devised and the appropriate values of parameters, which are cutoff bandwidth, number of samples and sampling duration in frequency domain, are investigated and numerically determined to enhance the performance. The performance of proposed scheme is analyzed in terms of the computational complexity and the ranging estimation errors. Simulation results demonstrate that the proposed scheme performs as well as the conventional scheme at remarkably reduced computational costs.

This paper proposes a low complexity noise suppressor with hybrid filterbanks and adaptive time-frequency tiling. An analysis hybrid filterbank provides efficient transformation by further decomposing low-frequency bins after a coarse transformation with a short frame size. A synthesis hybrid filterbank also reduces computational complexity in a similar fashion to the analysis hybrid filterbank. Adaptive time-frequency tiling reduces the number of spectral gain calculations. It adaptively generates tiling information in the time-frequency plane based on the signal characteristics. The average number of instructions on a typical DSP chip has been reduced by 30% to 7.5 MIPS in case of mono signals sampled at 44.1 kHz. A Subjective test result shows that the sound quality of the proposed method is comparable to that of the conventional one.