In this paper, we propose an efficient regularized zero-forcing (RZF) precoding method that has lower hardware resource requirements and produces a shorter delay to the first transmitted symbol compared with truncated polynomial expansion (TPE) that is based on Neumann series in massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme, named matrix decomposition-polynomial expansion (MDPE), essentially applies a matrix decomposition algorithm based on polynomial expansion to significantly reduce full matrix multiplication computational complexity. Accordingly, it is suitable for real-time hardware implementations and high-mobility scenarios. Furthermore, the proposed method provides a simple expression that links the optimization coefficients to the ratio of BS/UTs antennas (β). This approach can speed-up the convergence to the matrix inverse by a matrix polynomial with small terms and further reduce computation costs. Simulation results show that the MDPE scheme can rapidly approximate the performance of the full precision RZF and optimal TPE algorithm, while adaptively selecting matrix polynomial terms in accordance with the different β and SNR situations. It thereby obtains a high average achievable rate of the UTs under power allocation.

We introduce a new type of Montgomery-like square root formulae in GF(2m) defined by an arbitrary irreducible trinomial, which is more efficient compared with classic square root operation. By choosing proper Montgomery factors for different kind of trinomials, the space and time complexities of such square root computations match or outperform the best results. A practical application of the Montgomery-like square root in inversion computation is also presented.

Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.

In general, a time-limited signal such as a single sinusoidal waveform framed by a frame period T can be utilized for conveying a multi-level symbol in data transmission. If such a signal is analyzed by the conventional DFT (Discrete Fourier Transform) analysis, the infinite number of frequency components with frequency spacing fD = T1 is needed. This limits the accuracy with which the original frequency of the unframed sinusoidal waverform can be identified. It is especially difficult to identify two similar framed sinusoids whose frequency spacing is narrower than fD. An analytical principle for time-limited signals is therefore proposed by introducing the concept of an Extended Frame into DFT. Waveform analysis more accurate than DFT is achieved by taking into account multiple correlations between extended frames made of an input frame signal and the element frequency components corresponding to the length of each extended frame. In this approach, it is possible to use arbitrary element frequency spacing less than fD. It also allows an element frequency to be selected as a real number times of fD, rather than as an integer times of fD that is used for DFT. With this analyzing mechanism, it is verified that an input frame signal with only the frequency components which coincide with any of the element frequencies can be exactly analyzed. The disturbance caused by the input white noise is examined. As a result, it is found that the superior noise suppression function is achieved by this method over a conventional matched filter. In addition, the error caused by using a finite number of element frequencies and the A/D conversion accuracy required for sampling an input signal are examined, and it is shown that these factors need not impede practical implementation. For this reason, this principle is useful for multi-ary transmission systems, noise tolerant receivers, or systems requiring precise filtering of time limited waveforms.

For mobile video codecs, the huge energy dissipation for external memory traffic is a critical challenge under the battery power constraint. Lossy embedded compression (EC), as a solution to this challenge, is considered in this paper. While previous studies in lossy EC mostly focused on algorithm optimization to reduce distortion, this work, to the best of our knowledge, is the first one that addresses the distortion control. Firstly, from both theoretical analysis and experiments for distortion optimization, a conclusion is drawn that, at the frame level, allocating memory traffic evenly is a reliable approximation to the optimal solution to minimize quality loss. Then, to reduce the complexity of decoding twice, the distortion between two sequences is estimated by a linear function of that calculated within one sequence. Finally, on the basis of even allocation, the distortion control is proposed to determine the amount of memory traffic according to a given distortion limitation. With the adaptive target setting and estimating function updating in each group of pictures (GOP), the scene change in video stream is supported without adding a detector or retraining process. From experimental results, the proposed distortion control is able to accurately fix the quality loss to the target. Compared to the baseline of negative feedback on non-referred B frames, it achieves about twice memory traffic reduction.

This paper presents a measurement-domain intra prediction coding framework that is compatible with compressive sensing (CS)-based image sensors. In this framework, we propose a low-complexity intra prediction algorithm that can be directly applied to measurements captured by the image sensor. We proposed a structural random 0/1 measurement matrix, embedding the block boundary information that can be extracted from the measurements for intra prediction. Furthermore, a low-cost Very Large Scale Integration (VLSI) architecture is implemented for the proposed framework, by substituting the matrix multiplication with shared adders and shifters. The experimental results show that our proposed framework can compress the measurements and increase coding efficiency, with 34.9% BD-rate reduction compared to the direct output of CS-based sensors. The VLSI architecture of the proposed framework is 9.1 Kin area, and achieves the 83% reduction in size of memory bandwidth and storage for the line buffer. This could significantly reduce both the energy consumption and bandwidth in communication of wireless camera systems, which are expected to be massively deployed in the Internet of Things (IoT) era.

In this paper, we propose a low-complexity widely-linear minimum mean square error (WL-MMSE) signal detection based on the Chebyshev polynomials accelerated symmetric successive over relaxation (SSORcheb) algorithm for uplink (UL) over-loaded large-scale multiple-input multiple-output (MIMO) systems. The technique of utilizing Chebyshev acceleration not only speeds up the convergence rate significantly, and maximizes the data throughput, but also reduces the cost. By utilizing the random matrix theory, we present good estimates for the Chebyshev acceleration parameters of the proposed signal detection in real large-scale MIMO systems. Simulation results demonstrate that the new WL-SSORcheb-MMSE detection not only outperforms the recently proposed linear iterative detection, and the optimal polynomial expansion (PE) WL-MMSE detection, but also achieves a performance close to the exact WL-MMSE detection. Additionally, the proposed detection offers superior sum rate and bit error rate (BER) performance compared to the precision MMSE detection with substantially fewer arithmetic operations in a short coherence time. Therefore, the proposed detection can satisfy the high-density and high-mobility requirements of some of the emerging wireless networks, such as, the high-mobility Internet of Things (IoT) networks.

Compressed sensing (CS)-based wideband spectrum sensing has been a hot topic because it can cut high signal acquisition costs. However, using CS-based approaches, the spectral recovery requires large computational complexity. This letter proposes a wideband spectrum sensing algorithm based on multirate coprime sampling. It can detect the entire wideband directly from sub-Nyquist samples without spectral recovery, thus it brings a significant reduction of computational complexity. Compared with the excellent spectral recovery algorithm, i.e., orthogonal matching pursuit, our algorithm can maintain good sensing performance with computational complexity being several orders of magnitude lower.