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A variable-phase-shifter (VPS) architecture with hybrid precoding for mixture use of phase shifters and switches, is proposed for millimeter wave massive multiple-input multiple-output communications. For the VPS architecture, a hybrid precoding design (HPD) scheme, called VPS-HPD, is proposed to optimize the phases according to the channel state information by alternately optimizing the analog precoder and digital precoder. To reduce the computational complexity of the VPS-HPD scheme, a low-complexity HPD scheme for the VPS architecture (VPS-LC-HPD) including alternating optimization in three stages is then proposed, where each stage has a closed-form solution and can be efficiently implemented. To reduce the hardware complexity introduced by the large number of switches, we consider a group-connected VPS architecture and propose a HPD scheme, where the HPD problem is divided into multiple independent subproblems with each subproblem flexibly solved by the VPS-HPD or VPS-LC-HPD scheme. Simulation results verify the effectiveness of the propose schemes and show that the proposed schemes can achieve satisfactory spectral efficiency performance with reduced computational complexity or hardware complexity. [ABSTRACT FROM AUTHOR]

In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively. Then based on the designed transmit beam, we determine the analog beamformer and digital beamformer subject to the constant envelop constraint of phase shifter network in mmWave MIMO, still using the alternating minimization method. Simulation results show that under the same SINR constraint of communication users, larger antenna array can achieve better radar beam quality. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is an important aspect of satellite communications. Different with the existing algorithms that typically use the first-order Taylor lower bound approximation to convert non-convex EE maximization (EEM) problems into convex ones, in this letter a two-step quadratic transformation method is presented. In the first step, the fractional form of the achievable rate over the total power consumption is converted into a non-fractional form based on quadratic transformation. In the second step, the fractional form of the signal power over the interference-and-noise power is further converted into a non-fractional form, still based on quadratic transformation. After the two-step quadratic transformation, the original EEM problem is converted into an equivalent convex one. Then an alternating optimization algorithm is presented to solve it by iteratively performing two stages until a stop condition is satisfied. Simulation results show that the presented algorithm can fast converge and its performance is better than that of the sequential convex approximation algorithm and the multibeam interference mitigation algorithm. [ABSTRACT FROM AUTHOR]

In this letter, we investigate time-domain channel estimation for wideband millimeter wave (mmWave) MIMO OFDM system. By transmitting frequency-domain pilot symbols as well as different beamforming vectors, we observe that the time-domain mmWave MIMO channels exhibit channel delay sparsity and especially block sparsity among different spatial directions. Then we propose a time-domain channel estimation exploiting block sparsity (TDCEBS) scheme, which always aims at finding the best nonzero block achieving the largest projection of the residue at each iterations. In particular, we evaluate the system performance using the QuaDRiGa which is recommended by 5G New Radio to generate wideband mmWave MIMO channels. The effectiveness of the proposed TDCEBS scheme is verified by the simulation results, as the proposed scheme outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

Hydrothermal liquefaction (HTL) of biomass used HTL reaction under high temperature and pressure to produce bio-oil. This technology is considered as one of the most promising converting technology of biomass to biofuels. This paper summarized current research developments of HTL for bio-oil and analyzed its reaction mechanism and influencing factors based on bibliometric analysis. The results showed that reaction conditions and catalyst have been still global researching focuses about HTL. Compared with homogeneous catalysts, the study of HTL by using heterogeneous catalyst developed more quickly. With promotion of resource recovering, food waste, sludge, and other organic waste can also be used as raw materials for HTL for bio-oil now. [ABSTRACT FROM AUTHOR]

In this article, multiuser beam training based on hierarchical codebook for millimeter wave massive multi-input multi-output is investigated, where the base station (BS) simultaneously performs beam training with multiple user equipments (UEs). For the UEs, an alternative minimization method with a closed-form expression (AMCF) is proposed to design the hierarchical codebook under the constant modulus constraint. To speed up the convergence of the AMCF, an initialization method based on Zadoff-Chu sequence is proposed. For the BS, a simultaneous multiuser beam training scheme based on an adaptively designed hierarchical codebook is proposed, where the codewords in the current layer of the codebook are designed according to the beam training results of the previous layer. The codewords at the BS are designed with multiple mainlobes, each covering a spatial region for one or more UEs. Simulation results verify the effectiveness of the proposed hierarchical codebook design schemes and show that the proposed multiuser beam training scheme can approach the performance of the beam sweeping but with significantly reduced beam training overhead. [ABSTRACT FROM AUTHOR]

In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance. [ABSTRACT FROM AUTHOR]

Different from the existing works, in this paper we investigate the pilot allocation in multi-cell massive multiple-input multiple-output (MIMO) systems, aiming to maximize the spectral efficiency (SE) subject to the constraint that the number of user equipments (UEs) satisfying the quality of service (QoS) is maximized. Since the considered bilevel optimization problem that is tough to handle, we resort to the greedy algorithms with low computational complexity. We propose a QoS constrained (QC) pilot allocation scheme, which sequentially allocates the pilot sequence having large interference power to the UE with small signal power so that the number of UEs satisfying QoS can be maximized and then the SE can also be maximized. Simulation results show that the propose QC scheme outperforms the conventional scheme and smart pilot allocation (SPA) scheme. [ABSTRACT FROM AUTHOR]

Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multi-output system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector. Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design method has better spectral efficiency performance than other methods with low phase shifter resolution. [ABSTRACT FROM AUTHOR]

In this paper, a two-step codeword design approach for millimeter wave (mmWave) massive MIMO systems is presented. Ideal codewords are first designed, which ignores the hardware constraints in terms of phase shifter resolution and the number of RF chains. Based on the ideal codewords, practical codewords are then obtained taking the hardware constraints into consideration. For the ideal codeword design in the first step, additional phase is introduced to the beam gain to provide extra degree of freedom. We develop a phase-shifted ideal codeword design (PS-ICD) method, which is based on alternative minimization with each iteration having a closed-form solution and can be extended to design more general beamforming vectors with different beam patterns. Once the ideal codewords are obtained in the first step, the practical codeword design problem in the second step is to approach the ideal codewords by considering the hardware constraints of the hybrid precoding structure in terms of phase shifter resolution and the number of RF chains. We propose a fast search based alternative minimization (FS-AltMin) algorithm that alternatively designs the analog precoder and digital precoder. Simulation results verify the effectiveness of the proposed methods and show that the codewords designed based on the two-step approach outperform those designed by the existing approaches. [ABSTRACT FROM AUTHOR]

The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications. In this article, we provide an overview on CSI acquisition, including beam training and channel estimation for mmWave massive multiple-input multiple-output systems. The beam training can avoid the estimation of a high-dimension channel matrix, while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead. [ABSTRACT FROM AUTHOR]

Aiming at maximizing the achievable sum rate of wideband multiuser mmWave massive MIMO systems, hybrid precoding is studied. Since each computation of the achievable sum rate can be performed only after the analog precoder and the digital precoder are both determined, the maximization of the achievable sum rate has intractable computational complexity. By introducing the interference free (IF) achievable sum rate, the design of the analog and digital precoders can be decoupled. To avoid the beam conflict and maximize the IF achievable sum rate, a Hungarian-based codeword selection algorithm is proposed for the analog precoding design. The simulation results verify the effectiveness of the proposed scheme and show that a better performance can be achieved compared with the existing schemes. [ABSTRACT FROM AUTHOR]

We investigate beam training and allocation for multiuser millimeter wave massive MIMO systems. An orthogonal pilot-based beam training scheme is first developed to reduce the number of training times, where all users can simultaneously perform the beam training with the base station (BS). As the number of users increase, the same beam from the BS may point to different users, leading to beam conflict and multiuser interference. Therefore, a quality-of-service (QoS) constrained (QC) beam allocation scheme is proposed to maximize the equivalent channel gain of the QoS-satisfied users, under the premise that the number of the QoS-satisfied users without beam conflict is maximized. To reduce the overhead of beam training, two partial beam training schemes, an interlaced scanning (IS)-, and a selection probability (SP)-based schemes, are proposed. The overhead of beam training for the IS-based scheme can be reduced by nearly half, while the overhead for the SP-based scheme is flexible. The simulation results show that the QC-based beam allocation scheme can effectively mitigate the interference caused by the beam conflict and significantly improve the spectral efficiency, while the IS-based and SP-based schemes significantly reduce the overhead of beam training at the cost of sacrificing spectral efficiency, a little. [ABSTRACT FROM AUTHOR]

Beam training based on hierarchical codebook for millimeter wave (mmWave) massive MIMO is investigated. Unlike the existing work using the same hierarchical codebook to estimate different multi-path components (MPCs), dynamic hierarchical codebooks which are updated according to the estimated MPCs are adopted. First, a generalized hierarchical codebook design method is proposed. Then based on this method, a beam training method which dynamically updates the hierarchical codebook by removing the contribution of the estimated MPCs from the codebook is proposed. Simulation results verify the effectiveness of our method and show that the proposed method outperforms the existing ones in terms of the success detection rate of beam training. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the downlink transmission for cache-enabled fog radio access networks aiming at maximizing the delivery rate under the constraints of fronthaul capacity, maximum transmit power, and size of files. To reduce the delivery latency and the burden on fronthaul links and make full use of the local cache and baseband signal processing capabilities of enhanced remote radio heads (eRRHs), a two-level transmission scheme including cache-level and network-level transmission is proposed. In cache-level transmission, only requested files cached at the local cache are transmitted to the corresponding users. The duration of cache-level transmission is the delay caused by the transfer between the baseband unit (BBU) and eRRHs as well as the signal processing at the BBU. The remaining requested files are jointly transmitted to the corresponding users at network-level transmission. For cache-level transmission, a centralized optimization algorithm is first presented and then a decentralized optimization algorithm is provided to avoid the exchange of signaling among eRRHs. Meanwhile, another centralized optimization algorithm is presented to tackle the optimization problem for network-level transmission. All presented algorithms are proved to converge to the Karush–Kuhn–Tucker solutions of the problems. Numerical results are provided to validate the effectiveness of the proposed transmission scheme as well as evaluating the system performance. [ABSTRACT FROM AUTHOR]

The temporal correlation of target can be exploited to improve the radar estimation performance. This paper studies the estimation of target scattering coefficients in an adaptive radar system, and a novel estimation method based on Kalman filter (KF) with waveform optimization is proposed for the temporally correlated target in the scenario with both noise and clutter. Different from the existing indirect methods, a direct optimization method is proposed to design the transmitted waveform and minimize the mean square error of the KF estimation. Additionally, the waveform is optimized subject to the practical constraints including the transmitted energy, the peak-to-average power ratio, and the target detection performance. With clutter and noise, the waveform optimization problem is non-convex. Therefore, a novel two-step method is proposed and converts the original non-convex problem into several semidefinite programming problems, which are convex and can solve efficiently. Simulation results demonstrate that the proposed KF-based method with waveform optimization can outperform state-of-art methods and significantly improve the estimation performance. [ABSTRACT FROM AUTHOR]

In this paper, a framework of beamspace channel estimation in millimeter wave massive MIMO system is proposed. The framework includes the design of hybrid precoding and combining matrix as well as the search method for the largest entry of over-sampled beamspace receiving matrix. Then based on the framework, three channel estimation schemes including identity matrix approximation (IA) based scheme, scattered zero off-diagonal (SZO) based scheme and concentrated zero off-diagonal (CZO) based scheme are proposed. These schemes together with the existing channel estimation schemes are compared in terms of computational complexity, estimation error and total time slots for channel training. Simulation results show that the proposed schemes outperform the existing schemes and can approach the performance of the ideal case. In particular, total time slots for channel training can be substantially reduced. [ABSTRACT FROM AUTHOR]

Instead of merely improving the spectral efficiency, improving the energy efficiency (EE) is another important concern for multibeam satellite systems, due to the power constraint of satellites. However, so far there has been no detailed work on the precoding design concerning the EE for multibeam satellite. In this letter, the EE maximization problem is investigated for multibeam satellite systems under the total power constraint as well as the quality of service constraints. Precoding design algorithms based on zero-forcing (ZF) and sequential convex approximation (SCA) are presented, respectively. In particular, these algorithms are verified by the real measured channel data of multibeam satellite systems. Numerical results show that the precoding algorithm based on SCA outperforms that based on ZF. It is also implied that the EE cannot be always improved by solely increasing the power of the satellite, while reducing the satellite operation power is an effective way for the EE improvement. [ABSTRACT FROM AUTHOR]

Channel estimation for 3-D lens millimeter wave massive MIMO system is investigated. First, the structure of beamspace channel is analyzed to show that the dominant entries of beamspace channel matrix form a dual crossing (DC) shape. Then, a DC-based channel estimation algorithm is proposed to iteratively refine the selection of dominant entries until the stop condition is met. Simulation results show that the DC-based algorithm outperforms the existing algorithms, including orthogonal matching pursuit and adaptive support detection (ASD). At signal-to-noise ratio of 15 dB, the DC-based algorithm has 57.7% improvement in terms of normalized mean squared error compared with ASD while the computational complexity is only half of ASD due to fast computation of least squares estimation. [ABSTRACT FROM PUBLISHER]

The analog beamforming and analog combining in millimeter-wave (mm-Wave) massive MIMO communications are investigated. Based on a hierarchical codebook, the weighted sum-rate maximization is studied by jointly considering the duration for channel training and the receiving signal-to-noise ratio (SNR). An algorithm employing exhaustive search at the starting level of the codebook and the multi-sectional search at the other levels is proposed. At each iteration, the channel gain of the dominant path is estimated and the level of the codebook achieving the weighted sum-rate maximization is predicted by the receiver. Then the predicted level is fed back to the transmitter. If the predicted level is reached by the algorithm, the transmitter begins the data transmission with the obtained analog beamformer and the receiver uses the obtained analog combiner for data receiving. Simulation results show that the proposed algorithm outperforms multi-sectional search, where the former can achieve the weighted sum-rate almost twice of the latter when each block is consisted of 35 symbols and the channel SNR is 20 dB. [ABSTRACT FROM PUBLISHER]

In this paper, the ranges and velocities of multiple extended targets are estimated by exploiting the target sparsity in the cognitive radar system. Different from the point targets in the traditional compressed sensing (CS) radar, the parameters of extended targets are expressed and estimated by using a novel CS-based model. Since the echo signals from extended targets are the convolutions between the transmitted waveform and target impulse responses (TIRs), the dictionary matrices in the proposed cognitive radar for all extended targets must be first established in the CS-based reconstruction algorithm. Then, the target parameters are estimated by reconstructing the nonzero entries of a sparse vector. To further improve the performance of CS reconst-ruction, a novel two-step method is proposed to minimize the mutual coherence of the dictionary matrix by optimizing the transmitted waveform. Simulation results demonstrate that the estimation performance of the extended targets is significantly improved by optimizing the transmitted waveform. [ABSTRACT FROM PUBLISHER]

Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood detector has the high complexity, whereas state-of-the-art low-complexity detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM signals, we propose a low-complexity signal detector based on structured compressive sensing (SCS) to improve the signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM signals by leveraging the structured sparsity. In addition, we also propose the SM signal interleaving to permute SM signals in the same transmission group, whereby the channel diversity can be exploited to further improve signal detection performance. Theoretical analysis quantifies the gain from SM signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Apart from spectral efficiency (SE), energy efficiency (EE) has become an important metric for future wireless communication systems. However, how to achieve balance between SE and EE for a multicell multiuser coordinated beamforming system remains an open problem. In this paper, we propose a new paradigm for EE–SE balancing, namely, using a new metric called resource efficiency (RE) as the objective function to achieve a reasonable tradeoff between SE and EE. The considered optimization problem is more complex than traditional problems and, in general, is NP-hard for finding the globally optimal solution. To obtain insight into the problem, we first equivalently transform the original problem into a parameterized subtractive form optimization problem with some auxiliary variables by using the fraction programming theory. Then, a successive convex approximation method and the classical second-order cone programming method are jointly used to solve the equivalent problem. Finally, an effective algorithm with proven convergence is proposed to obtain the solution to the considered RE optimization. In addition, the algorithm is further extended to consider imperfect channel state information (CSI) using the worst-case design. Extensive numerical simulations are given to validate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

BACKGROUND: As a special residue containing zinc and manganese, spent Zn-Mn batteries cause a serious concern due to their toxicity, abundance and permanence in the environment, and biotechnological recovery of Zn and Mn is one method of recycling thiswaste. In this study identification of the optimum values of the effective parameters in biotechnological processes was investigated using response surfacemethodology. RESULTS: The released dose of Zn andMnwas highly dependent on many parameters. Non-linear equationswere formulated to describe the relationship between bioleaching efficiency of Zn andMn and four important parameters. The optimum parameter values were determined as follows: dose of mixed energy substrates, exogenous-acid pH adjustment, incubating temperature and pulp density of 28 g L-1, 1.9, 33 °C, 9.7%, respectively, for Zn release and 29 g L-1, 1.8, 36.7 °C, 8%, respectively, for Mn mobilization. Themaximum predictive extraction efficiency of Zn andMn was 52.5% and 52.4%, respectively. CONCLUSION: The maximum efficiency of extraction of both Zn andMn reached 50% under optimum conditions after 9 days of bioleaching. Additionally, XRD analyses suggested that Zn andMn existed mainly as Hetaerolite (ZnMn2O4) in the spent Zn-Mn batteries, which gradually disappeared and released Zn2+ and Mn2+ into solution during bioleaching process. [ABSTRACT FROM AUTHOR]

This work investigates the estimation of target scattering coefficients (TSC) in cognitive radar systems with temporally correlated targets. An estimation method based on Kalman filtering (KF) is proposed to exploit the temporal TSC correlation between the pulses in the frequency domain. To minimize the mean square error of the estimated TSC at each KF iteration, unlike existing indirect methods, in this paper the radar waveform is optimized directly under the constraints of transmitted power, peak-to-average power ratio (PAPR) and detection probability. Since the optimization problem regarding the waveform design is non-convex, a novel method is proposed to convert this problem into a convex one. Simulation results demonstrate that the performance of the TSC estimation for the temporally correlated target is significantly improved by radar waveform optimization. Meanwhile, no performance degradation is observed with the introduction of the additional PAPR constraints and the detection constraints for KF estimation with the optimized waveform. [ABSTRACT FROM AUTHOR]

Existing works design the pilot pattern for sparse channel estimation, assuming that the power of all pilots is equal. However, equal power allocation is not optimal in cognitive radio (CR) systems. In this correspondence, we jointly design the pilot power and pilot pattern for sparse channel estimation in orthogonal-frequency-division-multiplexing-based CR systems, based on the rule of mutual incoherence property that minimizes the coherence of the measurement matrix used for the sparse recovery. Under the sum power constraint and peak power constraint, the pilot design is formulated as a joint optimization problem, which is then decoupled into tractable sequential formations. Given a pilot pattern, we formulate the design of pilot power as a second-order cone programming. Then, we propose a joint design algorithm, which includes discrete optimization for pilot pattern and continuous optimization for pilot power. Simulation results show that the proposed algorithm can achieve better channel estimation performance in terms of mean square error and bit error rate and can further improve the spectrum efficiency by 2.4%, compared with existing algorithms assuming equal pilot power. [ABSTRACT FROM AUTHOR]

In this paper, we consider the pilot design based on the mutual incoherence property (MIP) for sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems. With respect to the length of channel impulse response (CIR), we first derive a sufficient condition for the optimal pilot pattern generated from the cyclic different set (CDS). Since the CDS does not exist for most practical OFDM systems, we propose three pilot design schemes to obtain a near-optimal pilot pattern. The first two schemes, including stochastic sequential search (SSS) and stochastic parallel search (SPS), are based on the stochastic search. The third scheme called iterative group shrinkage (IGS) employs a tree-based searching structure and removes rows in a group instead of removing a single row at each step. We later extend our work to multiple-input–multiple-output (MIMO) systems and propose two schemes, i.e., sequential design scheme and joint design scheme. We also combine them to design the multiple orthogonal pilot patterns, i.e., using the sequential scheme for the first several transmit antennas and using the joint scheme to design the pilot pattern for the remaining transmit antennas. Simulation results show that the proposed SSS, SPS, and IGS converge much faster than the cross-entropy optimization and the exhaustive search and are thus more efficient. Moreover, SSS and SPS outperform IGS in terms of channel estimation performance. [ABSTRACT FROM PUBLISHER]

In this correspondence, sparse channel estimation is first introduced in orthogonal frequency-division multiplexing (OFDM)-based cognitive radio systems. Based on the results of spectrum sensing, the pilot design is studied by minimizing the coherence of the dictionary matrix used for sparse recovery. Then, it is formulated as an optimal column selection problem where a table is generated and the indexes of the selected columns of the table form a pilot pattern. A novel scheme using constrained cross-entropy optimization is proposed to obtain an optimized pilot pattern, where it is modeled as an independent Bernoulli random process. The updating rule for the probability of each active subcarrier selected as a pilot subcarrier is derived. A projection method is proposed so that the number of pilots during the optimization is fixed. Simulation results verify the effectiveness of the proposed scheme and show that it can achieve 11.5% improvement in spectrum efficiency with the same channel estimation performance compared with the least squares (LS) channel estimation. [ABSTRACT FROM PUBLISHER]

ABSTRACT In this paper, concerning underwater acoustic (UWA) communications, we propose a wavelet filter bank system as the extension of orthogonal frequency-division multiplexing system. We exploit the convolutional structure of a UWA channel and formulate the pilot-assisted channel estimation as a sparse recovery problem. Then, we investigate the restricted isometry property of the measurement matrix via eigenvalue analysis and Gershgorin circle theorem. The sparse recovery problem is proven to satisfy the restricted isometry property. Moreover, we also propose a low-complexity complex-field homotopy algorithm for sparse channel estimation, regarding the fact that the channel taps of each path are usually complex valued in practice. Simulation results show that the wavelet filter bank system achieves more accurate UWA channel estimation performance than orthogonal frequency-division multiplexing system under the same conditions of bandwidth, duration, data rate and channel profile. The proposed complex homotopy algorithm outperforms orthogonal matching pursuit (OMP) and stagewise OMP in both systems, whereas its computational complexity is similar to OMP. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

H.264/AVC achieves higher compression efficiency than previous video coding standards. However, the process of selecting the optimal coding mode for each macroblock (MB) results in extremely high computation complexity, which make it difficult for practical use. In this paper, an efficient algorithm is proposed to reduce the complexity of MB mode selection. The proposed algorithm identified the interior region of the motion object by using the motion vectors information firstly. For the interior region surrounded by the identical motion vectors, we skip the mode selection of the MBs and then treat them with large block size modes directly. We also discuss the specific examples in this region. For the boundary region, we classify them into different types according to the coded mode information. After that we process the different regions with different mode set distinctly. Experimental results show that the proposed algorithm can save the encoding time up to 46% on average compared to the conventional method in the JVT JM8.6 reference encoder with only 0.12 dB performance degradation. [ABSTRACT FROM AUTHOR]

The paper presents a kind of transmission system which employs M-ary Position Phase Shift Keying (MPPSK) to send data and Phase Locked Loop (PLL) based techniques for data retrieve. With a single PLL, MPPSK demodulation is achieved, as well as carrier recovery and symbol synchronization. Firstly, MPPSK modulation method is briefly introduced. 2PPSK’s PSD expression is given with its optimization result. Orthogonal Phase Detector (PD) and static threshold are used for the purpose of wider phase range and simplicity in demodulation. The data rate is alterable, which is 4.65 kbps for 2PPSK and 9.3 kbps for 4PPSK in the paper. Then some indicative comparisons in Signal to Noise Ratio Symbol Error Rate (SNR-SER) are made among 2PPSK, 3PPSK and 4PPSK, of which 4PPSK has proved to be optimal in ten slots each symbol conditions. And finally, it is demonstrated by system simulations that lower than 10−4 Symbol Error Rate (SER) performance can be obtained at 13 dB symbol SNR. [ABSTRACT FROM AUTHOR]

Pilot power allocation is investigated under the framework of physical layer secure communications in time‐division duplex systems, where the secret keys are generated from the estimates of sparse wireless channels. The joint optimisation of secret key capacity and sparse channel estimation performance based on pilot power allocation is formulated as a convex optimisation problem. Considering the fairness between these two sides, a scaling factor is introduced. Then, a scheme is proposed to fast solve the problem by looking up a table and using existing optimisation solvers. Simulation results show that a proper scaling factor can make a trade‐off between the secret key capacity and sparse channel estimation performance. [ABSTRACT FROM AUTHOR]

The joint sparsity of uplink channels in massive multi‐input–multi‐output (MIMO) systems is explored and a block sparse model is proposed for joint channel estimation. The block coherence of this model is analysed. It is indicated that as the number of antennas at the base station grows to be infinity, the block coherence will be zero. Then a block optimised orthogonal matching pursuit (BOOMP) algorithm is proposed. Simulation results verify the analysis and show that the joint estimation using the BOOMP algorithm can significantly improve the channel estimation performance. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the deterministic pilot allocation for sparse channel estimation in OFDM systems. Based on the rule of minimizing the coherence of the DFT submatrix, we derive that the pilot design according to the cyclic different set (CDS) is optimal. However, the CDS only exists for some specific number of OFDM subcarriers. For those cases where the CDS is unavailable, we propose a scheme using discrete stochastic approximation to obtain a near-optimal pilot pattern. Simulation results demonstrate that our scheme is much faster convergent and more efficient than the exhaustive search; and it has been shown that substantial improvement for channel estimation can be achieved. [ABSTRACT FROM PUBLISHER]