亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

This paper studies a secrecy integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) aims to send confidential messages to a single-antenna communication user (CU), and at the same time sense several targets that may be suspicious eavesdroppers. To ensure the sensing quality while preventing the eavesdropping, we consider that the BS sends dedicated sensing signals (in addition to confidential information signals) that play a dual role of artificial noise (AN) for confusing the eavesdropping targets. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS, to minimize the matching error between the transmit beampattern and a desired beampattern for sensing, subject to the minimum secrecy rate requirement at the CU and the transmit power constraint at the BS. Although the formulated problem is non-convex, we propose an algorithm to obtain the globally optimal solution by using the semidefinite relaxation (SDR) together with a one-dimensional (1D) search. Next, to avoid the high complexity induced by the 1D search, we also present two sub-optimal solutions based on zero-forcing and separate beamforming designs, respectively. Numerical results show that the proposed designs properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, thus achieving desirable transmit beampattern for sensing while ensuring the CU's secrecy rate.

This paper proposes a scalable channel estimation and reflection optimization framework for reconfigurable intelligent surface (RIS)-enhanced orthogonal frequency division multiplexing (OFDM) systems. Specifically, the proposed scheme firstly generates a training set of RIS reflection coefficient vectors offline. For each RIS reflection coefficient vector in the training set, the proposed scheme estimates only the end-to-end composite channel and then performs the transmit power allocation. As a result, the RIS reflection optimization is simplified by searching for the optimal reflection coefficient vector maximizing the achievable rate from the pre-designed training set. The proposed scheme is capable of flexibly adjusting the training overhead according to the given channel coherence time, which is in sharp contrast to the conventional counterparts. Moreover, we discuss the computational complexity of the proposed scheme and analyze the theoretical scaling law of the achievable rate versus the number of training slots. Finally, simulation results demonstrate that the proposed scheme is superior to existing approaches in terms of decreasing training overhead, reducing complexity as well as improving rate performance in the presence of channel estimation errors.

Integrated sensing and communication (ISAC) emerges as a new design paradigm that combines both sensing and communication systems to jointly utilize their resources and to pursue mutual benefits for future B5G and 6G networks. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a more general point-to-point ISAC model is proposed to account for the scenarios that the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach.

The sparsity of multipaths in the wideband channel has motivated the use of compressed sensing for channel estimation. In this letter, we propose a different approach to sparse channel estimation. We exploit the fact that $L$ taps of channel impulse response in time domain constitute a non-orthogonal superposition of $L$ geometric sequences in frequency domain. This converts the channel estimation problem into the extraction of the parameters of geometric sequences. Numerical results show that the proposed scheme is superior to existing algorithms in high signal-to-noise ratio (SNR) and large bandwidth conditions.

In this thesis, we investigate the problem of efficient data detection in large MIMO and high order MU-MIMO systems. First, near-optimal low-complexity detection algorithms are proposed for regular MIMO systems. Then, a family of low-complexity hard-output and soft-output detection schemes based on channel matrix puncturing targeted for large MIMO systems is proposed. The performance of these schemes is characterized and analyzed mathematically, and bounds on capacity, diversity gain, and probability of bit error are derived. After that, efficient high order MU-MIMO detectors are proposed, based on joint modulation classification and subspace detection, where the modulation type of the interferer is estimated, while multiple decoupled streams are individually detected. Hardware architectures are designed for the proposed algorithms, and the promised gains are verified via simulations. Finally, we map the studied search-based detection schemes to low-resolution precoding at the transmitter side in massive MIMO and report the performance-complexity tradeoffs.

We consider resultant-based methods for elimination of indeterminates of Ore polynomial systems in Ore algebra. We start with defining the concept of resultant for bivariate Ore polynomials then compute it by the Dieudonne determinant of the polynomial coefficients. Additionally, we apply noncommutative versions of evaluation and interpolation techniques to the computation process to improve the efficiency of the method. The implementation of the algorithms will be performed in Maple to evaluate the performance of the approaches.

Massive grant-free multiple-access is a valuable research topic for next generation multiple-access, since it significantly reduces the control signaling overhead and transmission latency. This paper constructs a novel uniquely-decodable multi-amplitude sequence (UDAS) set for grant-free multiple-access systems, which can provide high spectrum efficiency (SE) without additional redundancy and realize low-complexity active user detection (AUD). We firstly propose an UDAS-based multi-dimensional bit interleaving coded modulation (MD-BICM) transmitter. Then, this paper presents the detailed definition of UDAS, and provides three conditions for constructing a UDAS set. Following, two kinds of UDAS sets are constructed based on cyclic and quasi-cyclic matrix modes; and some important features of the cyclic/quasi-cyclic UDAS sets are deduced. Besides, we present a statistic of UDAS feature based AUD algorithm (SoF-AUD), and a joint multiuser detection and improved message passing iterative decoding algorithm for the proposed system. Finally, the active user error rate (AUER) and Shannon limits of the proposed system are deduced in details. Simulation results show that the AUER of our proposed system can reach an extremely low value $10^{-6}$, when $E_b/N_0$ is 0 dB and the length of transmit block is larger than a given value (e.g., 576). Meanwhile, the SE of our proposed system can compare with the designed non-orthogonal multiple-access (NOMA) codebooks, verifying the valid and flexible.

Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have explored model pruning to obtain lightweight and efficient networks. However, most existing pruning approaches are driven by empirical heuristics and rarely consider the joint impact of channels, leading to unguaranteed and suboptimal performance. In this paper, we propose a novel channel pruning method via class-aware trace ratio optimization (CATRO) to reduce the computational burden and accelerate the model inference. Utilizing class information from a few samples, CATRO measures the joint impact of multiple channels by feature space discriminations and consolidates the layer-wise impact of preserved channels. By formulating channel pruning as a submodular set function maximization problem, CATRO solves it efficiently via a two-stage greedy iterative optimization procedure. More importantly, we present theoretical justifications on convergence and performance of CATRO. Experimental results demonstrate that CATRO achieves higher accuracy with similar computation cost or lower computation cost with similar accuracy than other state-of-the-art channel pruning algorithms. In addition, because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.

Full-Duplex (FD) communication can revolutionize wireless communications as it doubles the spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) multi-user FD system for weighted sum-rate (WSR) maximization. All the devices are assumed to have a limited dynamic range (LDR), and we adopt an impairment-aware HYBF approach. We also present a novel interference and self-interference (SI) power allocation scheme to include the optimal power allocation. The analog processing stage is assumed to be quantized, and we consider both the unit-modulus and unconstrained cases. Compared to the traditional designs, the proposed design considers the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional LDR noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. We investigate the maximum achievable gain of a hybrid multi-user FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains. Simulation results show that our HYBF scheme can significantly outperform the fully digital HD systems with only a few RF chains. We also show that amplitude manipulation at the analog stage can improve the performance when the number of RF chains is small.

We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of $\widetilde{\mathcal{O}}(1/t^2)$. This contrasts with a rate of $\mathcal{O}(1/\log(t))$ for standard gradient descent, and $\mathcal{O}(1/t)$ for normalized gradient descent. This momentum-based method is derived via the convex dual of the maximum-margin problem, and specifically by applying Nesterov acceleration to this dual, which manages to result in a simple and intuitive method in the primal. This dual view can also be used to derive a stochastic variant, which performs adaptive non-uniform sampling via the dual variables.