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Register a new userAdaptive Reduced-Rank Processing Using a Projection Operator Based on Joint Iterative Optimization of Adaptive Filters For CDMA Interference Suppression
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Added by
Rodrigo de Lamare
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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This paper proposes a novel adaptive reduced-rank filtering scheme based on the joint iterative optimization of adaptive filters. The proposed scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that constitutes the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and simple least-mean squares (LMS) adaptive algorithms for its computationally efficient implementation. Simulation results for a CDMA interference suppression application reveals that the proposed scheme significantly outperforms the state-of-the-art reduced-rank schemes, while requiring a significantly lower computational complexity.
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This letter studies a cellular-connected unmanned aerial vehicle (UAV) scenario, in which a UAV user communicates with ground base stations (GBSs) in cellular uplink by sharing the spectrum with ground users (GUs). To deal with the severe air-to-ground (A2G) co-channel interference, we consider an adaptive interference cancellation (IC) approach, in which each GBS can decode the GUs messages by adaptively switching between the modes of IC (i.e., precanceling the UAVs resultant interference) and treating interference as noise (TIN). By designing the GBSs decoding modes, jointly with the wireless resource allocation and the UAVs trajectory control, we maximize the UAVs data-rate throughput over a finite mission period, while ensuring the minimum data-rate requirements at individual GUs. We propose an efficient algorithm to solve the throughput maximization problem by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that our proposed design significantly improves the UAVs throughput as compared to the benchmark schemes without the adaptive IC and/or trajectory optimization.
Recently, the spatial modulation (SM) technique has been proposed for visible light communication (VLC). This paper investigates the average symbol error rate (SER) for the VLC using adaptive spatial modulation (ASM). In the system, the analysis of the average SER is divided into two aspects: the error probability of the spatial domain and the error probability of the signal domain when the spatial domain is correctly estimated. Based on the two aspects, the theoretical expression of the average SER is derived. To further improve the system performance, an optimization problem is proposed to optimize the modulation orders on the LEDs. The ASM based and the candidate reduction (CR)-ASM based optimization algorithms are proposed to solve the problem, respectively. Numerical results show that the derived theoretical values of the average SER are quite accurate to evaluate the system performance. Moreover, compared with the existing schemes, the proposed two algorithms are better choices for VLC.
In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction is composed of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques.
In this paper, we propose a new algorithm for recovery of low-rank matrices from compressed linear measurements. The underlying idea of this algorithm is to closely approximate the rank function with a smooth function of singular values, and then minimize the resulting approximation subject to the linear constraints. The accuracy of the approximation is controlled via a scaling parameter $delta$, where a smaller $delta$ corresponds to a more accurate fitting. The consequent optimization problem for any finite $delta$ is nonconvex. Therefore, in order to decrease the risk of ending up in local minima, a series of optimizations is performed, starting with optimizing a rough approximation (a large $delta$) and followed by successively optimizing finer approximations of the rank with smaller $delta$s. To solve the optimization problem for any $delta > 0$, it is converted to a new program in which the cost is a function of two auxiliary positive semidefinete variables. The paper shows that this new program is concave and applies a majorize-minimize technique to solve it which, in turn, leads to a few convex optimization iterations. This optimization scheme is also equivalent to a reweighted Nuclear Norm Minimization (NNM), where weighting update depends on the used approximating function. For any $delta > 0$, we derive a necessary and sufficient condition for the exact recovery which are weaker than those corresponding to NNM. On the numerical side, the proposed algorithm is compared to NNM and a reweighted NNM in solving affine rank minimization and matrix completion problems showing its considerable and consistent superiority in terms of success rate, especially, when the number of measurements decreases toward the lower-bound for the unique representation.
The performance of millimeter wave (mmWave) communications critically depends on the accuracy of beamforming both at base station (BS) and user terminals (UEs) due to high isotropic path-loss and channel attenuation. In high mobility environments, accurate beam alignment becomes even more challenging as the angles of the BS and each UE must be tracked reliably and continuously. In this work, focusing on the beamforming at the BS, we propose an adaptive method based on Recurrent Neural Networks (RNN) that tracks and predicts the Angle of Departure (AoD) of a given UE. Moreover, we propose a modified frame structure to reduce beam alignment overhead and hence increase the communication rate. Our numerical experiments in a highly non-linear mobility scenario show that our proposed method is able to track the AoD accurately and achieve higher communication rate compared to more traditional methods such as the particle filter.
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