Statistical conditional optimization criteria lead to the development of an iterative algorithm that starts from the matched filter (or constraint vector) and generates a sequence of filters that converges to the minimum-variance-distortionless-response (MVDR) solution for any positive definite input autocorrelation matrix. Computationally, the algorithm is a simple, noninvasive, recursive procedure that avoids any form of explicit autocorrelation matrix inversion, decomposition, or diagonalization. Theoretical analysis reveals basic properties of the algorithm and establishes formal convergence.When the input autocorrelation matrix is replaced by a conventional sample-average (positive definite) estimate, the algorithm effectively generates a sequence of MVDR filter estimators; the bias converges rapidly to zero and the covariance trace rises slowly and asymptotically to the covariance trace of the familiar sample-matrix-inversion (SMI) estimator. In fact, formal convergence of the estimator sequence to the SMI estimate is established. However, for short data records, it is the early, nonasymptotic elements of the generated sequence of estimators that offer favorable bias/covariance balance and are seen to outperform in mean-square estimation error, constraint-LMS, RLS-type, orthogonal multistage decomposition, as well as plain and diagonally loaded SMI estimates. An illustrative interference suppression example is followed throughout this presentation.Index Terms-Adaptive filters, algorithms, code division multiaccess, estimation, interference suppression, iterative methods, least mean square methods.
The radio frequency (RF) spectrum becomes overly crowded in some indoor environments due to the high density of users and bandwidth demands. To accommodate the tremendous wireless data demands, efficient spectrum-sharing approaches are highly desired. To this end, this paper introduces a new spectrum sharing solution for indoor environments based on the usage of a reconfigurable reflect-array in the middle of the wireless channel. By optimally controlling the phase shift of each element on the reflect-array, the useful signals for each transmission pair can be enhanced while the interferences can be canceled. As a result, multiple wireless users in the same room can access the same spectrum band at the same time without interfering each other. Hence, the network capacity can be dramatically increased. To prove the feasibility of the proposed solution, an experimental testbed is first developed and evaluated. Then, the effects of the reflect-array on transport capacity of the indoor wireless networks are investigated. Through experiments, theoretical deduction, and simulations, this paper demonstrates that significantly higher spectrum-spatial efficiency can be achieved by using the smart reflect-array without any modification of the hardware and software in the users' devices.
Direct-sequence/code-division multiple-access (DS/CDMA) communication systems equipped with adaptive antenna arrays offer the opportunity for jointly effective spatial and temporal (code) multiple-access interference (MAI) and channel noise suppression. This work focuses on the development of fast joint space-time (S-T) adaptive optimization procedures that may keep up with the fluctuation rates of multipath fading channels. Along these lines, the familiar S-T RAKE processor is equipped with a single orthogonal S-T auxiliary vector (AV) selected under a maximum magnitude cross-correlation criterion. Then, blind joint spatial/temporal MAI and noise suppression with one complex S-T degree of freedom can be performed. This approach is readily extended to cover blind processing with multiple AV's and any desired number of complex degrees of freedom below the S-T product. A sequential procedure for conditional AV weight optimization is shown to lead to superior bit-error-rate (BER) performance when rapid system adaptation with limited input data is sought. Numerical studies for adaptive antenna array reception of multiuser multipath Rayleigh-faded DS/CDMA signals illustrate these theoretical developments. The studies show that the induced BER can be improved by orders of magnitude, while at the same time significantly lower computational optimization complexity is required in comparison with joint S-T minimum-variance distortionless response or equivalent minimum mean-square-error conventional filtering means.
The Welch lower bound on the total squared correlation (TSC) of signature sets is known to be tight for real-valued signatures and loose for binary signatures whose number is not a multiple of four. In this letter, we derive new bounds on the TSC of binary signature sets for any number of signatures and any signature length. Then, for almost all , in 1 2. .. 256 , we design optimum binary signature sets that achieve the new bounds. The design procedure is based on simple transformations of Hadamard matrices.
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