This paper presents the performance of the Directionally Constrained Minimization of Power (DCMP) and the Zero-Forcing (ZF) in the Angular Spread (AS) environment. To obtain the optimal weights for both methods, the Extended Array Mode Vector (EAMV) is employed. It is known that the EAMV represents the instantaneous AS as well as the instantaneous DOA in the slow fading channel. As a result, it is shown that the DCMP and the ZF using the EAMV estimates can improve the Signal-to-Interference-plus-Noise Ratio (SINR) considerably, as compared with those using the Direction of Arrival (DOA) information only. At the same time, the intrinsic problems causing the performance loss in the DCMP and the ZF are revisited. From this, the reasons for the performance deterioration are analyzed, in relation with the AS, the number of samples, the number of antenna elements, and the spatial correlation coefficient of the signals. It follows that the optimal signal combining techniques using the EAMV estimates can diminish such effects.

Recently the effect of the angular spread caused by locally scattered signals in the vicinity of the mobile has received considerable attention. This paper proposes the Extended Array Mode Vector (EAMV) which represents the Instantaneous Angular Spread (IAS) as well as the Instantaneous Direction Of Arrival (IDOA) of the received signal at the Base Station (BS). Using the EAMV, MUSIC algorithm is generalized in order that it is possible to estimate both the IDOA and the IAS. In computer simulations, the estimates of the IDOA and the IAS in the fading situation are evaluated. The results show that the estimates for small angular spread agree well with the given values and demonstrate the validity of the proposed approach.

It is known that MUSIC and ESPRIT algorithms can estimate simultaneously both the instantaneous Direction of Arrival (DOA) and the instantaneous Angular Spread (AS) in multiple scattering environments. These algorithms use the Extended Array Mode Vector (EAMV) with complex angle. The previous work evaluated the performance of those algorithms by comparing the estimated DOA and the estimated AS with the DOA and the AS given in the EAMV, which uses the first-order approximation. Thus, this evaluation method has not clearly reflected the estimation accuracy of MUSIC and ESPRIT. This paper presents the joint estimation performance of MUSIC and ESPRIT by introducing the criteria for evaluation. For this, the spatial signature (SS) is reconstructed from the estimates of the DOA and the AS, and compared to the true SS in the meaning of data fitting.