Smart antennas for IMT-2000 code division multiple access wireless communications

A method, for use in a smart antenna system, of generating a weight vector based on a maximum signal-to-interference-plus-noise-output power ratio (SINR0) criteria with an eigenvector finding technique, wherein the weight vector is applied after pseudo noise (PN) dispreading rather than in front of a receiver for channel estimation and data symbol demodulation, said method comprising the steps of:

• receiving new post-PN processing vectors y(i) and new pre-PN processing vectors x(i);
• setting a post-PN correlation signal vector y for a finger of a user at snapshot k as:
• y(k)=s(k)+i(k)+n(k)=s(k)+v(k)
• where s(k) is an M×1 desired user signal vector through fading channel;
• i(k) is an M×1 PN-spread interference signal vector;
• n(k) is an M×1 thermal noise vector;
• and v(k)=i(k)+n(k) is an interference plus noise vector;
• calculating an optimum weight vector w(k) as:
• w(k)=ζRvv−1(k)a(k)
• w _ ⁢ ( k ) = w _ ⁡ ( k ) w 1 ⁡ ( k ) ⁢ ⁢ where R vv ⁡ ( k ) = G G - 1 ⁢ ( R xx ⁡ ( k ) - 1 G ⁢ R yy ⁡ ( k ) ) ,
• where G is a PN spread processing gain;
• Rxx(k) is an M×M autocorrelation matrix of M×1 vector x(k), which is a pre-PN de-spreading array sample vector;
• Ryy(k) is an M×M autocorrelation matrix of M×1 vector y(k), which is a post-PN de-spreading array sample vector;
• and a(k) is a channel vector.

A method, for use in a smart antenna system, of generating a weight vector based on a maximum signal-to-interference-plus-noise output power ratio (SINR0) criteria without an eigenvector finding technique, wherein the weight vector is applied after pseudo noise (PN) dispreading rather than in front of a receiver for channel estimation and data symbol demodulation, said method comprising the steps of:

• setting an initial weight vector w(0) and a convergence parameter;
• receiving new post-PN processing vectors y(i) and new pre-PN processing vectors x(i);
• obtaining an autocorrelation matrix Rxx(k) of a pre-PN de-spreading array sample vector x(k) based on samples in the current snapshot interval;
• and
• recursively updating an optimum weight vector by taking a gradient vector ∇(k) of a signal-to-noise ratio (SINR) with respect to w(k) wherein the optimum weight vector can be obtained as:
• w _ ⁢ ( k ) = w _ ⁡ ( k - 1 ) + 2 ⁢ ( G - 1 ) ⁢ ⁢ μ D 2 ⁡ ( k ) ⁡ [ { D ⁡ ( k ) + [LeftBracketingBar] z ⁡ ( k ) [RightBracketingBar] 2 } ⁢ z * ⁡ ( k ) ⁢ y _ ⁡ ( k ) - G ⁢ [LeftBracketingBar] z ⁡ ( k ) [RightBracketingBar] 2 ⁢ R xx ⁡ ( k ) ⁢ w _ ⁡ ( k - 1 ) ] w _ ⁡ ( k ) = w _ ⁡ ( k ) w 1 ⁡ ( k )
• where μ is a convergence parameter;
• G is the PN processing gain equal to a number of chips per symbol, z(k) is an array output;
• and D(k)=GwH(k−1)Rxx(k)w(k−1)−|z(k)|2.