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Output SNR Improvement in Array Processing Architectures of WCDMA Systems by Low Side Lobe Beamforming

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Output SNR Improvement in Array Processing Architectures of WCDMA Systems by Low Side Lobe Beamforming Output SNR Improvement in Array Processing Architectures of WCDMA Systems by Low Side Lobe Beamforming * Rajesh Khanna & Rajiv Saxena Department of Electronics & Communication Engineering, Thapar Institute of Engineering & Technology, Patiala. * Principal, Rustamji Institute of Technology, BSF Academy, Tekanpur ABSTRACT wanted signal. This is done by placing the nulls in the In Wideband direct sequence code division multiple pattern in the corresponding known directions of these access (WCDMA) same frequency spectrum is shared through interferers and simultaneously steering the main beam in all cells simultaneously, as opposed to TDMA which is used for the direction of the desired signal. This method of beam most 2nd generation systems. In a WCDMA system all forming is known as Null Beam Forming [1]. Adaptive transmitted signals turn out to be disturbing factors to all arrays, often referred as “Smart antennas”, were suggested other users in the system in the form of interference limiting in the early 1960’s and proved to be useful to cancel the system capacity. To suppress the amount of interference, directional interfering signals, improving the performance fast and reliable interference controlling algorithms must be of cellular wireless communications systems [3]. An employed in next generation systems. In this paper it is shown adaptive array with optimum weight vector can be used for that antenna arrays with steer able low side lobes can reduce null beamforming. The flexibility of array weighting to get interference in WCDMA can increase the system capacity and the desired array pattern can be exploited to cancel output signal to noise ratio of the array processing directional sources operating even at the same frequency architecture. The performance metric O/P SNR of an array processing architectures is simulated in an interfering as that of the desired source, provided these are not in the environment to demonstrate the advantage of low side lobe same direction of the desired source. In adaptive antenna beamforming over adaptive antennas. theory the basic concept of nulling is based on number of antenna elements. An array with M number of antenna Index Terms-Low side-lobe beamforming, Output SNR, elements can null out M-1 interferers only. If the number Array Processing Architectures. of interferers are more than M-1 than the array become overloaded and its performance starts degrading. One thing is sure that the adaptive antennas are designed to restrain a 1. INTRODUCTION little numbers of strong interferers in wireless One of key features in a 3G cellular system is a communication. high data rate. For a high data rate, a lower BER, a smaller With the WCDMA technique, all users spreading factor and higher transmitting power at a base communicate simultaneously in the same frequency band. station are required. The increased power results in Thus the number of interferers in CDMA signal increased interferences in the cell. In WCDMA systems environment is at least in the order of tens and it is never multiple access interference (MAI) is the major cause of realistic to have that many antenna elements in the cell site transmission impairment. A promising technique to reduce of a given wireless communication system for nulling this MAI is adaptive antenna array and there has been great these interferers. This means the behaviour of the radiation deal of interest in reducing MAI through use of adaptive pattern adaptation system is no longer of the null steering beamforming to improve the capacity and performance of type but instead, [4] an antenna pattern with reduced CDMA based systems [2]. The adaptive beamforming response toward undesirable interferences is desired. This techniques proposed for mobile communications perform technique also helps to reduce co-channel interference due spatial filtering by forming sharp nulls in the direction of the to less energy being transmitted in unwanted directions. interfering mobiles. Will null beamforming help in reducing The main beam is simply pointed in the direction of the MAI in WCDMA systems where number of interferers are desired mobile unit. In this way, the reduction in high? interference level will be only partial, however it is no longer necessary to put nulls in the direction of the interfering units with critical precision, the adaptation 2. NULL VS. LOW SIDE LOBE system actually requires less complexity. BEAMFORMING In cellular communications there are cases where A null in an antenna pattern denotes a zero the angular spread of the rays impinging on the array is response. Instead of steering the beams toward all mobiles, large that a sharp null can not provide efficient interference one may shape the antenna pattern in such way that it cancels suppression. [5] Such cases, which arise when the mobile interfering mobiles and produces a strong beam towards the station is located close to the base station or when the direction of arrival changes quickly due to the motion of the performance [10] viz; Signal-to-interference-plus-noise mobile, can significantly degrade the performance of the ratio (SINR), Normalized SINR (α), Array gain (AG) and system. Because in such case one has to form a broad null Mean square error (MSE). These quantities are computed towards the complete angular spread. The large angular at the output of the array beamformer. These metrics are spread can also be handled if a broad low side lobe is evaluated in presence of interferers. The mathematical formed towards the direction of the largely spread formulation of these metrics is derived next. interference. There has been considerable interest in the recent years in beamformers which are able to synthesize 4.1 Signal-to-interference-plus-noise ratio controlled broad low side lobe in the mobile radio networks. (SINR) SINR is a useful metric for its absolute From the above discussion it is obvious that in the interpretation. In radar systems SINR dictates detection next generation wireless systems the side lobe control will performance. SINR is a generalization of SNR (signal-to- play a major part in reducing interference and improving noise ratio) that explicitly takes into account the impact of capacity [6-8]. In next generations systems the side lobe interference, i.e., interfering. Maximizing SINR is the topography will play an important role in controlling optimal criterion for most detection and estimation interference and antenna arrays shall be able to reduce problems [10]. For example, any communication system interference by pointing the main beam towards the detection performance is directly related to SINR. Under direction of the desired user and by minimizing the the assumptions of Gaussian interference and known sidelobes towards other interferering users. This ability of statistics it is shown that maximizing the SINR leads to the array system shall lead to increased system capacity. maximizing the probability of detection power at the The side lobe control can be achieved by use of both tapered individual element [1] We are most concerned with SINR beamformers non-adaptively and by shaping the antenna at the output of the array and therefore when SINR is not pattern adaptively. The array mathematical model used in further specified it is assumed that we mean output SINR. thesis is derived next. Input SINR, or element-level SINR, is ratio of the signal power to the interference plus noise 2 3. ARRAY SIGNAL MODEL σ s (5) SINR input = 2 2 To derive array mathematical signal model σ i + σ n consider an M-element uniformly spaced linear array, as 2 2 2 where σ ,σ ,σ are the desired signal, interference, shown in Fig.1 [9]. The model considered here is for single s i n signal which can be extended to multiple signals. In Fig.1 and thermal noise powers at the individual element level. the array elements are equally spaced by a distance d, and a Output SINR is the ratio of the signal power to the plane wave arrives at the array from a direction θ off the interference-plus-noise power at the output of the array 2 2 H 2 H (6) array broadside. The angle θ is called the direction-of- w X s σ s w a (θ ) SINR output = = H 2 w H R w arrival (DOA) or angle-of-arrival (AOA) of the received E [ w x in ] in signal. The signal received at the array be given by where [a(θ )]2 is the array manifold vector defined in (3). x(t) =[x(t) x (t) x (t) x (t)]T 1 2 3 M (1) For the distortion less constraint with no steering vector 2 π x ( t ) = x ( t) exp[ − j( (i − 1)d sin θ )] (2) mismatch, i.e., the steering vector used to compute w is i 1 λ identical to the steering vector used to evaluate the SINR, then, equation (1) may be expressed in vector form as by simplifying (6) x(t) = a(θ )x (t) σ 2 N i (3) SINR = s (7) output w H R w The vector x(t) is often referred to as the array input data in vector and a(θ) is called the steering vector. We define mean square error (MSE) as a measure of an ⎡ 1 ⎤ adaptive filter's ability to cancel noise. MSE is defined as ⎢ 2π ⎥ (4) H 2 H ⎢exp( − j d sin θ) ⎥ MSE = E[ w x ] = w R w (8) λ in in a(θ) = ⎢ ⎥ ⎢ . ⎥ Substituting (7) into (8) yields the final expression for the ⎢ ⎥ 2π output SINR ⎢exp[ − j (M − 1)d sin θ]⎥ ⎣⎢ λ ⎦⎥ M σ 2 (9) SINR = s output MSE Equation (9) shows that output SINR is inversely 4. PERFORMANCE METRICS OF proportional to MSE AN ARRAY PROCESSING 4.2 Normalized SINR ARCHITECTURE Normalized SINR, α, is the ratio of the SINR for To analyze the performance of any algorithm used a given weight vector to the SINR for the optimal weight for beamforming in array processing architectures there are vector.
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