Smart Antennas: Mobile Station (MS) and Base Station (BS) Antenna Beamforming

• Published in: Smart Antennas and Electromagnetic Signal Processing for Advanced Wireless Technology pp. 217 - 261
• Main Authors: Chong, Ng Kim, Leong, Stetson Oh Kok, Hoole, P.R.P., Gunawan, E.
• Format: Book Chapter
• Language: English
• Published: Denmark Routledge 2020; River Publishers
• Edition: 1
• Subjects: Aerospace & Radar Technology; Electronic Devices; Electronics & Semiconductors; Radar & Navigation Systems; Adjacent BS; AWGN; AF; Handover Algorithm; LMS Algorithm; Beam Pattern; Noise Factor; Neighbor BSs; Antenna Elements; Velocity Estimation Error; Beamforming Algorithm; SNR Variation; Antenna Beamforming; Triangle Method; Smart Antenna; Radiation Pattern; Smart Antenna Systems; Noise Power; BS Antenna; AWGN Channel; CBS; Adaptive Array Antenna; Noise Channel; Isotropic Antenna
• ISBN: 9788770222068; 8770222061
• DOI: 10.1201/9781003339564-8

In this chapter are presented the details of beamforming using model-based electromagnetic signal processor to form a beam from a mobile station (MS) moving through a series of stationary base stations (BSs). The beamforming algorithm is linked to an automatic tracker of the MS position and velocity of the BS with which it needs to maintain connection. The working of the tracker and beamforming algorithms is illustrated for a two-element adaptive array antenna on the MS. The chapter also explains a technique for handing over the control of the MS from one BS to another as the MS moves over the large space or area covered by the wireless system. In the final part of the chapter, the beamforming of a BS antenna that needs to track and stay connected to an MS is presented. In the case of the BS, more antenna elements may be used to form the BS array antenna. Results are presented for two- to nine-element adaptive array antenna. In this chapter, we shall introduce the concept of smart or intelligent antennas in cellular communications. In a smart antenna, the antenna beam is dynamically changed to enhance the system performance. In particular, by controlling the signal strengths at each element of an array antenna, by changing the weights of an adaptive antenna algorithm, the directivity of the antenna is dynamically controlled. We shall focus on the use of such an antenna on an MS, whilst remembering that the same principles may be applied for a BS smart antenna. Amongst the advantages of using smart antennas, the following are the most important: Increasing the channel capacity through frequency reuse within steerable beams. Since the power required is much less than a fixed antenna, 218resulting in a lower carrier-to-interference ratio, the smart antenna can allow channels to reuse frequency channels. Space division multiple access (SDMA) with smart antennas allows for multiple users in a cell to use the same frequency without interfering with each other since the BS smart antenna beams are sliced to keep different users in separate beams at the same frequency. Increasing communication range without increasing battery power. The increase in range is due to a bigger antenna gain with smart antennas. This would also mean that fewer BSs may be used to cover a particular geographical area. Reducing multipath, cochannel interference and jamming signals by forming null points in the direction of unwanted signals. Hence, the link quality can be improved. This could also enable the smart antenna beams to be always focused on the hot spots where the number of subscribers is large in a given area of a cell. Better tracking of the position and velocity of the MSs. The position-velocity estimator (PVE) algorithm presented in Chapter 7 is further enhanced to include MS antenna beamforming. This is a crucial aspect of smart antennas in cellular communications. The MS estimates its own position and velocity, and simultaneously optimizes its antenna beam for reception and transmission. First, the possibility of combining the PVE algorithm and the least mean square (LMS) beamforming algorithm to perform beamforming and position-velocity estimation is investigated. Next and more importantly, an accurate single module beamforming with position-velocity estimator (BFPVE) algorithm is designed using the principle of maximum likelihood estimation. Based on a two-element antenna array, the proposed algorithms are tested in MATLABTM for different channel conditions. This chapter presents the details of beamforming using model-based electromagnetic signal processor to form a beam from a mobile station (MS) moving through a series of stationary base stations. The beamforming algorithm is linked to an automatic tracker of the MS position and velocity of the BS with which it needs to maintain connection. The working of the tracker and beamforming algorithms is illustrated for a two-element adaptive array antenna on the MS. Increasing the channel capacity through frequency reuse within steerable beams. Since the power required is much less than a fixed antenna, resulting in a lower carrier-to-interference ratio, the smart antenna can allow channels to reuse frequency channels. The adaptive beamforming system makes use of the antenna array to perform signal separation and interference rejection. The radiation pattern of an array is determined by the radiation pattern of its individual elements: their orientation and relative positions in space, and the amplitude and phase of the exciting current.