Effect on Channel Capacity of Multi-User MIMO System in Crowded Area
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Effect on Channel Capacity of Multi-User MIMO System in Crowded Area

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Multiple-Input Multiple-Output (MIMO) and Multi-User

Multiple-Input Multiple-Output (MIMO) and Multi-User
MIMO (MU-MIMO) systems have been expected to
improve the channel capacity over a limited bandwidth of
existing networks.

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    Effect on Channel Capacity of Multi-User MIMO System in Crowded Area Effect on Channel Capacity of Multi-User MIMO System in Crowded Area Document Transcript

    • www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 41 IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426 Effect on Channel Capacity of Multi-User MIMO System in Crowded Area Vinay Thakur1 , Surinder Kumar Rana2 , Abhishek Thakur3 1,2 Electronics & Communication Department, Sri Sai University, India 3 Electronics & Communication Department, Indo Global College of Engineering, Punjab, India 1 Vinay.rajput1@gmail.com, 2 Sindu.97@gmail.com I. INTRODUCTION Multiple-Input Multiple-Output (MIMO) and Multi-User MIMO (MU-MIMO) systems have been expected to improve the channel capacity over a limited bandwidth of existing networks [1], [2]. The effects on channel capacity of Single-User MIMO (SU-MIMO) systems in urban scenarios have been previously studied [3]. It has been clarified that the larger number of antennas cannot contribute the improvement on the channel capacity in urban SU-MIMO scenarios due to very high spatial correlation. MIMO is also called by some people my moh and me moh by other people, for the better communication we mostly use multiple antennas at receiver and transmission end. In the latest technology there are several forms of the antennas. In this paper, we focus on the MU-MIMO transmission because it can discriminate multiple users by the difference of Angle of Arrival (AoA). We compare the Multi Access Channel (MAC) capacity in uplink with the channel capacity in SU- MIMO by setting the total numbers of transmitting and receiving antennas of SU-MIMO and MU-MIMO to be the same. Multiple input and multiple output technique has call the notice in wireless communications, because it gives a hike in data output and range without any need of any other external power and any change in bandwidth. It attains this target by giving the same total transmitting power over the antennas to achieve the spectral efficiency and to attain a gain that improves the reliability by reducing the fading effect. When the same numbers of antenna elements are used, the better performance is obtained with MU-MIMO in urban scenarios, unlike identical independent distributed (i.i.d.) channels which are generally assumed in MIMO transmission. We also clarify an interesting relationship between the channel capacity improvement of MU-MIMO compared with SU-MIMO and a path visibility. A. Antenna and User Models The antennas and the user are simulated through fullwave EM simulations that are performed with a three dimensional (3D) solver, FEKO [12]. The MIMO handset has two classic single-band PIFAs designed co-polarized to each other and both resonate at 2.6 GHz. We consider three usage scenarios: i) Head only (H), ii) voice scenario with the user head and hand (HH); and iii) data scenario (D) with the user’s two hands. The examined usage scenarios are shown in Fig. 1(a)-(c) where the phantom head and the hand models are used to simulate the user. B. Antenna Efficiency An important factor in characterizing antennas is the radiation pattern and hence, gain and efficiency of the antenna. The antenna patterns and efficiency definitions are not obvious and cannot be directly derived from conventional pattern descriptions when the antenna is placed in the vicinity of or on a lossy medium. This is due to losses in the medium that cause waves in the far-field to attenuate more quickly and finally to zero. The antenna efficiency is proportional to its gain [11] (,) (,) GDθφ =η⋅ θφ. (2) In (2) ηis the total efficiency factor and D(,) θφ is the antenna directivity, which is obtained from the antenna normalized power pattern that is observed in the far-field. An antenna within a handset, for example, and/or in the vicinity of a user would have different efficiency from an antenna in free space due to changes in the far-field radiation pattern. Fig. 2 shows the total far-field pattern of the antenna in the different usage scenarios described in Fig. 1. The difference in the patterns among the different scenarios is obvious. These differences arise from the change in the electric field distributions at varying distances from the body or any other obstacles in the communications channel. II. ANALYSIS MODEL The urban propagation model employed in this paper is represented in Fig. 1. This model is composed of 64 blocks of 50m×50m. Each block is composed of 4 buildings. The road width is 20m. The buildings are assumed to be constructed of concrete and the relative dielectric constant and conductivity are set to 5 and 0.01S/m, respectively. The uplink scenario of (M1+M2)×NMU- MIMO systems (from MT to BS) are considered. The characters M1, M2, and N respectively represent the numbers of antenna elements of the first MT, the second
    • MT, and the BS. It is noted that M1+M2is supposed to be not greater than N. A linear-array BS is located at the top of a building on one side of the model as shown in Fig. 1. Since an accurate reflection or diffraction cannot be obtained at the edge of analysis model, the MTs are assumed to move independently on the road in an area of 280m×280m around the center of the model along the broken lines in Fig. 1 at the height of 1.5m. The MT antennas are set in a symmetrical array at half-wavelength (λ/2) spacing. The propagation characteristics between MT and BS are then calculated by using the ray-tracing method. The distribution of the height of buildings is assumed following chi-squared distribution:χ2(k), with kdegrees of freedom (DoF) which is herein set to 5. The minimum height of these buildings is set to 4m. The height of building:h, can be expressed as [4] () 4, =+ hkχ The carrier frequency is 3GHz. The numbers of reflection and diffraction are 30 and 2, respectively. The channel response matrices are obtained from the complex received voltage matrices which are calculated at intervals of 14m in length along the broken lines in Fig. 1. The uplink scenario is considered. It is assumed that the Channel State Information (CSI) between the transmitter and receiver is not known by the MT. Whenthe transmitter does not know the CSI, the channel capacity of SU-MIMO can be obtained in the units of bps/Hz as [1] In cases of MU-MIMO, the analysis of channel is commonly referred to the MAC [5]. The MAC capacity (CMAC) is considered as the total channel capacity which the BS antenna can receive from the MTs moving in the propagation area. In MAC channel, the BS can estimate all the CSI from the MTs. In cases of 2-user MIMO systems, this CMACcan be obtained by a substitution of the combined CSI (HMAC) shown in Fig. 2 into (3). III. FUNCTION OF MIMO Three main categories of MIMO, Precoding ,Spatial multiplexing and Diversity coding. Precoding is multistream beam forming and considered to be all spatial processing. In single stream beam signal is transmitted with appropriate gain, phase and maximized power at receiver. Its advantages are to increase received signal gain with all signals get add up from different antennas & reduce multipath fading. In Line of sight, beam formed is directional but conventional beam are not good analogy in cellular network ,with multiple antenna, the transmitting beam formed cannot maximized signal level at receiving antenna. So precoding is used and requires channel state information (CSI) at transmitter and receiver. In spatial multiplexing, splits high rate signal stream into multiple lower rate signals ,each signal stream is transmitted from different transmitting antennas at same frequency channel and required MIMO antenna configuration. If these signals arrive at the receiver antenna array with sufficiently different spatial signatures and the receiver has accurate CSI, it can separate these streams into (almost) parallel channels. It increase channel capacity at higher signal-to-noise ratios (SNR) and maximum number of spatial streams is limited by less number of antennas at the transmitter or receiver. It can be used without CSI at the transmitter, but can be combined with precoding if CSI is available. It can also be used for simultaneous transmission to multiple receivers, known as space-division multiple access or multi-user MIMO, in which case CSI is required at the transmitter. Channel Capacity Characteristics of Urban MU-MIMO Systems The channel capacity of urban SU-MIMO has been evaluated [3]. It has been clarified that the channel capacity of SU-MIMO is deteriorated compared with the i.i.d. cases due to a very high spatial correlation in urban propagation environment. Hence, to reduce the effect of the spatial correlation, the MU-MIMO transmission is introduced. Figure 3 shows the effects of model configurations on the channel capacity of (2+2)×4 MU-MIMO compared with 4×4 SU-MIMO. The results present significance, since there are situations that CMAC> CSU, i.e. the MU-MIMO transmission presents effectiveness. These results confirm that the channel capacity characteristics of MU-MIMO are greatly different from those in neither indoor nor i.i.d. scenarios [6]. These are supported by Fig. 4. The average spatial correlation between users of (2+2)×4 MU-MIMO which two MTs moving independently in the propagation area is much lower than the average spatial correlation between each antenna element of 4×4 SU-MIMO which all MT antenna elements always stay closely. Since the spatial correlation becomes low, its effect on the channel capacity is also deteriorated.
    • www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 43 From the view of receiving antenna (BS), the AoA-diff. is definedas the difference of angle which the signal from each MT arrives at BS.Its effect on the channel capacity is indicated. Figure 5 shows the CMACand CSUat each AoAdiff. In cases of MU-MIMO, when the AoA-diff is increased or the MTs stay farther from each other, the channel capacity is relatively increased. Even if the BS is low mounted (50m) which MU-MIMO transmission is not much more effective than SU-MIMO (see Fig. 3), the channel capacity is also increased when two MTs are far apart which the correlation becomes low. Moreover, when the MTs stay at very near locations, or the AoA-diff is small, (2+2)×4-MU-MIMO channels can be approximately regarded as 4×4 SU-MIMO, and the channel capacity becomes low due to high correlation. Figure 6 shows the channel capacity improvement of MU- MIMO over SU-MIMO. The curves show the ratio between CMAC and CSU. The intersections between these curves and the horizontal dashed line indicate the turning points which CMAC becomes greater than CSU(CMAC/CSU> 1). As the average building height is higher, the turning points relatively present at a higher BS antenna height. For a clear discussion, the path visibility defined as the probability that the direct wave can be received at the receiving antenna or Line-o Sight (LoS) exists [3], is considered. Figure 7 shows the effect of the path visibility on the characteristics of CMAC/CSU. As the results, along the increment of the path visibility, the ratio between CMAC/CSUis relatively increased, because in urban propagation scenario which the spatial correlation is very high, the independent movements of users in MUMIMO can reduce the spatial correlation. That is the reason why the MU-MIMO transmission can present the effectiveness while the SU-MIMO cannot. Furthermore, considering the fitting curve in Fig. 7, it is clarified that CMAC becomes greater than CSU, when the path visibility is about 13 percent. That is to say, to obtain an effectiveness of urban wireless communication, not only the MU-MIMO transmission is supposed to be employed, but also the BS antenna should be mounted at the height so as the path visibility is greater than 13 percent. This result will be useful when considering the installation of the BS in urban SU/MU-MIMO systems. IV. CONCLUSION Throughout this paper, the channel capacity characteristics of urban SU-MIMO and MUMIMO considering the uplink scenario were studied. The MU-MIMO transmission was introduced to reduce the spatial correlation. The MAC capacity in 2-user 2×4 ((2+2)×4) MU-MIMO was compared with the channel capacity in 4×4 SU-MIMO. It was clarified that the spatial correlation between users of MU-MIMO which two MTs moving independently in the propagation area was much lower than that of SU-MIMO which all MT antenna elements stayed closely all the times. Its effect on the channel capacity was consequently deteriorated. By the definition of AoA-diff, it was shown that when the MTs stayed farther from each other which the spatial correlation became low, the channel capacity was increased. Moreover, when the AoA-diff was small or the MTs stayed at very near locations, (2+2)×4 MU-MIMO channels could be approximately regarded as 4×4 SU-MIMO. Finally, it was shown that the channel capacity improvement of MU-MIMO over SU- MIMO was relatively increased along with the increment of the path visibility.
    • REFERENCES. [1] S. Hemrungrote, T. Hori, M. Fujimoto, and K. Nishimori, “Effects of path visibility on urban MIMO systems,” Proc. ISAP2009, Bangkok, Thailand, pp.157-160, Oct. 2009.. [2] Y. Ito, "The distribution of height and width of buildings", in Radiowave Propagation Handbook, Eds. Japan: Realize Inc., 1999, pp. 342–349, Realize Inc., Japan, 1999. [3] A. Goldsmith, S.A. Jafar, N. Jindal, and S. Vishwanath, “Capacity limits of MIMO channels,” IEEE J. Commun., vol.21, no.5, pp.684 -702, Jun. 2003. [4] P. Kildal, K. Rosengren, “Correlation and capacity of MIMO systems and mutual coupling, and diversity gain of their antennas: simulations and measurements in a reverberation chamber,” IEEE Communications Magazine, Dec. 2004.