Prediction of SINR Improvement with a Directional Antenna Prediction of SINR Improvement with a Directional
Prediction of SINR Improvement with a Directional Antenna Prediction of SINR Improvement with a Directional
Prediction of SINR Improvement with a Directional Antenna Prediction of SINR Improvement with a Directional
Prediction of SINR Improvement with a Directional Antenna Prediction of SINR Improvement with a Directional
Prediction of SINR Improvement with a Directional Antenna Prediction of SINR Improvement with a Directional

Prediction of SINR Improvement with a Directional Antenna Prediction of SINR Improvement with a Directional

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  • Prediction of SINR Improvement with a Directional

    Antenna or Antenna Array in a Cellular System

    Karthik KS

    Department of Electrical Engineering,

    Indian Institute of Technology Madras,

    Chennai, 600036, India

    Email: kartks@tenet.res.in

    Bhaskar Ramamurthi

    Department of Electrical Engineering,

    Indian Institute of Technology Madras,

    Chennai, 600036, India

    Email: bhaskar@tenet.res.in

    AbstractWe consider a reuse-1 cellular system and study theimprovement in SINR possible for a fixed/nomadic receiver, fromthe use of an optimally oriented directional patch antenna or aUniform Linear Array (ULA) with 2 or 4 antenna elements alongwith Minimum Mean Square Error (MMSE) combining. Weperform the studies with a ray-tracing propagation model as wellas an empirical statistical model that is based on measurementvalues, in an urban macro scenario. The results show that amean improvement of around 5 dB in SINR is possible usingeither the directional antenna or a 2-antenna ULA. In contrast,a 4-antenna ULA gives a mean improvement of around 11 dB.Interestingly, the results are similar with both channel models.

    Index TermsRay-tracing, SINR, directional antenna, ULA,MMSE, relay.

    I. INTRODUCTION

    Comprehensive planning is essential for building a mobile

    cellular network, especially for the major emerging standards

    of the next-generation cellular networks that target high spec-

    tral efficiency. Although there is no frequency planning due

    to 1:1 spectrum reuse, the locations of base stations and their

    configurations and specifications still need to be determined

    and an accurate prediction of coverage and capacity is essen-

    tial. There exist a number of prediction models which can be

    used for this purpose.

    One method of estimating the behavior of signals in a

    mobile communication system is by the use of a statistical

    propagation model. These models are empirically derived

    estimates of attenuation and propagation of electromagnetic

    waves within the geographical area of the cell. They make use

    of various parameters such as building heights, transmitter and

    receiver heights, transmitter-receiver distance and frequency.

    Since these are empirical models, an accurate characterization

    of the environment with precise building data is not taken

    into consideration and the prediction for a receiver location

    represents only a typical estimate. The cells are characterized

    as urban, suburban and rural, and the model parameters are

    varied accordingly.

    Ray-tracing [1] is a technique that provides a deterministic

    estimate of the mobile radio channel at a particular location.

    A model of the actual 3-D environment is created and the

    physical wave propagation process is modeled. The radiation

    emitted by the transmitter is subjected to suitable formulations

    of propagation phenomena such as reflection, refraction and

    scattering until several multi-path components reach the re-

    ceiver. This ray-tracing technique is used, for example, in the

    Radio Propagation Simulator1 (RPS) tool in a deterministic

    manner using Geometrical Optics and Uniform Theory of

    Diffraction.

    The emerging wireless cellular networks are likely to re-

    use spectrum in every sector (1:1 reuse) in order to maximize

    the system spectrum efficiency. This in turn can lead to

    a situation where the receivers near the cell edge experi-

    ence a poor SINR (Signal-to-Interference-plus-Noise-Ratio)

    due to significant Co-Channel Interference (CCI) from the

    adjacent cells. One of the ways to increase the SINR for

    fixed/nomadic users is to use a directional antenna at the

    receiver in order to suppress the CCI from the adjacent cells.

    Employing multiple antennas at the receiver and using some

    well-known receiver-based techniques such as MinimumMean

    Square Error (MMSE) combining also results in interference

    suppression [2].

    Using ray-tracing technique in a 3-D model of Dresden city,

    we find that the number of strong interferers is mostly limited

    to 3 or less in a reuse-1 cellular system. Hence significant

    improvement in SINR is possible using either a directional

    antenna, or a uniform linear array with MMSE combining at

    the receiver. The results are compared with the urban macro-

    cell scenario of the WINNER (Wireless World Initiative New

    Radio) II generic channel model [3].

    The emerging cellular networks are expected to have relays

    and pico base stations in large numbers [4]. Relays can be

    used to address the problem of poor SINR at cell edges.

    In this paper, we attempt to obtain insights regarding the

    SINR improvement in cellular networks due to the large-

    scale deployment of wireless relays using well-known SINR

    improvement techniques, with two different channel models.

    Both the models are seen to predict similar improvements,

    and hence either of the two models can be used to study

    deployment-related issues on wireless relays.

    The paper is organized as follows. The ray tracing simula-

    tion using RPS is introduced in Section II, followed by the

    comparison with WINNER model in Section III. The simula-

    1Product of Actix GmbH

  • tion results are discussed in Section IV, before concluding in

    Section V.

    II. RAY TRACING SIMULATION

    The RPS ray tracing simulation algorithms are based on

    geometrical optics. The simulator determines the propagation

    paths that can contribute to the signal at a given receiver

    position. Besides the geometrical calculation, the propagation

    loss is determined for each ray separately.

    The transmitters and the receivers are placed as points

    in a 3-D topographical database. The RPS accesses the 3-

    D database and launches a finite number of rays from each

    transmitter position into all directions with 1 deg resolution

    in the three-dimensional space. When a ray encounters an

    obstacle, the radio propagation effects of reflection, diffraction

    and scattering are applied by the RPS simulation algorithms

    as per the material properties of the obstacles, which are read

    from the material property database. Each ray is traced until a

    given maximum path loss is exceeded. A part of the Dresden

    3-D environment model along with the rays is as shown in

    Fig. 1.

    Fig. 1. Dresden 3-D model snapshot

    The algorithm calculates the properties of the electromag-

    netic field, the complex channel gains, Angle of Departure

    (AoD), Angle of Arrival (AoA), and Time Delay of Arrival

    (TDoA) for every ray of every transmitter-receiver combi-

    nation. The effects of the beam patterns of the transmit

    and receiver antennas are taken into account by the ray

    tracing algorithm during simulation. The result of the ray

    tracing algorithm is a time-invariant (single sample point)

    complex impulse response of a Single-Input-Single-Output

    (SISO) radio channel between a particular pair of transmitter

    and receiver, which can be written as:

    hSISO =

    n

    n (1)

    where,

    n = nexp

    (

    j2dn

    )

    (2)

    for a flat-fading channel [5]. Here, n denotes the path index

    between transmitter-receiver pair, n is the attenuation co-

    efficient corresponding to the nthpath/ray, dn is the distance

    between the transmitter and the receiver on path n, is

    the carrier wavelength. RPS tool provides the values of nfor every ray received in every receiver from each of the

    transmitters.

    The channel impulse response of a Multiple-Input-Multiple-

    Output (MIMO) channel is in the form of a matrix and depends

    on the antenna configuration that is used in transmitter and the

    receiver as described in [6]. Separate placement of individual

    antenna elements in the 3-D database, for an antenna array

    with elements few centimeters apart from each other as in the

    case of ULAs at a receiver, is not feasible since the resolution

    is insufficient. Hence, the channel impulse response matrix is

    constructed from the SISO channel impulse response (1) using

    the plane wave model and the procedure explained in [6]. In

    our simulations, we have considered the inter-element distance

    of the ULA to be equal to = 0.5. The channel impulse

    response matrix of a receiver with ULA having Nr antenna

    elements can be constructed as a vector of length 1xNr as:

    h =

    n

    nexp

    (

    j2dn

    )

    1

    ej2 cos n

    ...

    ej2(Nr1) cos n

    (3)

    where, is the spacing between the antenna elements

    within the ULA normalized w.r.t and is the angle of the

    incoming ray w.r.t the ULA.

    Ray tracing simulation using the RPS tool was carried out

    for 100 receiver locations in the Dresden environment. The

    entire geographical area was covered by 7 base-stations, each

    having three sectors each. The sector antennas were oriented

    at 120 degrees from each other and were located well above

    (about 25 m) the average height of buildings. The sector

    antennas used had a 3 dB bandwidth of 70 deg and a gain of

    17 dBi, and a down tilt of 12 degrees. The transmitter power

    in each sector of the base-station was fixed at 40 dBm. An

    inter-site distance of at least 600 m or more was maintained.

    The receivers were placed very close to the buildings, in

    a random manner with heights varying from 1.5 m to 6.5

    m, to simulate placement near windows. The simulation was