"Научный аспект №2-2019" - Технические науки

Optimum Antenna Selection Technique for MIMO System

Аль Тахар Инас Ануар – факультет Электроники, радиотехники и системы связи Института сферы обслуживания и предпринимательства (филиала) Донского государственного технического университета в г.Шахты.

Аннотация: В этой статье был предложен метод выбора антенн в системе с несколькими входами и несколькими выходами (MIMO), который оптимизирует пропускную способность канала системы, полученную путем выбора наилучшей антенны. Метод выбора антенны может использоваться для уменьшения энергии, потребляемой на цепь RF, и повышения эффективности использования энергии для достижения максимальной пропускной способности системы MIMO. В этой статье представлены результаты отношения сигнал / шум (SNR) , которые варьируется в зависимости от количества передающих антенн, и которые должны быть выбраны в зависимости от условия канала, что обеспечивает лучшую производительность по вероятности битовой ошибки (BER).

Ключевые слова: MIMO, массивных каналах MIMO, метод выбора антенн, информацию о состоянии канала на передающей стороне (CSI), пропускной способности канала.

Abstract: This paper has proposed an antenna selection technique in multiple input multiple output (MIMO) system that optimizes the system channel capacity transmission, obtained through selection of the best antenna. The antenna selection technique can be used to reduce the energy consumed per RF chain and improve energy efficiency to achieve maximum throughput of the MIMO system. In this paper, results show that the signal-to-noise ratio (SNR) is varied with the number of transmitting antenna to be selected per channel condition, it offers a better bit error rate (BER) performance.

Keyword: MIMO, Massive channel MIMO, antenna selection technique, channel state information (CSI), channel capacity.


The main challenges of future wireless communication systems are the increase of channel capacity and improved quality of service (QoS), and low-cost hardware in a large-scale system. It has been shown that multi-input-multi-output (MIMO) technology is one solution to attain this by transmitting multiple data streams from multiple antennas[1], in Fig.1, there is a system model with transmit antennas NT and receive antennas NR, where H channel can be represented by (NR x NT). However, the channel capacity (MIMO) improves with the increasing number of transmitting and receiving antennas [2]. But the main drawback of (MIMO) system is that additional high-cost (RF) chains are required as multiple antennas are employed. In general, (RF) chains include low noise amplifier (LNA), frequency down-converter, analogue-to-digital converter (ADC), and each (RF) chain contains a power amplifier (PA) contribute around 65% of the entire energy consumption [3]. Each antenna has (RF) chains. So as the number of antennas increases the number of (RF) chains will increase, which helps to increase the power consumption [4]. Therefore, cost-effective implementation of (MIMO) technology persists a major challenge. Antenna selection technique assists in reducing the implementation cost with preserving most of the benefits of (MIMO) technology by using fewer (RF) chains than the number of antenna elements, while the antenna elements are typically inexpensive, and in some cases are just a patch of copper, the (RF) chains are considerably more expensive. In antenna selection technique, a subset of the available antenna elements is adaptively chosen by a switch, and only signals from the chosen subset are processed further by the available (RF) chains [5]. The channel capacity of the system will depend on which transmitting antennas are chosen as well as the number of transmitting antennas that are chosen. Therefore, channel capacity can be increased by the antenna selection technique [6].

This technique exploits channel state information (CSI) at the transmitting side to extract almost similar benefit as of full diversity system. Other benefits of using this technique reduce the transmission energy by reducing the number of the active transmitter [7]. In addition, this technique has been used in large-scale (MIMO) system. For example, using measured massive (MIMO) channels by using two types of antenna arrays in the same realistic environment, as reported in [8]. With (massive- MIMO), we consider multi-user (MU-MIMO), where a base station is equipped with a large number (say, tens to hundreds) of antennas, and serving several single-antenna users in the same time-frequency resource. This work is aimed at increasing the capacity of the channel using the method of choosing the optimal antenna. The rest of the work is organized as follows. In Section II, the system model is described. In Section III, shows the effect of the channel correlation. In Section IV, illustrative results using the program (Matlab) package. Finally, conclusions are drawn in Section V.


Figure 1. MIMO system (NR x NT).


The antenna selection is a signal processing technique that can help to reduce the number of (RF) chain and power consumption in (MIMO) systems. Meanwhile, the advantage of (MIMO) systems is that better performance can be achieved without using additional transmitting power or bandwidth extension. However, the basic idea of its working mechanism is shown in Fig.2.

The channel capacity with antenna selection (NT, NR) as the number of the selected antennas varies by Q. Each column in Q contains pi denotes the index of the ith selected column, i =1, 2,….., Q. Then, the received signal y is represented as

y = image002H{P1,P2,…,PQ}x+z (1)

Where H{P1, P2,…., PQ} ϵ ϹNRxQ is effective channel will be modelled by (NR x Q) matrix, x ϵ ϹQx1 is transmitted signal , z ϵ ϹNRx1 is additive noise vector, and Ex represents the average energy of every transmitted signal. The channel capacity of the system employing Q selected transmit antennas is given by:

image003C{P1,P2,…,PQ} image004log2 det (INR + image005 HH {P1,P2,…PQ}) bps/Hz (2)


To maximize the system capacity, one must pick the antenna with the greatest capacity, that is,

{P1opt,P2opt,….,P3opt}=arg image006 C{P1, P2,…,PQ} (3)

Where AQ is a set of all possible antenna combinations with Q selected antennas.


Figure 2. Antenna selections with Q with RF chains and NT transmit antennas (Q < NT).


At the receiver, in order to estimate the transmitting antenna is carried out as follows:

i = max (image008 ), (4)

Where H is the (NR x NR) matrix of the complex channel transfer factors; and R is received vector.


The correlation matrices can be generated using the spatial channel model (SCM) for the

(I-METRA MIMO) channel parameters in the 3rd generation partnership project (3GPP) [9]. Case (A) corresponds to the frequency-non-selective Rayleigh fading environment without any correlation among all antenna elements. Case (B) and case (C) deal with the typical urban macrocell environment with the different delay spread, in which each delay component is coming from the same angle of arrival (AoA). Case (D) model the microcell and bad urban environments with each delay component from the different (AoA). The transmit and receive correlation matrices (Rt and Rr) contain information about how signals from each element at the transmitter and receiver are correlated with each other and they are given by:


Rtx =image009 Rrx= image010 (5)

The correlated channel matrix at delay time τ is then obtained as:

Hcorr (image011rx1/2 H (τ,t) Rtx1/2 . (6)

The ergodic capacity for a (MIMO) system over uncorrelated channel paths assuming equal total power transmission as in (SISO) system is given by:

(7) H HH ) ] } b/s/Hz C = E {log [ det ( INr + image012

The correlated channel matrix is given in equation (7) substituting the modified channel matrix in equation (MIMO) channel capacity is given by

C = log2 det (INR + image013 Rr1/2 Hw Rt HwH RrH/2) (8)

Where Rt is the correlation matrix between the transmit antennas, Rr is the correlation matrix between the receive antennas, H is the channel gain matrix, Hw is the i.i.d. Rayleigh fading channel gain matrix. If (NT = NR = N), Rr and Rt are full ranks, and (SNR) is high.The amount of capacity reduction due to the correlation between the transmit and receive antennas. Thereby, the correlation channel between antenna elements depends mainly on the mean angle of arrival (AoA), power azimuth spectrum (PAS) and angle spread (AS) as well as antenna spacing.


The proposed technology is aimed to find the best number of activated antennas that optimize the energy efficiency of the system to achieve the maximum channel capacity for a given range of signal to noise ratio (SNR). In Fig.3, shows (SNR) versus channel capacity (bps/Hz) for (MIMO) system with antenna selection techniques having (NT =10) and Q=1, 2, 3......., 10. From the figure, it is clear that the relationship between the number of selected transmitting antennas and the spectral efficiency of the system, the channel capacity achieved by increases dramatically. In Fig.4, shows (SNR) versus maximizing the channel capacity (bps/Hz) (NT=NR=10). In Fig.5, provides the best number of antennas for a given range of SNR values. This is clearly seen when comparing with the (BER) results in the fading channel shown in Fig.6.


Figure 3. Shows the relationship of channel capacity with (SNR) when using the method of selecting a number of antennas.


Figure 4. Shows the (SNR) relation to the maximum capacity of the (MIMO) channel (bps / Hz) at NT = NR = 10.



Figure 5. Difference (SNR) vs optimal antenna number selection for (MIMO) NT=NR=10.


Figure 6. Shows the value of (BER) versus (SNR) at Q = 8,9,10 and NT = 10.


In this paper, we have used a novel antenna selection technique with larger dimension for the MIMO system that can be used on the channel state information (CSI) at the transmitting side. This aims at reducing cost hardware and energy consumed per multiple RF chain, its main drawback in the future next generation of mobile communication. The simulation result shows the channel capacity increases in proportion to the selected appropriate number of antennas to be activated for a given range of SNR values and BER (bit error ratio) when SNR = 18 dB is equal to 10-3. This is to be exploited in future work to satisfy the requirement of high energy efficiency and low cost for the MIMO system.


  1. E. Telatar, ''Capacity of multi-antenna Gaussian channel,'' European Transaction Telecommunication, vol. 10,no.6,pp.558-595,November/December 1999.
  2. Y. Cho, J. Kim, W. Yang, C. Kang, ”MIMO- OFDM wireless communications with MATLAB”, John Wiley & Sons (Asia) Pvt. Ltd, Singapore, 2010.
  3. D. Gesbert, M. Kountouris, R. Heath, C.-B. Chae, and T. Salzer, “Shifting the MIMO paradigm,” IEEE Signal Processing Magazine, vol. 24, no. 5, pp. 36–46, 2007.
  4. M. Gruber, O. Blume, D. Ferling, D. Zeller, M. Imran, and E. Strinati, “EARTH - Energy Aware Radio and Network Technologies,” in 20th IEEE International Symposium on Personal, Indoor and Mobile Radio Commun. (PIMRC), Sept. 2009, pp. 1–5.
  5. Taylor & Francis Group, LLC,''MIMO system technology for wireless communications'', edited by George Tsoulos.2006.
  6. FaseelaK.Karim,Basil K.Jeemon,''Spatial Modulation With Multiple Active Transmit Antennas,''International journal of modern trends in engineering and research ,2015.
  7. Shuvabrata .B, Kishore K. G,'' Adaptive Antenna Selection Technique for MIMO System,'' IEEE 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN).
  8. X. Gao, F. Tufvesson, O. Edfors, and F. Rusek, “Measured propagation characteristics for very-large MIMO at 2.6 GHz,” in 2012 4th Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012, pp. 295–299.
  9. G. T. S. Group, Spatial Channel Model, Spatial Channel Model AHG (Combined ad-hoc from 3GPP and 3GPP2), 3GPP Std. SCM-134 text V6.0, April 22, 2003.
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