Basically, a system with smart antennas exhibit number of antennas and also includes Digital Control (DC) system. The combination of the above gives the beam forming intelligence, thus the term smart antenna is originated. The addition of more number of elements in a smart antenna based system can bring changes in its physical system. Thus, because of composite system, antenna can perform shifting of signals before it gets transmitted to every successive element. The block diagram of smart antenna is shown in Figure 1, which consists of number of radiating elements, control unit, dividing/combining network. Based on the number of inputs the processor will control the feeder components of antenna and that optimizes the communication links. Thus, smart antennas are not just an antennas but acts as complete transceiver.

The smart antenna consists of set antenna components, which dynamically make changes in the antenna pattern to filter the interference, noise in the channel and solve the effect of the multipath fading over the interested signal. The smart antenna can receive and transfer the signals in both the spatially sensitive, adaptive manner. The smart antenna is effective due to combination of digital signal processing (DSP) software and antenna components. This combination enhances the performance/capacity in the wireless system. The smart an-

tenna mainly located at the base stations due to need of sophisticated DSP.

Due to the system complexity and higher power consumption, the smart antenna mechanisms are mainly considered at base station. Currently the smart antennas are used in various communication areas. From the conventional theory, smart antenna is basically classified as Switched Beam and Adaptive Array system (Figure 2). The switched beam system is further classified into single beam directional antenna and multi-beam directional antenna. Similarly, adaptive array system is further classified into single user beam forming and multi-user beam forming. Basically, switched beam system uses only fundamental forms of switching between apriori array beams and discrete directional antenna. On the other hand, adaptive beam forming is used for assessing amount of signals being received by the user.

i) Adaptive Antennas (AAs)

These are the antenna arrays which dynamically changes the antenna pattern to control the interference, multipath and noise. The main functionality of these antennas is that it sets enhances the received signals and also generate the beams to transmit the signals. These antennas release the broadcast energy to the narrow beam which cannot found in conventional antennas. Based on the signal distribution in air, this antenna optimizes the real time based distribution to the desired user and avoids the other users to occupy the same channel in same/adjacent/distant cell [1] - [8] .

The significance of AAs is that the generated antenna beams can adaptively tracks the signal direction, i.e. no other signals are included in the interferer direction. In AAs, there is no inter-cell issue is found due to continuous tracking of user. The AAs can enhance the capacity in greater extent than switched beam. The AAs can have extensive signal processing through DSP. The main drawback of these antennas is that the cost of installation is very high [8] .

ii) Switched Beam (SB) systems

These kind of systems are formed by a switch function among the distinct pre-defined beams/directive antennas of an array. The point at which the top level performance can be achieved as receiver power is considered in SB systems. These antennas exhibit high directivity, which helps to achieve some gain [8] [9] .

These systems can be implemented easily and the cost is low. Also, the system uses the algorithms to select the beam. This system needs only interaction among the base station receiver. The main drawback of this system is that the inter-cell interferences among the beams are needed to be handled and also it cannot mitigate the interference components of multipath near to the desired signal [10] .

iii) Adaptive BeamForming (ABF)

The adaptive beam forming [11] [12] [13] (ABF) combines the multiple antenna inputs from the antenna array in order to form the narrow beams for individual users present in a cell. The beam former (BF) is also known as adaptive antenna. The beam former indicates the antenna capability (direct the flow of signals in a particular direction). In the Figure 3, P is the weight vector and it can be formulated by input I(t) received through various antennas. The adaptive processor will then minimizes the errors Er(t) among the array output y(t) and desired signal ds(t).

The ABF needs a proper signal processing, which is more expensive as per the commercial applications point of view. The digital beam forming (DBF) in the wireless communication gives the complex digital data obtained through received signals. The advantage of the beam former is that it adjust the complex weights present at the output end of every array sensor and by which a pattern is produced which optimizes target signals reception at a particular direction with statistical sense. Also, the beam forming estimates the signal coming from the particular direction even at presence of noise and interference of co-channels.

The ABF can be achieved with the advanced DSP and software’s. The energy of an desired beam can generate the significant gain and facilitates the signal extension.

The main disadvantages of the narrow beam are that it faces interferences, allows more users in a same cell at same time and at same frequency. By implementing ABF, cell spectral efficiency can be multiplied up to ten-times. The traditional beam former steers the gain of an antenna in a particular direction of desired signal. There are two variety of algorithm which helps to achieve ABF. i.e. Recursive Least squares (RLS) and Least Mean Squares (LMS) algorithm.

・ Recursive Least Squares (RLS): This adaptive algorithm, which provides the rapid convergence speed. This algorithm identifies the coefficients which minimizes the weighted linear least squares of the input signals. The input signals in this algorithm are considered deterministic and exhibits its significance in cost of computational complexity [13] [14] .

・ Least Mean Squares (LMS): This algorithm is presented in 1959 by Window and Hoff as adaptive algorithm. This algorithm uses the gradient based method to calculate the gradient vector of the available data. This algorithm consist an iterative process that brings the successive corrections in negative direction of the gradients with least errors. This algorithm is quite simple and doesn’t need a correction functions or matrix inversion [13] [14] .

The smart antenna based systems gives the following significances [11] :

i) Enhanced coverage, Enhanced capacity

ii) Enhanced link quality, Enhanced data rate

iii) Low power consumption

iv) High sensitive reception, Reduced costs

v) Suppression of Interference

vi) Lower absorption rate

vii) Enhanced spectral efficiency

viii) Reduced multipath fading

In this access technique, the access is performed based on the direction of the entering signal. This method helps to enhance the capacity, reduce the interference and improve the entire link quality of wireless communication. This method ranges from the switched beam mechanism to adaptive antenna. The access method enhances the capacity and QoS in the communication network. By using this method several mobiles can be communicated in one base station at same frequency.

In this access method, “the total bandwidth can be divided with number of simultaneous users, where each user can be allocated with a channel and bandwidth”.

i.e.

The development of these techniques was found in World War II, where the different countries military units started to search the effective technique to block the enemies’ radio signals by jamming. The technique spreads the energy of signal information.

In this technique, the capacity of channel is can be used as much as possible and later the bit stream can be divided into frames and these frames can be divided as time slots allotted for each users. The system based with this technique uses only two time slots/frame for each user.

In the populated areas, the enhancement of the capacity is major point. There are two main strategies to enhance the capacity i.e. Interference mitigation (at downlink)/rejection (at uplink). Thus, to mitigate the existing interference, the directional beam can be steered towards the mobiles while to reject the interference by forming nulls/directional beams at base station receives the antenna patterns at interfering direction of co-channel users.

The capacity of an system is the spectral efficiency and also traffic offered by various users. The number of users which supported based on the traffic generated by various users and channels in each cell.

The extension of the coverage in the sparsely populated area is major issue than enhancing the capacity. In these areas, the generated gain of adaptive antennas can extend the cell range to cover the more users and large area than other antennas.

A research of Jain and Agarwal [16] described the coverage and connectivity improvement concept in wireless communication using smart antenna system. It is mentioned that the researches in this field is more as the smart antennas offer better network performance. Author has mentioned that to attain the better network performance the network provider must need to calculate the gain for performance computation using propagation results of smart antenna. Authors have analyzed the performance of the proposed system and found that better coverage in cell area.

The collaborative work of Li and Wang [17] explains the QoS optimization in OFDM network for moving users by using semi smart antenna s. It is observed that the most of the patterns may change on user movement. In order make better changes in qualified user’s movement in pattern a periodic optimization (PO) method is introduced and achieved the same without excess triggering.

The work of Li et al. [18] have given a semi smart antennas implementation to optimize the radio resources in OFDMA network and offer a better quality of service. The semi SAs helps to solve the co-channel interferences in cell edge side and optimize the energy consumption. Author have used the system level simulation and analyzed the proposed system performance by which concluded that the desired throughput has achieved.

The work of Imtiaj et al. [19] described the performance analysis of the various ABF algorithms in smart antenna. The ABF algorithm in smart antennas gives null steering, maximum coverage, better system capacity giving enhanced QoS than other antennas.

The performance analysis study on WSN with smart antennas is presented in Skiani et al. [20] . The study mainly concentrated on the WSN issues like routing, QoS, interference, power consumption etc. The study analysis which described in the test cases gives that with smart antennas 20% of QoS, 50% of efficiency and the power consumption is improved.

In order improve the QoS in vehicular communication by smart antennas optimization method is proposed in Constantinescu and Borcoci [21] . Author is focused on the issues related to the mobility management, scheduling tasks, neighbor discovery and interference range and also the base station issues like availability of bandwidth and coverage, velocity Vs throughput, availability/continuity of service. This study gives the desired research in vehicular communication.

The above research survey is summarized in Table 1.

In this, the recent researches in routing of smart antenna are discussed. These researches are selected from the IEEE Xplore. A Diamond Shaped Mesh Network (DSMN) routing is illustrated in Bao et al. [22] with the cross layer design to attain the benefits of Multi beam SAs. This routing mechanism has two unique features like

a) Cross layer design integrated with routing mechanism with multi beam smart antenna s.

b) The routing is of diamond shape and impalements multipath routes.

The simulation is used for performance analysis and found that high throughput is attained. Author Cikovskis [23] have presented the cancellation of the interpath interference in wireless network by smart antennas. In order to avoid the data degradation by interferences, a smart way of routing and transmission mechanisms are needed to used along with the smart antenna. In this work author have used analytical method to analyze the performance of the smart antenna and found that the cancellation of interferences depends on the degree of antennas freedom used.

A multicast delay based routing algorithm is introduced in the work of Hung et al. [24] by using the smart antenna. In this smart antenna are used because of its reusability and low power consumption. The performance analysis gives that

that the algorithm with smart antenna gives better power consumption and throughput.

In Loh et al. [25] have given the concept of performance assessment of the adaptive routing in WSN by using smart antenna. The smart antenna in this performs the significant role in performance enhancement and power optimization.

Babu et al. [26] given the concept of enhancing wireless network performance by using cognitive radio with smart antenna. Authors have studied the performance of wireless network by using the smart antenna. The architectural design of proposed method with spatial selectivity allows the load sharing in various bands which increases throughput. From the analysis of the simulations, authors work found that the interferences occurred for the licensed users during the implementation of uni-directional antenna. These above literatures are summarized in Table 2.

This section describes the research performed to bring the energy efficiency in the smart antennas.

Hanaoui and Mounir [27] exhibited the idea talking about the directional correspondence with smart antenna to improve the power efficiency in wireless communications. Author makes a perspective of ground with hubs by utilizing Matlab. The author concluded with the significance of energy efficiency with smart antennas.

The work of Kristem et al. [28] gives the significant discussion regarding the selection of antenna to achieve energy efficiency in wireless communication. Author presents discussion regarding proper energy allocation, Symbol error probability at pilots and data, and optimal selection rule in time-varying channels for the two schemes.

The significant work of Zhu et al. [29] , gives the energy saving mechanism called Transmission Mode (TM) selection in semi SAs. The proposed method is combined with the semi-SAs to attain energy saving. In this, based on the zooming scheme the identified transmission nodes were switched off/ sleeping mode without lowering the cell coverage. The optimization of TM mechanism to attain energy efficiency was defined and used generic algorithm to achieve the optimal solution. From simulation results found that the proposed TM selection mechanism in network combined with semi-SAs can offer significant energy efficiency in network.

Later, the research towards the energy efficiency for wireless network with smart antennas in multipath environment is presented in Tang and Ramanathan [30] . Later, Adaptive Antenna (AA) array combined with Multiple-Input-Multiple- Output (MIMO) channel model used for wireless multipath environments. The study gives that energy efficient one hop multicast in such environment.

In Ayodele et al. [31] described the smart antennas analysis to improve the signal quality and also spatial processing. This research work focused on the smart antennas can optimizes the Signal-To-Noise-Ratio (SNR) of multiple antenna arrays in. In order to improve communication channel performance adaptively, a measure such as SNR and dynamic range need to be used. The proposed antenna arrays are adapted and aligned in magnitude and phase. These above literatures are summarized in Table 3.

This section discusses the selected significant researches towards the security concerns, ABF in smart antenna. The research expressed in Yuanjian and Xiaohui [32] discusses the ABF method in SA system. This work illustrates a new ABF by analyzing and improving an algorithm based on projection of gradient vector. The proposed algorithm was implemented to a Uniform Linear Array (ULA) and its improved structure to compare its performance with that of the conventional LMS algorithm. The results obtained give the significances of the proposed method in performance and throughput wise. The discussion regarding security system modelling based on smart antenna is found in Oluwole and Srivastava [33] . This work uses three antenna elements array. (i) First element was used to transmit/receive the radio frequency signal. This transceiver used to receive also acts remotely to transmit information signal virtually to base station. (ii) The other two elements in base station were used as descrambler against any illegitimate activities. This work considers the imperative securities challenges such as authentication, attacks, privacy and secrecy. In the choice of frequency bandwidth for the transmission of signal, IEEE standard 802.11 designed for

wireless system had been maintained. The work concludes an significant scopes of the security issues and solution to overcome. These above literatures are summarized in Table 4.

Although, we speak highly about adaptive array antenna, however, it is potentially shrouded with pitfalls. The first problem in using adaptive array antenna is its implementation issue which is associated with higher complexity with respect to design. Moreover, such forms of research work have heavy dependencies over precise estimation of channel as well as speed of convergence related to its Beamforming mechanism. Hence, adoption of adaptive array antenna has definitive problems towards implementation in MAC layer. Therefore, applicability of different forms of smart antenna towards cross layer is a significant problem that is still unsolved.

There is comparatively lesser number of studies towards multiplexing techniques over wireless network. For an example, Orthogonal Frequency Division Multiplexing (OFDM) is one of the dominant techniques used in 3G and 4G network and a variant of it is also a part of investigation in 5G networks. In reality, there are very number of research journals (only 8 journals published in IEEE Xplore since 2010) towards such forms of multicarrier technologies with respect to smart antenna. It is in a very nascent stage of research.

It has been observed that majority of the research work carried out in smart antenna has used simulation based study and less number of investigations towards real-time implementation. Although, there is nothing wrong in performing implementation using simulation based study, but existing research work has failed to prove its applicability towards real-time usage. There is not a single benchmarked studied using simulation-based approach posing threat to internal validity.

Apart from this, the existing researches focused on the smart antenna are failed to give the efficient smart antenna system and faces some of the following issues. For the diversity, there is need of multiple antennas at the terminal end. Many of the researchers are thought that adaptively combining the signals of antennas rather than using not blocked antennas. The use of multiple antennas significantly increases throughput in existing system; however it also increases the power and also decreases the quality of communication performance.