# An Overview of Key Technologies in Physical Layer Security
## 4 Challenges and Promising PLS Solutions
### Promising PLS Solutions
#### 4.2.4 Intelligent Reflecting Surface (IRS)  
Future wireless networks are expected to employ intelligent and software re-configurable functionalities to enable safe and secure communication [108]. Intelligent reflective surfaces have been  
Figure 18: Cooperative jamming using unmanned aerial vehicle (UAV).  
identified as a solution to create a controllable wireless environment. According to [109], an IRS is a software-controlled artificial surface that can be programmed to alter its electromagnetic response. An IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in the desired way towards the intended receiver by adjusting the reflecting coefficients. Authors in [110] discussed two methods of deploying IRS, namely (a) energy focusing and (b) energy nulling. Energy focusing employs a beamforming technique by using the IRS reflecting elements to adjust the phases of the signal coming from the transmitter, so that it is focused towards one intended user. Meanwhile, energy nulling uses the IRS to perform destructive reflection by adjusting the phases of the scattered signals to null out the signal at the unintended recipients [110]. One application of IRS is in holographic beamforming, as shown in [111]. Holographic beamforming is a dynamic beamforming technique that uses a software-defined antenna (SDA).  
To demonstrate the application of IRS in PLS, we consider a communication setup of a MISO shown in Figure 19 from [14]. The system comprises a single transmitter, [PERSON], with \(N\) antennas; an intended receiver, [PERSON]; and one eavesdropper, [PERSON]. Both [PERSON] and [PERSON] have single receive antennas. The system is also made of an IRS which has L reflecting elements. The received signals at [PERSON] and [PERSON] are given by  
\[y_{B}=\mathbf{h}_{IB}^{H}\mathbf{\Theta}\mathbf{H}_{AI}\mathbf{x}+\mathbf{h}_{ AB}^{H}\mathbf{x}+n_{B} \tag{37}\]  
\[y_{E}=\mathbf{h}_{IE}^{H}\mathbf{\Theta}\mathbf{H}_{AI}\mathbf{x}+\mathbf{h}_{ AE}^{H}\mathbf{x}+n_{E} \tag{38}\]  
where \(\mathbf{h}_{IB}\) is the IRS-Bob channel, \(\mathbf{\Theta}=\text{diag}\{[\text{e}^{(i\phi_{1})},\ldots,\text{e}^{(i\phi_ {i})},\ldots,\text{e}^{(i\phi_{L})}]\}\) is the diagonal with \(\phi_{i}\) denoting the phase shift of the \(i\)-th reflecting element of the IRS, \(\mathbf{H}_{AI}\) is the Alice-IRS channel, \(\mathbf{x}\) is the transmitted signal with maximum transmit power, \(P\), \(\mathbf{h}_{AB}^{H}\) is the Alice-Bob channel, and \(n_{B}\) is the AWGN noise at Bob with variance \(\sigma_{n,B}^{2}\). Similarly, subscript \(\{\cdot\}_{E}\) denotes parameters with relevance to Eve.  
Figure 19: Intelligent Reflecting Surface (IRS) system model [14].  
The achievable secrecy rate is given by  
\[\begin{split} R_{s}&=R_{b}-R_{e}\\ &=\log_{2}\left(1+\frac{\mathbf{h}_{IB}^{H}\mathbf{\Theta}\mathbf{H} _{AI}\mathbf{h}_{AB}^{H}P\mathbf{H}_{AI}^{H}\mathbf{\Theta}^{H}\mathbf{h}_{ IB}+\mathbf{h}_{AB}}{\sigma_{n,B}^{2}}\right)\\ &-\log_{2}\left(1+\frac{\mathbf{h}_{IE}^{H}\mathbf{\Theta}\mathbf{ H}_{AI}\mathbf{h}_{AE}^{H}P\mathbf{H}_{AI}^{H}\mathbf{\[PERSON]}^{H}\mathbf{h}_{ IE}+\mathbf{h}_{AE}}{\sigma_{n,E}^{2}}\right)\end{split} \tag{39}\]  
The performance of the secrecy rate in Equation (39) is illustrated by the simulation shown in Figure 20. The secrecy rate is shown by varying the distance between [PERSON] and [PERSON]. Figure 20 clearly shows that the performance of a PLS scheme without an IRS yields a lower secrecy rate with an increasing distance between [PERSON] and [PERSON]. On the other hand, an improved secrecy rate can be achieved when IRS is introduced, as shown in the above-mentioned figure. However, it should be noted that optimization techniques may significantly and efficiently enhance the secrecy rate, as shown in [14].