symbols of $\bar{x}$ one after the other, starting from the symbol of $\bar{x}_{K_1+K_2}$. This estimated symbol is symbolized as $\hat{x}$ and represented as :

$$ \hat{x}_{K_1+K_2} = q \left( \frac{\tilde{y}_{K_1+K_2}}{r_{K_1+K_2,K_1+K_2}} \right) \qquad (5.17) $$

where $q(.)$ Is the quantification function. Then, the detected symbol $\hat{x}_{K_1+K_2}$ is removed in the next step to detect $\hat{x}_{(K_1+K_2)-1}$. This operation is repeated until all components are detected. So we will get :

$$ \hat{x}_i = q \left( \frac{\tilde{y}_i - \sum_{j=i+1}^{(K_1+K_2)} r_{i,j} \hat{x}_j}{r_{i,i}} \right), \quad i = ((K_1 + K_2) - 1), ((K_1 + K_2) - 2), \dots, 1 \quad (5.18) $$

Assuming no error propagation, symbol detection can be correctly carried out sequentially in $K_1+K_2$ steps [131], from the last symbol $\tilde{y}_{K_1+K_2}$ to $\tilde{y}_1$. The interference free symbol detection is only possible if the sum of transmit antennas within the same communication range $K_1+K_2$ does not exceed $N_r$ (i.e., $K_1 + K_2 \le N_r$). Otherwise, the system is under-determined. This condition is referred to as the AQR. Then finally, the receiver $V_2$ retains the desired data from $V_1$ and ignores the rest.

From Figure 5.2, it is also possible for the $V_4$, as the hidden node in the communication range of $V_2$, doing the simultaneous transmission. Using the same ZF-VBLAST architecture as mentioned above, we can do the interference nulling and cancelation process from both $V_3$ and $V_4$ as the interference vehicles. Indeed, this mechanism will unblock many vehicles and allows them to communicate simultaneously. For this purpose, vehicles negotiate with each other the number of selected transmit antennas so that the AQR is satisfied and maximize the network throughput.

### 5.3.5 TPA Algorithm

The AQR condition requires that the number of transmitting antennas in a receiver detection range does not exceed the number of its receiving antennas (i.e., $\sum K_x \le N_r$). Hence, AQR limits the maximum number of transmitters within the receiver detection range to $N_r$ transmitters if each one uses one transmitting antenna. However, in dense VANETs, it is common for a receiver to have more than $N_r$ nearby transmitters. Therefore, some of them may remain blocked during an ongoing transmission, despite the cross-layer using the transmit AS mechanism described in the previous section.

In what follows, we address this problem in terms of transmit power adaption. Researchers
have investigated power adaptation in VANETs. Article [132] propose a transmit power algo-
rithm to solve node isolation in rural zones and keep the connection time longer. The criteria
used for power adaption is the neighbors’ density. The algorithm increases or decreases trans-
mission power so that the number of neighbors of each node is always within a minimum and
maximum threshold. We adapt this TPA algorithm to our MU-MIMO VANETs using transmit
AS so that the number of simultaneous transmission in the same detection zone will satisfy the
AQR condition.

<figure><img src="image_46.png" /><figcaption>Figure 5.3: Transmission power adaptation (TPA), (a) is the scenario without TPA where there are four interfered vehicles, and (b) is a scenario using TPA and only has one interfered vehicle.</figcaption></figure>

Let us refer to Figure 5.3 for describing the scenario of using the TPA algorithm in dense
VANETs. We deploy VANETs using 4 × 4 MIMO systems, which means each vehicle will be
equipped with four transmit antennas and four receive antennas. In Figure 5.3(a), the trans-
mitter T uses the maximum transmit power level. As seen, this creates an unnecessary broad
transmission range and thus a large number of interfered vehicles (four vehicles in this case).
If all transmitter (T, I₁, I₂, I₃, and I₄) want to transmit simultaneously, the total number
of transmit antennas in one of the receiver detection zones may be more than the number
of receive antennas; hence the violation of the AQR condition. However, in Figure 5.3(b), T
reduces the transmit power, and therefore the number of interfered vehicles is also reduced (to one vehicle in this case). Thus these two vehicles are allowed to communicate simultaneously because it respects the AQR condition. An adapted transmit power level, decreases the number of blocked neighbors to mitigate the blocking problem.

For this purpose, we propose to add a PHY/MAC cross-layer design where the MAC layer collects the receiver's neighborhood information, which is then used by the PHY layer for the transmission power adaptation. This adaptation is made progressively with a step off $\Delta$ to keep the connectivity between the vehicles. Each transmitter adapts its transmit power $P_t$ according to its corresponding receiver's neighborhood density, noted $N(R)$. It starts with an initial power $P_{ini}$ and then it increases or decreases the power level by $\Delta$ without exceeding the maximal transmission power $P_{max}$, as described by algorithm 1, where $P_t(t.T_c)$ is the transmit power level during the $t^{th}$ period of $T_c$.

<table><caption>**Algorithm 1:** Transmit power adaptation</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Input:</td><td>$N(R)$, $\Delta$, $T_c$, $P_{ini}$, $P_{max}$, $P_{min}$, $t$;</td></tr><tr><td>Output:</td><td>$P_t$;</td></tr><tr><td>if</td><td>$t=1$ then</td></tr><tr><td></td><td>$P_t(1.T_c) = P_{ini}$;</td></tr><tr><td>else</td><td></td></tr><tr><td></td><td>if $N(R) > N_r$ then</td></tr><tr><td></td><td>$P_t(t.T_c) = \max\{P_t((t-1).T_c) - \Delta, P_{min}\}$;</td></tr><tr><td></td><td>else</td></tr><tr><td></td><td>if $N(R) = N_r$ then</td></tr><tr><td></td><td>$P_t(t.T_c) = P_t((t-1).T_c)$;</td></tr><tr><td></td><td>else</td></tr><tr><td></td><td>$P_t(t.T_c) = \min\{P_t((t-1).T_c) + \Delta, P_{max}\}$;</td></tr><tr><td></td><td>end</td></tr><tr><td></td><td>end</td></tr><tr><td></td><td>end</td></tr></tbody></table>

Indeed, if $N(R)$ exceeds $N_r$ the transmitter decreases the power with a step of $\Delta$ to decrease the communication range and minimize the number of interfering vehicles so that the neighboring vehicles are still allowed to communicate simultaneously because it respects the AQR condition. If $N(R)$ is less than $N_r$, it increases the transmit power with a step-off $\Delta$ to
expand the communication range to improve the transmission links and network throughput. Otherwise, it maintains the same power level. This process is repeated after each round of $T_c$ period and until the end of the transmission.

### 5.3.6 Cross-Layer Design Based on TAS and TPA

In this section, we propose a MAC protocol for VANETs adapted from [76, 77], to support the cross-layer design based on transmit AS and TPA. The use of the AS algorithm proposed above requires an exchange of antenna list between the vehicles. The RTS/CTS control packets used in the IEEE 802.11p MAC protocol are not sufficient. We proposed a new MAC protocol using new control packets and RTS/CTS packets, to be implemented in the VANETs networks. This protocol is broken down into four phases: a phase to exchange the AS list, a phase for the negotiation of the number of antennas used by each vehicle between neighboring vehicles, a phase for simultaneous data transmission, and a phase for transmitting power adaptation. To describe the protocol, let us consider again the V2V communication scenario presented in Figure 5.2, where $V_2$ acts as a receiver and detects three other vehicles, i.e. $V_1$, $V_3$ (an exposed node) and $V_4$ (a hidden node) that want to communicate simultaneously. We focus on the exposed node problem of $V_3$, and describe how it can transmit simultaneously with $V_1$ to $V_2$, and at the same time, it still can communicate with $V_5$, hence it will not be a deferred node. The protocol advances through the following phases:

**AS list exchange phase.** This phase is also defined as the handshake step, where vehicles share their AS list with the neighbors. Assuming that $V_1$ is the first to have the channel access, it sends a transmission request of request-of-antenna-selection-list (RASL) to $V_2$, and $V_2$ responds with request-for-antenna-selection-list-acknowledgment (RASL-ACK) to accept the connection. Then, $V_1$ broadcasts a training sequence to all vehicles in its transmission area, to allow them to estimate the channel between them and $V_1$. The estimation of the channel by the neighbors will allow them to cancel any interference during the communication between $V_1$ and $V_2$. Based on the channel estimation, $V_2$ also looks for the best-transmitting antenna subsets of 1, 2, ...$N_t$ at $V_1$ in terms of maximum throughput calculated using equation 5.12 and 5.13. Then, it creates a list of sorted AS with the number of antennas, the identifiers of the antennas, and the corresponding throughput, as described in Figure 5.3. $V_2$ retransmits this list via antenna-selection-list (ASL) to $V_1$. This list is then broadcast by $V_1$ to the neighbors via broadcast-AS-RTS (B-RTS). Whenever a transmitter receives an AS list from the neighborhood, it forwards the AS list to its receiver. This phase is repeated successively until all
vehicles exchange their AS lists (in this example, between $V_3$ and $V_5$). Figure 5.4 details the packet exchange and the examples of control packets of this phase.

<figure><img src="image_47.png" /><figcaption>Figure 5.4: The AS list exchange phase and its control packet examples. $V_1$ and $V_3$ act as the transmitters, while $V_2$ and $V_5$ act as the receiver.</figcaption></figure>

**Negotiation phase.** This phase aims to negotiate the number of transmitting antennas used by each vehicle to allow simultaneous transmission and ensure the AQR condition. Assuming that $V_1$ first has access to the channel, it verifies whether it reaches the AQR. At first, the AQR is not violated, so $V_1$ sends broadcast-CTS (B-CTS) to all neighboring vehicles to use all of its antennas, in this case, four antennas. When the channel is released, $V_5$ accesses in turn to the channel for the negotiation phase. $V_5$ know that AQR is violated, so $V_5$ selects another combination from the AS list to satisfy the AQR, and it maximizes the network throughput, based on previous B-RTS. It chooses two antennas for $V_1$ ($\eta_2$) and one antenna for $V_3$ ($\eta_1$). Once it is done, it broadcasts the selected list to $V_3$ and the neighbors ($V_2$ and $V_1$) via B-CTS. Then, $V_3$ and $V_1$ updates the AS list accordingly and informs the neighborhood with RTS. Figure 5.5 details the AS list negotiation phase and also the examples of control packets of this phase.
<figure><img src="image_48.png" /><figcaption>Figure 5.5: The negotiation phase and its control packet examples.</figcaption></figure>

**Data transmissions phase.** In this phase, $V_1$ and $V_3$ send a CTS respectively to $V_2$ and $V_5$, and start to send data simultaneously through the final subsets of selected antennas. Then $V_2$ and $V_5$ extract the data using the ZFVBLAST detector, as described in section 5.3.4. The lost and damaged packets are retransmitted using the SR protocol, and received packets are acknowledged with ACK. The V2V between vehicles lasts for each $T_c$ round. When the connection is over, it waits until the channel is fully released before going on to the next step.

**Transmit power adaptation phase.** At this phase, if $V_1$ and $V_3$ continue to transmit data; they account for the number of AS list at their disposal. This stage will give them the neighbor's density of their respective receivers, and depending on this, and they choose the new transmit power level for the next $T_c$ period, as mentioned in section 2.5. The four phases of the proposed MAC protocol are summarized in Algorithm 2.

## 5.4 Performance Analysis

### 5.4.1 Simulation Environment

We use SUMO (Simulation of Urban MObility), MATLAB (MATrix LABoratory), and TraCI4Matlab [133] as the simulation tools. We choose SUMO to generate the mobility of the vehicles in our scenario, MATLAB to modeling our MU-MIMO VANETs system, and also to calculate the performance of our proposed cross-layer design, and TRACI4Matlab as an
<caption>**Algorithm 2:** Antenna selection with transmit power adaptation</caption>

while Vehicles ask for data transmission do
    if distance ≤ communication range then
        AS list exchange phase;
    else
        if AQR is not satisfied then
            Negotiation phase;
        else
            repeat
                Simultaneous data transmission;
                until the end of T<sub>c</sub>;
                Wait until all transmission are finished;
            end
            Receiver's neighbors' density estimation;
            Transmit power adaptation;
        end
    end
<figure><img src="image_49.png" /><figcaption>Figure 5.6: Manhattan grid for simulation in SUMO environment.</figcaption></figure>

interface between SUMO and MATLAB. We design a V2V VANETs scenario where all vehicles distributed randomly in a predictable area of 100m × 100m Manhattan grid (as depicted in Figure 5.6), $N_t = N_r = 4$ antennas (or 4 × 4 MIMO systems) equipped in each vehicle and the maximum relative velocity between vehicles is 40km/h. In this research, using Monte Carlo simulation, we evaluate the performance of the proposed cross-layer design in terms of normalized network throughput versus signal-to-noise ratio (SNR) in term of $E_b/N_0$. To get the network throughput value, we calculate the average throughput of each vehicle over the number of simulations and the number of potential V2V communication links. Then the network throughput value is normalized by dividing with the maximum value. Unless otherwise specified, the numerical values obtained in the rest of this research are all based on network parameters listed in Table 5.2.
<table><caption>Table 5.2: Simulation parameters for cross-layer design using TAS and TPA</caption><thead><tr><th>Parameter</th><th>Symbol</th><th>Value</th></tr></thead><tbody><tr><td>Simulation Scenario</td><td></td><td>Manhattan grid</td></tr><tr><td>Simulation Area</td><td></td><td>100m × 100m</td></tr><tr><td>Simulation Time</td><td>T<sub>sim</sub></td><td>50 s</td></tr><tr><td>Number of Simulations</td><td></td><td>30</td></tr><tr><td>Carrier Frequency</td><td>f</td><td>5.9 GHz</td></tr><tr><td>Number of Vehicles</td><td></td><td>40</td></tr><tr><td>Relative velocity</td><td>v</td><td>40 km/h</td></tr><tr><td>Time coherence</td><td>T<sub>c</sub></td><td>4.6 ms</td></tr><tr><td>Network Configuration</td><td></td><td>Single hop</td></tr><tr><td>Channel Model</td><td></td><td>Rayleigh flat fading</td></tr><tr><td>Frame Length</td><td>L</td><td>180 BPSK symbols</td></tr><tr><td>ARQ Protocol</td><td></td><td>Selective Repeat (SR)</td></tr><tr><td>Throughput</td><td>η</td><td>K(1 − PER) [123]</td></tr><tr><td>Symbol Duration</td><td>T<sub>s</sub></td><td>8 µs</td></tr><tr><td>Initial Power</td><td>P<sub>ini</sub></td><td>30 dBm</td></tr><tr><td>Maximal Transmission Power</td><td>P<sub>max</sub></td><td>30 dBm</td></tr><tr><td>Step of power</td><td>Δ</td><td>5 dBm</td></tr><tr><td>Receiver Sensitivity</td><td>R<sub>s</sub></td><td>-70 dBm</td></tr><tr><td>Transmitting Antennas Gain</td><td>G<sub>t</sub></td><td>0 dBi</td></tr><tr><td>Receiving Antennas Gain</td><td>G<sub>r</sub></td><td>0 dBi</td></tr><tr><td>Path Loss Exponent</td><td>α</td><td>3</td></tr></tbody></table>
<figure><img src="image_50.png" /><figcaption>Figure 5.7: Vehicle mobility scenario, (a) is the four vehicles scenario where $R_2$ will have a receiver blocking problem, and (b) is the eight vehicles scenario where the blocking problem will get worse</figcaption></figure>

## 5.4.2 Simulation Results

First, we describe the blocking problem in dense VANETs. Let us refer to the vehicle mobility scenario in Figure 5.7 to describe how the transmitter AS mitigates the blocking problem, but it still has the limitation in dense VANETs environment. Assuming that each vehicle is using four transmit antennas and four receive antennas (4 × 4 MIMO system), in scenario 5.7(a), we can see only two vehicles (i.e., $T_1$ and $T_2$) detected in the detection range of $R_1$. Using AS architecture with interference-free symbol detection and considering AQR condition, we assume $T_1$ and $T_2$ are doing transmission simultaneously, and each transmitter will use two transmit antennas. The total potential V2V communication links in this scenario are 3. In the denser VANETs scenario, like the eight vehicles scenario in scenario 5.7(b), we can see four vehicles (i.e., $T_1$, $T_2$, $T_3$, and $T_4$) detected in the detection range of $R_1$. We assume $T_1$, $T_2$, $T_3$, and $T_4$ will transmit simultaneously and each transmitter will use only one transmit antenna to satisfy the AQR condition. The total potential V2V links in this scenario are 7. We calculate the network performance in terms of the normalized throughput per potential V2V-links based on equation 5.12 and 5.13.

In Figure 5.8, compared to without the AS algorithm, we can see that the AS algorithm gives better network throughput performance, and using SR protocol gives slightly better per-
<figure><img src="image_51.png" /><figcaption>Figure 5.8: The throughput is degrading in dense VANETs because there was still a receiver blocking problem even though we used the proposed AS selection.</figcaption></figure>

performance than using GBN. Also, the four vehicles scenario has better throughput than eight vehicles scenario. Indeed, dense VANETs has more V2V links, which results in frequent violation of the AQR condition. Some transmitters are blocked, and the blocking problem reappears; hence the network throughput performance decrease.

Now, we want to investigate our proposed cross-layer design by adding the TPA algorithm. In Figure 5.9, we provide the throughput performance in the following cases: (i) without AS and TPA, (ii) with AS and without TPA, (iii) with AS and TPA. Note that when we do not use the TPA algorithm, the transmit power is by default $P_{ini} = P_{max}$ and we do not consider the transmit power adaptation phase of the MAC protocol. First, to see the cross layers gain based on AS, let us compare the throughput obtained in the cases (i) and (ii). In (ii), with AS, at lower of $E_b/N_o = 10$dB, the throughput grows up to more than 1.73 times of that given without AS in (i). However, at better of $E_b/N_o = 20$dB, the throughput reaches until 6% of gain than that given without AS. In fact, at low of $E_b/N_o$, the AS algorithm tends to choose less number of transmit antennas. However, at a high $E_b/N_o$, the optimal subset of all available transmit
<figure><img src="image_52.png" /><figcaption>Figure 5.9: Throughput without antenna selection and power adaptation; with antenna selection and without power adaptation; with antenna selection and power adaptation.</figcaption></figure>

antennas can be chosen as long as the AQR is satisfied. As the authors concluded in [76], using a cross-layer based on the TAS algorithm improves each V2V link's throughput. Besides, it allows more nearby transmissions, and therefore, it improves the average network throughput. Now, to see the gain brought by the cross-layer based on transmit power adaptation, we compare the throughput achieved with (case (iii)) and without TPA (case (ii)), both cases with AS. The throughput given by AS with TPA is higher, especially for low noise power level. At of $E_b/N_o = 20$dB, the gain can achieve 2%. However, at of $E_b/N_o = 10$dB (i.e., link quality is poor), we recorded a gain of 26%. Indeed, decreasing the transmit power does not affect the throughput per each V2V-link, but it increases the average network throughput. In sum, compared to the conventional case, at of $E_b/N_o = 10$dB, the performance improvement is about 173% for AS without TPA, whereas it is about 244% for AS with TPA. At of $E_b/N_o = 20$dB, the performance improvement is 6% for AS without TPA and 9% for AS with TPA.

In Figure 5.10, we evaluate the proposed approach with two different network density. The throughput improvement with TPA is remarkable in both scenarios (i.e., 40 and 100 vehicles).
<figure><img src="image_53.png" /><figcaption>Figure 5.10: Throughput in a network for 40 and 100 vehicles.</figcaption></figure>

The most significant improvement comes from the dense network where our proposed solution mitigates the blocking problem; hence, more vehicles can transmit data simultaneously.

In Figure 5.11, we study the impact of the total number of transmitting and receiving antennas on our proposed design. As illustrated, the 8 × 8 MIMO system gives better network throughput performance compared to 4 × 4 MIMO systems for both 40 and 100 vehicles scenario. The AQR condition depends on the total number of the receive antennas; thus, the more receive antennas means more transmit vehicles doing simultaneous transmission. Hence, in terms of normalized average throughput per V2V link, the 8 × 8 MIMO system in 100 vehicles scenario gives the best performance, which proves our proposed cross-layer design overcame the blocking problem in dense VANETS. Indeed, the more antennas mean more vehicles can transmit simultaneously. However, it also means more time needed to synchronize between vehicles. We can see that the time needed to do the negotiation phase in our MAC protocol also increase. It is interesting to add the delay parameter as one of the performance analysis in our future research.
<figure><img src="image_54.png" /><figcaption>Figure 5.11: Throughput comparison for 4 × 4 MIMO system and 8 × 8 MIMO system in 40 and 100 vehicles scenario.</figcaption></figure>

## 5.5 Conclusion

This research has proposed a solution to mitigate the blocking problem in dense VANETs environment. We consider using MIMO with ZF-VBLAST architecture in the PHY layer and retransmission using SR protocol in the MAC layer. This solution also uses TAS and TPA algorithms. This design lets the transmitter choose the best combination of transmitting antennas to maximize throughput and minimize interferences. Furthermore, it allows transmitters to send data simultaneously, with its interference-free symbol detection, which improves the network performance in terms of throughput. The simulation results show that this solution allows more vehicles to communicate simultaneously and significantly improves the average network throughput compared to the 802.11p MAC standard, particularly for VANETs with high density.

It should be noted that this solution can be used only for the VANETs system using uni-cast transmission because the transmitter needs the CSI before applying the TAS and TPA algorithm. Another drawback of this proposed cross-layer design is the use of RTS/CTS, which
needed to overcome the hidden node problem, at the expense of increasing the transmission
delay or latency due to the synchronization process before data transmission.<fnref n="1" />

<footnote n="1">¹This chapter is a slightly modified version of A PHY/MAC cross-layer design with transmit antenna selection and power adaptation for receiver blocking problem in dense VANETs. [134] published in Vehicular Communications Journal and has been reproduced here with the permission of the copyright holder.</footnote>
# General Conclusion and Perspectives

This thesis's work has been focused on studying the cross-layer design for next-generation VANETs while taking into account some matured technique from other 802.11 family standards. We were mainly interested in cross-layer designs in PHY, MAC, and NET layers, while considering two objectives stated in the NGV's PAR, i.e., having two times higher throughput in the MAC layer and operating in the higher mobility environments with vehicles' speed up to 500 km/h. We also use a cross-layer design approach to address the blocking problem caused by the CSMA/CA technique used in V2X communication.

## General Conclusion

The main results of this thesis are summarized as follows:

* In the first chapter, we have presented the state of the arts of the VANETs communication system. Furthermore, we have given a brief general overview of the standards, challenges, applications, and evolution of the VANETs. Cross-layer designs and MIMO techniques motivated us to improve the performance of the VANETs communication system.

* In the second chapter, we choose the PER and throughput as our performance metrics. The PER is simulated and compared against the theoretical PER calculation. We justified the OFDM technique's objective, where the performance of an OFDM system with a perfect frequency domain equalization over a frequency-selective channel is equivalent to the performance over the flat-fading channel. Our simulation result shows that if we want to have PER of 10%, we need SNR of 24 dB for BPSK, 26 dB for QPSK, 32 dB for 16-QAM, and more than 35 dB for 64-QAM. Indeed, a higher value of SNR is needed for the higher modulation order.

We simulate the throughput performance of a small size frame (100 Bytes) and use a minimum value of CW = 15 to show the worst possible condition where the preamble overhead is high, and the probability of collision is also high. We also highlighted two
problematics regarding the throughput performance of the legacy standard IEEE 802.11p in the high mobility environment, i.e., the impact of frame size and data rates (MCS) and the impact of vehicle density on the VANETs performance. Our simulation results showed a trade-off between frame size, MCS, and the NGV applications' performance, where bigger frame size and higher MCS improved the throughput performance but reduced the reliability due to the delay and PER. On the contrary, using a smaller frame size will reduce the throughput efficiency due to packet preamble overheads. Concerning the impact of the vehicle density, our simulation results showed the MAC layer's throughput degradation in a dense VANETs environment due to the CSMA/CA technique's blocking problem.

* Chapter 3 investigated the possible PHY layer enhancements to be possibly adopted into the 802.11bd standard for NGV communication. We considered some mature techniques from other 802.11 family standards. There are three PHY layer design considerations to be implemented in the 802.11bd standard, i.e., LDPC and midambles, DCM and extended-range mode, and MIMO-STBC. Our simulation results show a significant PER and throughput performance improvement resulting from the introduction of all techniques compared to the legacy 802.11p standard. It should be noted that the DCM technique reduced the channel capacity in half at the expense of having reliable communication in the worst SNR condition. LDPC and midambles are recommended for all V2X applications because it can give a better channel tracking mechanism that leads to up-to-date channel estimation to minimize error probability of the received signal at the receiver side. The use of DCM and extended-range mode is recommended for V2X applications that need a higher reliability requirement because it gave better performance even in the worst SNR condition and improved the communication range. Finally, MIMO-STBC is recommended for V2X applications that need higher throughput requirements in the unicast transmission scenario.

* Chapter 4 designed the potential 802.11bd standard using a cross-layer approach in PHY, MAC, and NET layers. We simulated our cross-layer designs for the NGV 802.11bd standard using 16-QAM with a code rate of 1/2 modulation in an enhanced highway LOS environment. For the ideal condition of one transmission without interference, the safety NGV application using a frame size of 300 Bytes could obtain 2.05x higher throughput. In comparison, the non-safety-related NGV applications using a frame size of 1500 Bytes could obtain 2.04x higher throughput compared to theoretical data rates in 802.11p standard (12 Mbps). Our cross-layer design answered two objectives requested by the PAR of
802.11bd standard, i.e., having two times higher throughput in the MAC layer and operating in a higher mobility environment with a relative speed of 500 km/h. The cross-layer design also gave a better throughput performance in a dense environment where many vehicles compete on the same channel.

* In chapter 5, we proposed a solution to mitigate the blocking problem in dense VANETs environment. We consider using MIMO with ZF-VBLAST architecture in the PHY layer and retransmission using SR protocol in the MAC layer. This solution also used TAS and TPA algorithms. This design lets the transmitter chose the best combination of transmitting antennas to maximize throughput and minimize interferences. Furthermore, it allows transmitters to send data simultaneously, with its interference-free symbol detection, which improves the network performance in terms of throughput. The simulation results show that this solution allows more vehicles to communicate simultaneously and significantly improves the average network throughput compared to the 802.11p MAC standard, particularly for VANETs with high density.

## Perspectives

Despite the results that have been proposed in this thesis, there are still several aspects that could be further investigated in future works. Some of the related topics for future research are highlighted as follows:

* In chapter 3, we considered only two objectives defined by the NGV's PAR. The first objective is achieving at least two times higher throughput (measured at the MAC data service access point) than the maximum mandatory data rate as defined in the 5.9 GHz band (12 Mb/s in a 10 MHz channel). The second objective is operating in high mobility channel environments at vehicle speeds up to 250 km/h (closing speeds up to 500 km/h). The simulation results showed that our cross-layer design answered these two objectives. We could implement our cross-layer design for future work to do real-world field testing and measurement campaign.

There is another objective defined by the NGV's PAR, such as at least one mode that achieving 3dB lower sensitivity level (more extended range) than that of the lowest data rate defined in IEEE 802.11TM-2016 operating in 5.9 GHz band (3 Mb/s in a 10 MHz channel); and providing interoperability, coexistence, backward compatibility, and fairness with deployed OCB (Outside the Context of a BSS) devices using IEEE 802.11p. For future work, we need to investigate the cross-layer design to address those objectives.
* In chapter 4, we only considered how to improve the throughput performance using the cross-layer design. However, other performance metrics are needed to consider, such as delay, latency, or the reliability of the NGV applications. For example, although a larger CW and retransmission technique could improve the throughput performance, we need to find an optimum CW and the number of retransmission due to both techniques increasing the delay latency of frame transmission. We also considered only the broadcast and unicast single-hop transmission in the NET layer. There are other scenarios to consider like multicast, geocast, geo networking in ETSI ITS-G5 standard, and a multi-hop transmission, where we need to consider hidden node problems in the MAC layer protocol. For future work, we need to consider all the possibilities like broadcast, unicast, or multicast (geocast) in single and multi-hop transmission to have a better routing strategy for future NGV applications.

* Chapter 5 investigated that the proposed cross-layer design can be used only for the VANETs system using unicast transmission because the transmitter needs the CSI before applying the TAS and TPA algorithm. Another drawback of this proposed cross-layer design is the use of RTS/CTS, which is needed to overcome the hidden node problem, at the expense of increasing the transmission delay or latency due to the synchronization process before data transmission. This solution is limited only to close-range low-speed NGV applications, e.g., urban low-speed V2I high definition map update. We need further investigation to know this solution's performance in other VANETs environments by considering the delay or latency performances. Furthermore, the NGV PAR also considered the use of the 60 GHz frequency band optionally. We could consider other MIMO techniques such as beamforming that could give the benefit to improve the future NGV performances.
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