Hidden terminal is one of the major problems in ad hoc networks. In addition, there are scenarios where the destination of interest is located in the radio range of other transmitters, so that the efforts on establishing data communication with this mobile station will fail due to collisions that may occur between transmitted control frames and undesired received control and data frames. This phenomenon becomes a bottleneck when most of data transmissions experience packet fragmentation. In such scenarios, the desired destination becomes unreachable during the data transfer of neighboring nodes. Using the same PHY of IEEE 802.11 and making slight modifications in its MAC, a new medium access control scheme is presented to address such problems. The performance of the proposed approach is compared with earlier schemes through simulation-based evaluations, showing performance enhancement due to considerable better handling of unreachability issues in the proposed MAC scheme
The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.
This paper introduces a novel approach to evaluate the performance of ad hoc networks based on their spatial capacity, defined as the maximum spatial spectral efficiency supported by the network while a zero outage probability is guaranteed for all communication links. Specifically, the spatial capacity together with upper and lower bounds is derived in closed-form for networks where transmitters follow a Poisson point process and their respective receivers are located at a fixed distance. In this scenario, the spatial capacity is achieved using a rate adaptation technique that adjusts the link spectral efficiency in accordance with the distance between the receivers and their closest interferers. Besides, the spatial capacity is analytically proved to be always greater than or equal to the maximum spatial spectral efficiency achieved when a fixed spectral efficiency and an unbounded outage probability are considered. Numerical results show that the spatial-capacity-achieving setting leads to a spatial spectral efficiency about 125% higher than the one reached with the fixed spectral efficiency strategy.
The cross-layer utility maximization problem, which is subject to stability constraints for a multicommodity wireless network where all links share the same number of orthogonal channels, is considered in this paper. We assume a time-slotted network, where the channel gains randomly change from one slot to another. The optimal cross-layer network control policy can be decomposed into the folloing three subproblems: 1) flow control; 2) next-hop routing and in -node scheduling; and 3) power and rate control, which is also known as resource allocation (RA). These subproblems span the layers from the physical layer to the transport layer. In every time slot, a network controller decides the amount of each commodity data admitted to the network layer, schedules different commodities over the network's links, and controls the power and rate allocated to every link in every channel. To fully exploit the available multichannel diversity, we consider the general case, where multiple links can be activated in the same channel during the same time slot, and the interference is controlled solely through power and rate control. Unfortunately, the RA subproblem is not yet amendable to a convex formulation, and in fact, it is NP-hard. The main contribution of this paper is to develop efficient RA algorithms for multicommodity multichannel wireless networks by applying complementary geometric programming and homotopy methods to analyze the quantitative impact of gains that can be achieved at the network layer in terms of end-to-end rates and network congestion by incorporating different RA algorithms. Although the global optimality of the solution cannot be guaranteed, the numerical results show that the proposed algorithms perform close to the (exponentially complex) optimal solution methods. Moreover, they efficiently exploit the available multichannel diversity, which provides significant gains at the network layer in terms of end-to-end rates and network congestion. In addition, the assessment of the improvement in performance due to the use of multiuser detectors at the receivers is provided.
Geolocation methods for HIPERLAN/2 OFDM systems were reported previously. This paper continues to study the possibility of overlaying geolocation functions in 802.11 DSSS wireless LANs. In this paper, a delay measurement-based TDOA measuring method is proposed for 802.11 wireless LANs, which eliminate the requirement of initial synchronization in the conventional methods. The performance of the potential overlaid geolocation systems for DSSS and OFDM wireless LANs are analyzed and compared in terms of symbol synchronization performance.
In this paper a proposal to improve the reliability of a downlink multiuser (MU) MIMO transmission scheme is investigated with the use of a new approach in orthogonal space-time block codes (OSTBC) and network coding with a superposition modulated system and side information. With the new encoded OSTBC approach, diversity is offered where each user receives all other users' symbols, which allows the recovery of symbols in several ways. In addition, multiple users can be accommodated with the same resource, which is quite useful in a wireless system where resources are always restricted. By employing superposition modulation, the side information needed for error recovery can be transmitted over the same resource used for the normal information frame. In addition, the proposed system exploits diversity through a novel technique of sub-constellation alignment-based signal combining for efficient side information dissemination. A detailed analysis of the new OSTBC approach is carried out. It is shown that the performance of the MU-MIMO system can be improved significantly in terms of block and frame error rates (BLER, FER) considered as reliability measures. By accommodating a reasonable number of multiple users, high reliability is achieved at the expense of the rate. To compensate for the low rate, conventional OSTBC can be considered and simulation results are shown, where, as a penalty to pay, multiple orthogonal resources are required.
Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications. However, such shorter wavelengths are susceptible to higher scattering and path loss forcing the link to depend predominantly on the line-of-sight (LOS) path. Dynamic movement of humans has been identified as a major source of blockages to such LOS links. In this work, we aim to overcome this challenge by predicting human blockages to the LOS link enabling the transmitter to anticipate the blockage and act intelligently. We propose an end-to-end system of infrastructure-mounted LiDAR sensors to capture the dynamics of the communication environment visually, process the data with deep learning and ray casting techniques to predict future blockages. Experiments indicate that the system achieves an accuracy of 87% predicting the upcoming blockages while maintaining a precision of 78% and a recall of 79% for a window of 300 ms.
Block diagonalization (BD) is a downlink multiuser multiple-input multiple-output (MIMO) strategy that utilizes transmitter channel state information (CSI). This paper proposes a practical uplink MIMO scheme for time division duplex (TDD) systems to co-exist with BD, so that the CSI of the BD channels is used also in the uplink transmission. It is shown that the precoded pilot symbols are sufficient in both uplink and downlink to satisfy the needs of both transmission and reception. The capacity of the system is analyzed in conjunction with linear receivers in a time-varying fading channel. According to the results the proposed uplink strategy provides capacity gain over non-precoded transmission, without being sensitive to Doppler.
