Long Range Wide Area Networks (LoRaWANs) have become a popular candidate for energy efficient networks like the Internet of Things (IoT). For the connection of remote regions, satellites in Low Earth Orbit (LEO) can be used to support the terrestrial LoRaWAN. LEO enables transmission from IoT devices with a very low transmit power. However, it also leads to high Doppler shifts as well as high Doppler rates. Both effects deteriorate the Long Range (LoRa) communications performance tremendously. Whereas new LoRa technologies are developed for systems with high Doppler effects, they are not backward compatible, i.e., they cannot be used for LoRa sensors which have already been deployed. In this paper, we propose a Doppler rate mitigation method which reduces these negative effects by subtracting an estimated Symbol Error (SE). Here, estimation is based on an adaptive linear regression method exploiting all LoRa symbols of a packet, compared to state-of-the-art techniques whose linear regression approachs are only using preamble symbols. Simulation results show that the proposed method tremendously outperforms state-of-the-art techniques, especially at high Doppler rates.
Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral and energy efficiency in B5G/6G systems. Channel estimation in RIS-aided systems is quite challenging due to the passive architecture of the RIS. Since RIS consists of a large number of reflecting elements, we need many phase shift allocations during the channel estimation phase to estimate the RIS-associated channels perfectly. In the absence of a sufficient number of pilot or phase shift sequences during the training phase, we get imperfect channel state information (CSI), which can significantly degrade the overall performance of the system. In this work, we analyse the effects of limited pilot and phase shift sequences on the asymptotic performance of the RIS-aided systems. We also optimise the achievable sum-rate of the system in the absence of full pilot and phase shift sequences.
Unmanned aerial vehicle (UAV)-aided communications may suffer from the coexistence with existing network infrastructure and the spectrum scarcity. Underlay cognitive radio technology should be employed to manage the secondary UAV-aided networks with the existing primary networks using the same frequency spectrum, where the interference to the primary network should be bounded. In this paper, we focus on the problem of multi-UAV-assisted underlay cognitive radio for multiple secondary users (SUs) and primary users (PUs). We establish an optimization problem to maximize the minimum throughput among SUs with respect to multi-UAV deployment locations, power and service ratio allocation. The problem is highly intractable due to its mixed-integer and non-convex nature. The problem is solved by decomposing it into two nested optimization problems. The inner problem involves joint power and service ratio allocation given the deployment location and UAV-SU allocation, which we transform equivalently into a convex optimization problem. The outer problem determines the multi-UAV deployment locations and UAV-SU allocation, solved via a metaheuristic method. Via this decomposition with the inner problem optimally solvable in polynomial time by a convex optimization solver, the search space of the metaheuristic algorithm is drastically reduced, resulting in an efficient nearoptimal algorithm for the complex problem. Numerical results demonstrate that the proposed approach achieves significant throughput improvement compared to baseline methods.
Combining sensing and communication into a single system allows the integration of radar sensors mounted on locally installed road side units into future intelligent transportation systems. These units have great potential to improve safety in dense intersection scenarios. However, this combined functionality is limited due to their potential interference. This paper investigates the influences of antenna characteristics on sensing and communication performance in crossing scenarios at 77 GHz. Different antenna characteristics are compared in ray-tracing simulations. The radar sensing performance is evaluated based on the maximum peak detection in the range-Doppler map, while the communication is evaluated based on the channel characteristics. The different antenna types achieve either a higher detection power in the angular region or a higher antenna focus. The communication is compared with respect to the root mean square delay spread and the minimum timing delay for an interferencefree frequency-modulated continuous wave (FMCW) sensing. The power of the line-of-sight path between the radar-equipped road side units varies over 20 dB between the different antennas. The alignment with each other results in even higher gains and losses. The results show that coordinating the different RSUs with an additional time or frequency scheduling is necessary. In the future, the intersection scene will be improved with more road users to investigate the antenna characteristics' impacts further.
This paper provides a solution for the joint activity detection and channel estimation (JADCE) problem in grant-free access with correlated device activity patterns. In particular, we consider a massive machine-type communications (mMTC) network operating in an event-triggered traffic mode. To this end, to encode the prior information about the correlated sparse activity pattern, the paper proposes a first-order Markov chain coupled with a sparsity-promoting spike and slab to capture the sparse and correlated activity pattern. Furthermore, we drive a hierarchical Bayesian parameter estimation using Markov chain Monte Carlo (MCMC) sampling to provide an efficient solution to the JADCE problem. Numerical results highlight improved activity detection accuracy and channel estimation quality performance.
