Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic community and the industrial community due to their diverse military applications and civilian applications. Furthermore, UAVs are also envisioned to be part of future airspace traffic. The application functions delivery relies on information exchange among UAVs as well as between UAVs and ground stations (GSs), which further closely depends on aeronautical channels. However, there is a paucity of comprehensive surveys on aeronautical channel modeling in line with the specific aeronautical characteristics and scenarios. To fill this gap, this paper focuses on reviewing the air-to-ground (A2G), ground-to-ground (G2G), and air-toair (A2A) channel measurements and modeling for UAV communications and aeronautical communications under various scenarios. We also provide the design guideline for managing the link budget of UAV communications taking account of link losses and channel fading effects. Moreover, we also analyze the receive/transmit diversity gain and spatial multiplexing gain achieved by multiple-antenna-aided UAV communications. Finally, we discuss the remaining challenge and open issues for the future development of UAV communication channel modeling.
Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.
Unmanned Aerial Vehicles (UAVs) are envisioned to be an important part of the device-centric Internet-of-Things (IoT). These bespoke Unmanned Aircraft Systems (UASs) that support UAVs significantly differ from traditional terrestrial and aeronautical networks, both of which are evolving towards their next-generation forms. The major challenges of the UAS include (1) the augmented interference due to strong Line-of-Sight (LoS) (2) the dynamic shadowing effects owing to 3-D aerial maneuvering, (3) the excessive Doppler shift owing to high UAV mobility as well as (4) the Size, Weight, And Power (SWAP) constraints. Against this background, we propose to invoke the recently developed coherent/non-coherent Spatial Modulation (SM) and its diversity-oriented counterpart of Space-Time Block Coding using Index Shift Keying (STBC-ISK). These arrangements employ multiple Transmit Antennas (TAs) in order to improve the network's Quality-of-Service (QoS), but they only use a single RF chain. Furthermore, based on the throughput, delay and power-efficiency, we conceive a novel three-fold adaptivity design, where the UAS may adaptively (I) switch between coherent and non-coherent schemes, (II) switch between single-and multiple-TA based arrangements as well as (III) switch between high-diversity and high-spectral-efficiency multiple-TA based schemes.L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/Noo4558/1,
Abstract-This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-input multipleoutput (MIMO) aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on an optimal pilot design conceived for time-domain channel estimation, which can either completely eliminate PC or significantly reduce it, depending on the channel's coherence time. This is achieved by designing an optimal pilot set to allowing us to beneficially group the users in all the cells and to assign a time-shifted pilot transmission to the different groups. Unlike the existing PC elimination schemes which require an excessively long channel coherence time, our proposed scheme is capable of completely eliminating PC under a much shorter coherence time. Moreover, the existing PC elimination schemes can no longer be used if the channel coherent time is insufficiently large. By contrast, even for extremely short channel coherent time, our scheme can still be implemented to significantly reduce PC. This is particularly beneficial for high velocity scenarios. Our simulation results demonstrate the efficiency of the proposed scheme.
From the power amplifier's perspective, the Peak-to-Average Power Ratio (PAPR) is of essential importance, especially for both single-RF and reduced-RF Multiple-Input Multiple-Output (MIMO) single-carrier schemes. In this context, many of the diversity-oriented index modulation schemes -including the full-RF Space-Time Shift Keying (STSK) and the single-RF Asynchronous STSK (ASTSK) that invoke randomized signals -exhibit eroded energy-efficiency. To circumvent this problem, we propose a holistic signal construction approach for single-RF, reduced-RF and full-RF MIMO setups, which always achieve both perfect 0 dB PAPR transmission and Inter-Channel Interference (ICI) free signal detection. More explicitly, first of all, we conceive a new family of single-RF Constant-Envelope ASTSK (CE-ASTSK), which is capable of substantially outperforming conventional Spatial Modulation (SM) in both Rayleigh fading and Ricean fading associated with increasing Line-of-Sight (LoS) power. Secondly, we propose the new full-RF CE-STSK concept, which is capable of outperforming the orthogonal Space-Time Block Codes (STBCs) without either increasing PAPR or imposing ICI. This is particularly beneficial because the conventional Linear Dispersion Code (LDC) approaches always compromise the orthogonality of STBC and hence impose ICI. Thirdly, we also conceive the reduced-RF versions of CE-STSK, which outperform both Generalized Spatial Modulation (GSM) and Space-Time Block Coded Spatial Modulation (STBC-SM). Finally, the proposed schemes are intrinsically amalgamated with turbo detection assisted channel coding, which further confirms the superiority of CE-ASTSK and CE-STSK over SM and STBC in the single-RF and full-RF modes, respectively.
Citation Zhang L, Zhao H, Pan G, et al. Secure analysis over generalized-K channels. Sci China Inf Sci, for reviewDear editor, The secrecy outage probability (SOP) is used to estimate the secrecy outage performance when the transmitter has no state information about potential wiretap channels [1], i.e., silent eavesdropping. Most previous work, such as [2,3], has focused on the SOP defined in [1], which we call the conventional SOP in this letter, where both unreliable transmission from the transmitter to the legitimate receiver (i.e., outage) and information leakage to eavesdroppers are considered to be secrecy outage. Thus, according to the conventional SOP definition, a secrecy outage does not necessarily imply that any information has been leaked to eavesdroppers. To capture the actual information leakage, [4] proposed a new SOP definition, which we call the proposed SOP in this letter, that is exactly the information leakage probability. However, they only considered the SOP over Rayleigh fading channels, a simple small-scale channel model. In actual wireless communications scenarios, shadowing is typically involved, resulting in large-scale fading [5].The generalized-K (GK) fading model was proposed [6] to capture composite fading channels (with both small-and large-scale fading), but the exact model involves modified Bessel functions of the second kind, which usually results in Meijer's G-function or more advanced special functions appearing in the final SOP expression [7]. It is still a matter of debate as to whether the Meijer's Gfunction can be viewed as a closed-form expression.To avoid this function, [8] simplified the GK model by using a mixed Gamma distribution.Although the SOP over GK fading channels has already been investigated [2,7], the authors in [2,7] only considered the conventional SOP definition, which does not give the actual information leakage probability. Moreover, they did not investigate the asymptotic performance when the main link's signal-to-noise ratio (SNR) is sufficiently large, which gives the secrecy diversity order and array gain [3]. Others have studied the SOP's asymptotic behavior [9], but their conclusions as to its secrecy diversity order are not valid in the general case.In this letter, we adopt the SOP definition in [4] and the simplified model of [8], and derive a closed-form expression for the proposed SOP over GK fading channels. To simplify this expression and obtain additional insights, we also perform an asymptotic analysis of the main link in the high-SNR region.System Model. In the standard Wyner model [1], a source transmits confidential messages to a destination d. Meanwhile, an eavesdropper e wants to overhear this information. Here, we assume that all links undergo independent GK fading. The exact probability density function (PDF) γ t of the instantaneous SNR at t (t ∈ {d, e}) is given by [7] f γt (x) = G 2,0 0,2
The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multipleinput multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33% compared with the one attained without optimizing power sharing factors.
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