Decoupled Downlink and Uplink Access for Aerial Terrestrial Heterogeneous Cellular Networks

To enable reliable connectivity in highly dynamic and dense communication environments, aerial-terrestrial heterogeneous cellular networks (AT-HCNs) have been proposed as a plausible enhancement to the conventional terrestrial HCNs (T-HCNs). In dense urban scenarios, users are often located in clusters and demand high bandwidth in both downlink (DL) and uplink (UL). We investigate this scenario and model the spatial distribution of clustered users using a Matern cluster process (MCP). Based on our analysis we then argue that decoupling of DL and UL in such a setting can significantly improve coverage performance and spectral efficiency. We further obtain closed-form expressions for the system coverage probability, spectral efficiency, and energy efficiency by using the Fox H-function. The obtained results confirm the validity of the proposed analytical model. Our simulations further indicate a significant performance improvement using decoupled access and provide quantitative insights on AT-HCN system design

Angular Spread Quantification of Multi-Antenna Vehicular Radio Communication Channels

The deployment of multi-antenna systems with software defined reconfigurable beam patterns can potentially benefit vehicle-to-vehicle (V2V) communications by increasing the channel coherence time. This in turn necessitates an accurate characterization and modeling of the angular statistics of vehicular radio propagation environments. This work proposes an improved three-dimensional (3-D) spatial description of vehicular propagation environments and derives the closed-form analytical expressions for the joint and marginal statistics of the 3-D angle-of-arrival (AoA) and angle-of-departure (AoD). Then, based on the proposed geometric channel model, the AoA and AoD angular spreads are quantified in terms of the joint angular spread, elevational constriction, and the azimuthal constriction. These considered quantifiers are shown to be of high significance in quantification of angular spread in V2V radio propagation environments. The impact of various physical parameters on the angular spread is also investigated. These parameters include the link-distance, scattering volume, and the number of scatterers along the azimuth and elevation axes. The derived analytical expressions are also validated by simulations

The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt from the Development of a 5G Testbed Environment

The capacity and coverage requirements for 5 th generation (5G) and beyond wireless connectivity will be significantly different from the predecessor networks. To meet these requirements, the anticipated deployment cost in the United Kingdom (UK) is predicted to be between £30bn and £50bn, whereas the current annual capital expenditure (CapEX) of the mobile network operators (MNOs) is £2.5bn. This prospect has vastly impacted and has become one of the major delaying factors for building the 5G physical infrastructure, whereas other areas of 5G are progressing at their speed. Due to the expensive and complicated nature of the network infrastructure and spectrum, the second-tier operators, widely known as mobile virtual network operators (MVNO), are entirely dependent on the MNOs. In this paper, an extensive study is conducted to explore the possibilities of reducing the 5G deployment cost and developing viable business models. In this regard, the potential of infrastructure, data, and spectrum sharing is thoroughly investigated. It is established that the use of existing public infrastructure (e.g., streetlights, telephone poles, etc.) has a potential to reduce the anticipated cost by about 40% to 60%. This paper also reviews the recent Ofcom initiatives to release location-based licenses of the 5G-compatible radio spectrum. Our study suggests that simplification of infrastructure and spectrum will encourage the exponential growth of scenario-specific cellular networks (e.g., private networks, community networks, micro-operators) and will potentially disrupt the current business models of telecommunication business stakeholders - specifically MNOs and TowerCos. Furthermore, the anticipated dense device connectivity in 5G will increase the resolution of traditional and non-traditional data availability significantly. This will encourage extensive data harvesting as a business opportunity and function within small and medium-sized enterprises (SMEs) as well as large social networks. Consequently, the rise of new infrastructures and spectrum stakeholders is anticipated. This will fuel the development of a 5G data exchange ecosystem where data transactions are deemed to be high-value business commodities. The privacy and security of such data, as well as definitions of the associated revenue models and ownership, are challenging areas - and these have yet to emerge and mature fully. In this direction, this paper proposes the development of a unified data hub with layered structured privacy and security along with blockchain and encrypted off-chain based ownership/royalty tracking. Also, a data economy-oriented business model is proposed. The study found that with the potential commodification of data and data transactions along with the low-cost physical infrastructure and spectrum, the 5G network will introduce significant disruption in the Telco business ecosystem

Superimposed Training based Estimation of Sparse MIMO Channels for Emerging Wireless Networks

Multiple-input multiple-output (MIMO) systems constitute an important part of todays wireless communication standards and these systems are expected to take a fundamental role in both the access and backhaul sides of the emerging wireless cellular networks. Recently, reported measurement campaigns have established that various outdoor radio propagation environments exhibit sparsely structured channel impulse response (CIR). We propose a novel superimposed training (SiT) based up-link channels’ estimation technique for multipath sparse MIMO communication channels using a matching pursuit (MP) algorithm; the proposed technique is herein named as superimposed matching pursuit (SI-MP). Subsequently, we evaluate the performance of the proposed technique in terms of mean-square error (MSE) and bit-error-rate (BER), and provide its comparison with that of the notable first order statistics based superimposed least squares (SI-LS) estimation. It is established that the proposed SI-MP provides an improvement of about 2dB in the MSE at signal-to-noise ratio (SNR) of 12dB as compared to SI-LS, for channel sparsity level of 21.5%. For BER = 10^−2, the proposed SI-MP compared to SI-LS offers a gain of about 3dB in the SNR. Moreover, our results demonstrate that an increase in the channel sparsity further enhances the performance gai

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A non-random and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature -the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size

Angle and Time of Arrival Characteristics of 3D Air-to-Ground Radio Propagation Environments

A three dimensional (3D) geometric channel model is proposed for ground-to-air (G2A) and air-toground (A2G) communication links. A low-elevated ground station (GS) and a high-elevated air station (AS) are taken at foci points of a virtual bounding ellipsoid corresponded from known knowledge of delay of longest propagation path. The effective region of scatterers around the GS is designed on the basis of this ellipsoid truncated by the average rooftop level (or average height of sea waves) and ground plane. Closedform expressions for joint and marginal probability density functions (PDFs) of angle of arrival (AoA) observed at AS and GS in correspondence with azimuth and elevation angles are derived. Furthermore, closed-form expressions for density of energy with respect to the delay of arriving multipath waves corresponded from both the elevation and azimuth AoA are derived independently when observed from either end of the communication link. Moreover, effect of different physical parameters of the channel on distribution of energy in angular and temporal domains is presented. The comparison of analytical results with results of a notable model is also presented. In order to verify the derived analytical expressions, a comparison of analytical results with the performed simulation results is presented, which shows a good match