Demand-based optimization for adaptive multi-beam satellite communication systems

Satellite operators use multiple spot beams of high throughput satellite systems to provide internet services to broadband users. However, in recent years, new mobile broadband users with diverse demand requisites are growing, and satellite operators are obliged to provide services agreed in the Service Level Agreements(SLA) to remote rural locations, mid-air aeroplanes and mid-ocean ships. Furthermore, the expected demand is spatio-temporal which varies along the geographical location of the mobile users with time and hence, creating more dynamic, non uniformly distributed, and time sensitive demand profiles. However, the current satellite systems are only designed to perform similarly irrespective of the changes in demand profiles. Hence, a practical approach to meet such heterogeneous demand is to design adaptive systems by exploiting the advancements in recently developed technologies such as precoding, active antenna array, digital beamforming networks, digital transparent payload and onboard signal processing. Accordingly, in this work, we investigate and develop advanced demand-based resource optimization modules that fit future payload capabilities and satisfy the satellite operators’ interests. Furthermore, instead of boosting the satellite throughput (capacity maximization), the goal is to optimize the available resources such that the satellite offered capacity on the ground continuously matches the geographic distribution of the traffic demand and follows its variations in time. However, we can introduce adaptability at multiple levels of the transmission chain of the satellite system, either with long term flexibility (optimization over frequency, time, power, beam pattern and footprint) or short term flexibility (optimization over user scheduling). These techniques can be optimized as either standalone or in parallel or even jointly for maximum demand satisfaction. However, in the scope of this thesis, we have designed real time optimizations only for some of the radio resource schemes. Firstly, we explore beam densification, where by increasing the number of beams, we improve the antenna gain values at the high demand hot-spot regions. However, such increase in the number of beams also increase the interbeam interference and badly affects SINR performance. Hence, in the first part of Chapter 2 of this thesis, we focus on finding an optimal number of beams for given high demand hot-spot region of a demand distribution profile. Also, steering the beams towards high demand regions, further increase the demand satisfaction. However, the positioning of the beams need to be carefully planned. On one hand, closely placed beams result in poor SINR performance. On the other hand, beams that are placed far away will have poor antenna gain values for the users away from the beam centers. Hence, in the second part of Chapter 2, we focus on finding optimized beam positions for maximum demand satisfaction in high demand hot-spot regions. Also, we propose a dynamic frequency-color coding strategy for efficient spectrum and interference management in demand-driven adaptive systems. Another solution is the proposed so-called Adaptive Multi-beam Pattern and Footprint (AMPF) design, where we fix the number of beams and based on the demand profile, we configure adaptive beam shapes and sizes along with their positions. Such an approach shall distribute the total demand across all the beams more evenly avoiding overloaded or underused beams. Such optimization was attempted in Chapter 3 using cluster analysis. Furthermore, demand satisfaction at both beam and user level was achieved by carefully performing demand driven user scheduling. On one hand, scheduling most orthogonal users at the same time may yield better capacity but may not provide demand satisfaction. This is majorly because users with high demand need to be scheduled more often in comparison to users with low demand irrespective of channel orthogonality. On the other hand, scheduling users with high demand which are least orthogonal, create strong interbeam interference and affect precoding performance. Accordingly, two demand driven scheduling algorithms (Weighted Semi-orthogonal scheduling (WSOS) and Interference-aware demand-based user scheduling) are discussed in Chapter 4. Lastly, in Chapter 5, we verified the impact of parallel implementation of two different demand based optimization techniques such as AMPF design and WSOS user scheduling. Evidently, numerical results presented throughout this thesis validate the effectiveness of the proposed demand based optimization techniques in terms of demand matching performance compared to the conventional non-demand based approaches

Satellite Beam Densification for High-Demand Areas

Conventional multi-beam pattern design in Geostationary (GEO) satellite communication systems consists of a regular grid of non-reconfigurable beams, where the beams overlap is typically assumed at the point where the beam edge reaches a 3-dB loss in the antenna pattern (with respect to the beam center). For certain high demand areas, this 3dB loss has a significant impact. To overcome this issue, in this paper we evaluate the potential gain of beam densification, i.e. considering an increased number of beams (keeping the same beam size and shape) to cover hot-spot areas, with the aim to push the beam overlap and increase the beam gain. In particular, we compare two beam patterns (kindly provided by ESA): One with regular beam grid, and one with densification in a particular hot-spot area. We provide a comparison in terms of per-beam average SINR and capacity, as well as an overall system analysis considering the whole densified region

Interference-aware Demand-based User Scheduling in Precoded High Throughput Satellite Systems

In recent years, dynamic traffic demand requisites have driven the satellite communication service providers to implement reconfigurable demand-driven features to align the delivered throughput with the temporal and geographical variations of the traffic demand. Also, in current interference-limited High Throughput Satellite (HTS) systems, the resulting inter-beam co-channel interference can be mitigated by carefully performing precoding and user scheduling. Unfortunately, the conventional user scheduling algorithms fail to provide demand satisfaction for dynamic traffic demand requisites. Hence, in this paper, we focus on the user scheduling design for precoded satellite systems where both co-channel interference and user demands are taken into account. In particular, we first classify the sectors in each beam according to the interference they may cause to neighboring beams. Next, we formulate the scheduling problem such as the activation of neighboring beam sectors is avoided while proportionally dwelling on the sectors based on their traffic demands. The supporting numerical results for different demand distribution profiles validate the effectiveness of proposed interference-aware demand-based user scheduling over conventional scheduling techniques

Feed-Forward Neural Networks to Overcome Complex Precoding Matrix Calculation in GEO Broadband Satellite Communication Systems

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Foreseeing Semi-Persistent Scheduling in Mode-4 for 5G enhanced V2X communication

One of the most dominant applications of Ultra Reliable Low Latency Communication of 5G-NR is V2X communication. For such latency-critical V2X communication, the distributed resource allocation using Semi-Persistent Scheduling (SPS) algorithm designed for out-of-coverage (Mode-4) scenario leads to a high collision probability and requires profuse sensing processes. Therefore, a need for more efficient distributed resource allocation scheme is compelled. In this paper, we investigate the 3GPP proposed SPS scheduling algorithm for its performance and formulate the problem under partial sensing systems. Furthermore, we propose a Foreseeing Semi-Persistent Scheduling (F-SPS) algorithm as an enhancement to the existing methodology, and conclusively, simulations are presented to illustrate the improved performance of the proposed F-SPS scheme in terms of reduced collision probability with an optimised number of sensing processes

Demand-Aware Beam Design and User Scheduling for Precoded Multibeam GEO Satellite Systems

For many years, satellite footprints have been fixed from the design phase until the last day of the satellite operational life. Flexibility in coverage by means of reconfigurable beams is becoming increasingly popular thanks to the recent developments in active antenna systems. On the other hand, spatial frequency reuse combined with precoding has been shown to boost the spectral efficiency while lowering the cost per bit. In this context, and motivated by the unbalanced demand requests of the satellite users, we propose a shift from the traditional system-throughput maximization design towards a demand-Aware design, where a new beam shaping technique and user scheduling are combined to satisfy the users’ demands. Supporting numerical results are provided that validate the effectiveness of the proposed beam planning and scheduling and quantify the benefits over conventional rigid techniques

Clustering-based Adaptive Beam Footprint Design for 5G Urban Macro-Cell

In a dense 5G urban-eMBB environment, the user density and traffic loads follow a spatiotemporal variability. To meet high traffic demands, the 5G base stations exploit spatial multiplexing by means of Active Antenna Systems (AAS) and beamforming. However, pedestrian and vehicular users are highly mobile, rendering non-dynamic beamforming designs totally inefficient in terms of meeting the users’ demand requests. In particular, the latter results in either overload or underutilized beams in a cell. Hence, a practical approach to meet such spatio-temporal heterogeneous demand is to consider dynamic and adaptive beam footprint design that takes into account both the actual users’ position as well as the traffic loads. In this paper, we first study and evaluate the state-of-the-art fixed cell beamforming (based on ITU-R M.2412-0) in a test environment and highlight its drawbacks. Next, we propose a adaptive macro-cell beam footprint design where the beams are dynamically shaped based on the spatial users distribution and their demand requests. Numerical simulations demonstrate the high system performance achieved by the proposed methodology

Demand-Based Adaptive Multi-Beam Pattern and Footprint Planning for High Throughput GEO Satellite Systems

The current broadband coverage area requisites and the expected user demand is satisfied by the state of the art satellite industry by using multiple spot beams of high throughput satellites with fixed multi-beam pattern and footprint planning. However, in recent years, new mobile broadband users with dynamic traffic demand are requesting for services in remote geographical locations such as air (aeroplanes) and water (ships). Furthermore, the expected demand varies with time and geographical location of the users. Hence, a practical approach to meet such heterogeneous demand is to plan adaptive beams to the satellites equipped with beamforming capabilities. In this paper, we study the state of the art fixed multi-beam pattern and footprint plan and show its drawbacks to support the non-uniformly distributed user terminals and varying traffic demands. To end this, we propose an adaptive multi-beam pattern and footprint plan where we design spot beams with flexible size and position based on the spatial clustering of the users in order to increase the flexibility of the high throughput satellite system. Numerical simulations demonstrate the high system performance of the proposed methodology