Rapid deployment is crucial for building up drone aided emergency communications to ensure the coverage and service support after the disaster. The chaos in the post-disaster area, such as the number of ground users and scattered locations, makes difficult on the decision of drone deployment. In this paper, an unsupervised machine learning method is conducted for drone deployment in drone aided emergency communications. Considering the importance of sustainable services for drones, the drone deployment problem is formulated with the aim of minimizing the total power of drones with all users' coverage while maintaining their rate requirements, with constraints on drones' coverage area, capacity and limited power. The problem is solved by two steps. A modified k-means clustering algorithm is proposed to obtain the number of drones and an optimal altitude and minimum transmit power algorithm is then derived. Simulation results show that although the number of drones obtained by the modified algorithm is more than that of the original k-means algorithm, all users are served and the minimum power of drones is guaranteed by proposed algorithms.

Massive multiple-input-multiple-output (MIMO) is a promising technique to provide high-data-rate communication in fifth-generation (5G) mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed- beam based multiuser massive MIMO system where each user is served by a beam allocated to it. As the directions of fixed beams are predetermined and the users are randomly distributed, there could be some ``worst-case'' users, located at the edge of its serving beam, suffering from strong inter-beam interference and thus experiencing low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates without sacrificing the sum data rate.

We assess the performance of two distinct functional splits based on latency/latency variation and mapping efficiency, both individually and in unison. By considering hardware-offloading possibilities for a low-layer split (especially a pre-resource mapper split) using an option-6 software-based local thermal equilibrium split as an example, we show how data rate, Ethernet frame size and, in general, traffic generation characteristics will be very important aspects in the design of the future Ethernet mapping function. Then, an integrated Ethernet fronthaul with legacy and new/evolved split functionality operating at 100 Gb/s link rate is presented with the state-of-the art sub-100-ns latency variation for a timing-protocol flow. This is achieved through the application of a gap-filling aggregator, used for the first time in such a mobile fronthaul application.

Massive multiple-input-multiple-output (MIMO) has become a promising technique to provide high-data-rate communication in fifth-generation mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed-beam based multiuser massive MIMO system, where each user is served by a beam allocated to it. To maximize the sum data rate, a greedy beam allocation algorithm is proposed under the practical condition that the number of radio frequency chains is smaller than the number of users. Simulation results show that our proposed greedy algorithm achieves nearly optimal sum data rate. As only the sum data rate is optimized, there are some “worst-case” users, who could suffer from strong inter-beam interference and thus experience low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates and the max-min fairness among served users without sacrificing the sum data rate.

An Opnet model of a time-aware shaper (TAS) based on the IEEE 802.1Qbv standard is presented. The TAS model is assumed to be the scheduling entity in an Ethernet-based fronthaul network, comprising of Ethernet switches. The fronthaul transports different traffic flow types as envisioned in next generation Radio Access Networks (RANs), including those for a timing protocol (based on the precision time protocol) and those from the implementation of different RAN functional subdivisions. The performance of the TAS is compared to that of a strict priority regime and is quantified through the frame delay variation of the high priority traffic when this contends with lower priority traffic. The results show that with the TAS implementation, contention effects can be overcome and frame delay variation (frame jitter) can be removed. Timing instability in the significant events of the scheduler is considered and a solution to overcome this issue is proposed.

A testbed implementation for an Ethernet fronthaul transporting signals arising from a long-term evolution (LTE) functional subdivision ("split") at the media-access control (MAC)/physical layer (PHY) interface is presented. Based on open LTE base station software, the testbed demonstrates significant data rate reductions compared to current fronthaul implementations that rely on In- phase and Quadrature radio sample transportation and data rates that scale with cell load. All generated traffic flows are clearly distinguishable using appropriate packet headers. A selection of test cases and their corresponding results are presented to demonstrate the operation of the fronthaul and the performance of individual flows in terms of data rates and overheads.

This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), and so on. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower bound. Then, based on the successive convex approximation technique and the relationship between the data rate and the mean square error, we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the baseband unit pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l1-norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC.

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a cloud radio access network (C-RAN) where both the RRHs and users all have multiple antennas. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. We propose to solve this problem with two phases. In Phase I, a new approach is proposed to check the feasibility of the original problem. If the feasibility is guaranteed, in Phase II, a low- complexity algorithm is proposed to solve the original optimization problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side.

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.

A combined clipping/predistortion scheme is proposed and demonstrated for the distortion compensation of OFDM-Radio over Fiber links at baseband. In this scheme, a memory-polynomial-based predistorter and a simple amplitude clipping method are used. No specific filter is used after the clipping function, which reduces the implementation complexity. Experimental results confirm that the proposed approach gives significantly better performance compared to the digital predistortion (without clipping) approach for highly distorted OFDM RoF links. Changes in the OFDM signal's PAPR through the RoF link are analysed for the predistortion schemes with and without clipping. As a result of this, a way of optimizing the performance of the clipping/predistortion approach is proposed.

In this paper we investigate protocol issues that might arise due to the extra fiber propagation delay in fiber-fed IEEE 802.16m networks. Our study indicates that although the fiber delay might affect network performance, an informed choice of protocol parameters, such as the guard times and the ranging channel structure, can minimize the reduction in efficiency and allow for relaxation of some of the constraints imposed on the optical distribution network architecture.

Memory polynomials (MP) are demonstrated for modeling of RoF link nonlinearities, within the frequency range from 0.5 to 2 GHz, using a complex-envelope behavioral modeling approach. The model accuracies are studied for different RF frequency and input power cases, with the dependency of the MP model accuracy on the nonlinearity order and memory length parameters discussed. The experimental results show that the RoF link presents more memory effects at some RF frequencies and that the link phase nonlinearity tends to increase with the operating frequency. Excellent agreement between the measured and modeled results is reported for several validated MP models. In comparison with a memoryless polynomial model, the MP model performs significantly better for all experimental cases.

With the deployment of high speed train (HST) systems increasing worldwide and their popularity with travelers growing, providing broadband wireless communications (BWC) in HSTs is becoming crucial. In this paper, a tutorial is presented on recent research into BWC provision for HSTs. The basic HST BWC network architecture is described. Two potential cellular architectures, microcells and distributed antenna systems (DASs) based cells, are introduced. In particular, the DAS is discussed in conjunction with radio over fiber (RoF) technology for BWC for HSTs. The technical challenges in providing DAS-based BWC for HSTs, such as handoff and RoF are discussed and outlined.

A high capacity wireless communication system requires careful design to minimize interference and distortion effects. This paper considers the adaptive predistortion of the nonlinear distortions induced by a Radio over Fiber (RoF) link and power amplifier (PA) connected in series. In particular, we study the architectures for the joint compensation of the nonlinearities in the presence of nonideal feedback. From the adaptive algorithm point of view, we study different combinations of algorithms for the predistortion. Our simulation results indicate that combined use of least mean squares (LMS) and recursive least squares (RLS) gives the best trade-off between complexity and performance. In addition, we show that the use of nonideal feedback causes a collapse in the performance of the predistortion. However, when using a compensated RoF link for feedback, the degradation of the adjacent channel power is very small compared to the case of the ideal feedback.