Dynamic Control of Tunable Sub-optimal Algorithms for Scheduling of Time-varying Wireless Networks

It is well known that for ergodic channel processes the Generalized Max-Weight Matching (GMWM) scheduling policy stabilizes the network for any supportable arrival rate vector within the network capacity region. This policy, however, often requires the solution of an NP-hard optimization problem. This has motivated many researchers to develop sub-optimal algorithms that approximate the GMWM policy in selecting schedule vectors. One implicit assumption commonly shared in this context is that during the algorithm runtime, the channel states remain effectively unchanged. This assumption may not hold as the time needed to select near-optimal schedule vectors usually increases quickly with the network size. In this paper, we incorporate channel variations and the time-efficiency of sub-optimal algorithms into the scheduler design, to dynamically tune the algorithm runtime considering the tradeoff between algorithm efficiency and its robustness to changing channel states. Specifically, we propose a Dynamic Control Policy (DCP) that operates on top of a given sub-optimal algorithm, and dynamically but in a large time-scale adjusts the time given to the algorithm according to queue backlog and channel correlations. This policy does not require knowledge of the structure of the given sub-optimal algorithm, and with low overhead can be implemented in a distributed manner. Using a novel Lyapunov analysis, we characterize the throughput stability region induced by DCP and show that our characterization can be tight. We also show that the throughput stability region of DCP is at least as large as that of any other static policy. Finally, we provide two case studies to gain further intuition into the performance of DCP.Comment: Submitted for journal consideration. A shorter version was presented in IEEE IWQoS 200

Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning

To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the network's connectivity. As a result, the UAVs can not rely on their own experiences to build the global model. This issue affects the path planning of the UAVs. To address this concern, we utilize a two-step approach. In the first step, by using Federated Learning (FL), the UAVs collaboratively build a global model of the outage probability in the environment. In the second step, by using the global model obtained in the first step and rapidly-exploring random trees (RRTs), we propose an algorithm to optimize UAVs' paths. Simulation results show the effectiveness of this two-step approach for UAV networks.Comment: to appear in IEEE GLOBECOM 202

Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations

In this paper, the trajectory optimization problem for a multi-aerial base station (ABS) communication network is investigated. The objective is to find the trajectory of the ABSs so that the sum-rate of the users served by each ABS is maximized. To reach this goal, along with the optimal trajectory design, optimal power and sub-channel allocation is also of great importance to support the users with the highest possible data rates. To solve this complicated problem, we divide it into two sub-problems: ABS trajectory optimization sub-problem, and joint power and sub-channel assignment sub-problem. Then, based on the Q-learning method, we develop a distributed algorithm which solves these sub-problems efficiently, and does not need significant amount of information exchange between the ABSs and the core network. Simulation results show that although Q-learning is a model-free reinforcement learning technique, it has a remarkable capability to train the ABSs to optimize their trajectories based on the received reward signals, which carry decent information from the topology of the network.Comment: 6 pages, 3 figures, to be presented in IEEE PIMRC 201

On Stability Region and Delay Performance of Linear-Memory Randomized Scheduling for Time-Varying Networks

Throughput optimal scheduling policies in general require the solution of a complex and often NP-hard optimization problem. Related literature has shown that in the context of time-varying channels, randomized scheduling policies can be employed to reduce the complexity of the optimization problem but at the expense of a memory requirement that is exponential in the number of data flows. In this paper, we consider a Linear-Memory Randomized Scheduling Policy (LM-RSP) that is based on a pick-and-compare principle in a time-varying network with $N$ one-hop data flows. For general ergodic channel processes, we study the performance of LM-RSP in terms of its stability region and average delay. Specifically, we show that LM-RSP can stabilize a fraction of the capacity region. Our analysis characterizes this fraction as well as the average delay as a function of channel variations and the efficiency of LM-RSP in choosing an appropriate schedule vector. Applying these results to a class of Markovian channels, we provide explicit results on the stability region and delay performance of LM-RSP.Comment: Long version of preprint to appear in the IEEE Transactions on Networkin

Joint space-frequency block codes and signal alignment for heterogeneous networks

In this paper, we propose a new diversity-oriented space-frequency block codes (SFBC) and signal alignment (SA) enabled physical network coding (PNC) method for the uplink of heterogeneous networks. The proposed joint Dual-SFBC with SA-PNC design substantially reduces interference and enables connecting a larger number of users when compared with methods adopting interference alignment (IA) or PNC. The main motivation behind the dual SFBC and SA-PNC design is that it allows the efficient coexistence of macro and small cells without any inter-system channel information requirements. Numerical results also verify that the proposed method outperforms the existing SA-PNC static method without any additional information exchange requirement between the two systems while achieving the main benefits of IA and SA-PNC coordinated methods recently proposed.publishe

Antenna architectures for CDMA integrated wireless access networks

In this paper we consider different antenna sub-systems for integrated wireless access networks (IWAN). The various antenna sub-systems present alternatives to the standard architecture of a cellular system where the transmission/ reception occurs via omnidirectional or directional antennas with each antenna located at a base station. The appropriate antenna sub-system is dependent on the modulation scheme used. In this paper we consider a CDMA scheme along with various antenna sub-systems such as a distributed antenna, an antenna sub-system which we refer to as a sectorized distributed antenna, and a distributed antenna utilizing sub-carrier multiplexing. These concepts lead to a network architecture consisting of network switches, radio controllers, base stations, and the various antenna subsystems

Downlink Resource Allocation for Autonomous Infrastructure-based Multihop Cellular Networks

Abstract Considering a multihop cellular system with one relay per sector, an effective modeling for the joint base-station/relay assignment, rate allocation, and routing scheme is proposed and formulated under a single problem for the downlink. This problem is then formulated as a multidimensional multichoice knapsack problem (MMKP) to maximize the total achieved throughput in the network. The well-known MMKP algorithm based on Lagrange multipliers is modified, which results in a near-optimal solution with a linear complexity. The notion of the infeasibility factor is also introduced to adjust the transmit power of base stations and relays adaptively. To reduce the complexity, and in order to analyze the underlying key factors in the system, the framework is restricted to a two-base-station two-relay system. In fact, the output of the proposed algorithm is the joint optimization of the routing path, and base-station selection to achieve the maximum total throughput in the system, which in conjunction with the proposed adaptive scheme leads to the implementation of the cell breathing via allocating the proper transmit power to the base-stations and relays