peer reviewed

In this paper, we address the resource allocation problem for task offloading from Internet of Things (IoT) devices to a non-terrestrial network. The proposed architecture contains clusters of IoT devices that can either execute their computing tasks locally or offload them to a dedicated unmanned aerial vehicle (UAV) functioning as a multi-access edge computing (MEC) server. The UAV can process the tasks itself or further offload them to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite within line-of-sight for remote computing. We formulate an optimization problem that aims to minimize the weighted sum of the total task-execution delay and the energy consumption of the IoT devices. Due to non-convexity of the problem and the inherent complexity-performance trade-off in optimization algorithms, we propose a set of low-complexity solutions. These include optimal methods based on convex subproblem decomposition and a greedy heuristic guided by convex optimization criteria. The framework jointly optimizes the computing resources and transmission power of IoT devices, the digital precoders and combiners at the UAV, the computing resources at the remote nodes (UAV, HAPS, and LEO), as well as task offloading decisions and subchannel allocation through a one-shot block coordinate descent approach. Simulation results highlight the performance gains of the proposed methods, demonstrating the impact of algorithmic complexity on key system metrics and the benefits of incorporating multiple non-terrestrial nodes compared to architectures lacking such capabilities.

Non-terrestrial networks (NTNs) play an essential role in the 6 G multi-layer architecture to provide ubiquitous coverage as well as guarantee heterogeneous requirements from vertical services. Compared to terrestrial gNodeB, flying base stations (F -BSs) in NTN are limited in terms of computation capability as well as energy budget. Therefore, it is of great importance for F-BSs to have computational and energy-efficient radio resource management (RRM) strategies. In this paper, we propose a low-complexity algorithm that jointly optimizes the transmit power and bandwidth allocation of OFDM-based multiuser downlink in NTN under flat fading scenario. The proposed algorithm exploits the flexible bandwidth design methodology to tackle the binary selection challenges, followed by a bandwidth adjustment step to force the allocated bandwidth to a multiplication of sub-channel bandwidth. More importantly, the proposed algorithm is robust against the channel estimation error. It is shown that the proposed algorithm retains the optimal solutions while significantly reduces the computational complexity, compared to both the optimal brand-and-bound (BnB) and the popular difference-of-convex (DC)-based sub-channel allocation solutions.

This paper studies the joint multislot design of user scheduling and precoding to minimize the time needed to serve all the users for unicast and multicast transmission in single-cell multiuser MISO downlink systems. In the literature, the joint design of scheduling and precoding is typically undertaken based on feedback from previous slots. In a system with time-varying channels and QoS requirements, joint multislot designs can achieve better performance since they have the flexibility to schedule users over multiple slots and also can split users across slots efficiently. Further, a joint multislot design can provide a feasible solution even when the sequential design fails. In this paper, scheduling is represented by a binary matrix where the rows represent users, columns represent slots and entries represent scheduling of users in the slots. Noticing that the users may not be permuted across slots for time-varying channels, service time needed for scheduling is rendered as the highest column index corresponding to non-zero columns. With the help of binary scheduling matrix, service time minimization is formulated as a structured mixed-Boolean fractional programming. Further, by exploiting the hidden convex-concave structure in the problem, a convex-concave procedure-based iterative algorithm is proposed. Finally, we vindicate the necessity and illustrate the superiority in performance of joint multislot design over the sequential solution through Monte-Carlo simulations.

The average performance of the MISO downlink channel, with a large number of users compared to transmit antennas of the base station, depends on the interference management which necessitates the joint design of scheduling and precoding. Unlike the previous works which do not offer a truly joint design, this paper focuses on formulating a problem amenable for the joint update of scheduling and precoding. Novel optimization formulations are investigated to reveal the hidden difference of convex/ concave structure for three classical criteria (weighted sum rate, max-min signal-to-interference plus noise ratio, and power minimization) and associated constraints are considered. Thereafter, we propose a convex-concave procedure framework based iterative algorithm where scheduling and precoding variables are updated jointly in each iteration. Finally, we show the superiority in performance of joint solution over the state-of-the-art designs through Monte-Carlo simulations.

Recent satellite standards enforce the coding of multiple users’ data in a frame. This transmission strategy mimics the well-known physical layer multigroup multicasting (MGMC). However, typical beam coverage with a large number of users and limited frame length lead to the scheduling of only a few users. Moreover, in emerging aggressive frequency reuse systems, scheduling is coupled with precoding. This is addressed in this work, through the joint design of scheduling and precoding for frame-based MGMC satellite systems. This aim is formulated as the maximization of the sum- rate under per beam power constraint and minimum SINR requirement of scheduled users. Further, a framework is proposed to transform the non-smooth SR objective with integer scheduling and nonconvex SINR constraints as a difference- of-convex problem that facilitates the joint update of scheduling and precoding. Therein, an efficient convex-concave procedure based algorithm is proposed. Finally, the gains (up to 50%) obtained by the jointed design over state- of-the-art methods is shown through Monte-Carlo simulations.

In this work, we consider the problem of data decoding in media-based modulation systems. The underlying problem is sparse because only a subset of the available transmit antennas is activated in each symbol; additionally, only one of the different mirror patterns is activated depending on the unknown data bits. Thus, the data recovery problem involves recovery of a block-sparse vector, with the additional structure that only one entry is active within each block. We term this structure as inclusion-exclusion sparsity, as the inclusion of an index in the active set precludes several other indices from being active. Devising efficient algorithms for recovering such sparse signals from noisy underdetermined linear measurements is an open problem. To this end, we propose a general, non-convex cost function that, when optimized, yields a sparse vector with additional structure, including, but not limited to, the inclusion-exclusion sparsity. Further, we propose a convex concave procedure (CCP) based algorithm for optimizing the cost function. The algorithm has low computational complexity and is globally convergent to a local optimum. Finally, we demonstrate the efficacy of our algorithm and its superior performance over existing data recovery schemes via Monte Carlo simulations.