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The multi allocation p-hub median problem (MApHM), the multi allocation uncapacitated hub location problem (MAuHLP) and the multi allocation p-hub location problem (MApHLP) are common hub location problems with several practical applications. HLPs aim to construct a network for routing tasks between different locations. Specifically, a set of hubs must be chosen and each routing must be performed using one or two hubs as stopovers. The costs between two hubs are discounted. The objective is to minimize the total transportation cost in the MApHM and additionally to minimize the set-up costs for the hubs in the MAuHLP and MApHLP. In this paper, an approximation algorithm to solve these problems is developed, which improves the approximation bound for MApHM to 3.451, for MAuHLP to 2.173 and for MApHLP to 4.552 when combined with the algorithm of Benedito & Pedrosa. The proposed algorithm is capable of solving much bigger instances than any exact algorithm in the literature. New benchmark instances have been created and published for evaluation, such that HLP algorithms can be tested and compared on huge instances. The proposed algorithm performs on most instances better than the algorithm of Benedito & Pedrosa, which was the only known approximation algorithm for these problems by now.

Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing DL-based end-to-end wireless communication systems to the research community and promoting the use of artificial intelligence (AI)/DL for the wireless physical layer. We then put forward two key trade-offs in designing DL models for communication, namely, accuracy versus generalization and compression versus latency. We discuss their relevance in the context of wireless communications use cases using emerging DL models including large language models (LLMs). Finally, we summarize our proposed evaluation guidelines to enhance the research impact of DL on wireless communications. These guidelines are an attempt to reconcile the empirical nature of DL research with the rigorous requirement metrics of wireless communications systems.

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. This work aims to optimize resource allocation and task offloading in VEC networks. Specifically, we formulate a game theoretical resource allocation and task offloading problem (GTRATOP) that aims to maximize the system performance by jointly considering the incentive for cooperation, competition among vehicles, heterogeneity between VEC servers and vehicles, and inherent dynamic of VNs. Since the formulated GTRATOP is NP-hard, we propose an adaptive approach for resource allocation and task offloading in VEC networks by incorporating bargaining game and matching game, which is called BARGAIN-MATCH. First, for resource allocation, a bargaining game-based incentive is proposed to stimulate the vehicles and VEC servers to negotiate the optimal resource allocation and pricing decisions. Second, for task offloading, a many-to-one matching scheme is proposed to decide the optimal offloading strategies. Third, the dynamic and time-varying features are considered to adapt the strategies of BARGAIN-MATCH to the real-time VEC networks. Moreover, the BARGAIN-MATCH is proved to be stable and weak Pareto optimal. Simulation results demonstrate that the proposed BARGAIN-MATCH achieves superior system performance and efficiency compared to other methods, especially when the system workload is heavy.

Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically receiving and relaying small data packets from a remote IoT device to its receiver in two hops with strict latency requirements, in the presence of an eavesdropper. This system requires careful optimization of important design parameters, such as the coding blocklengths of both hops, transmit powers, and UAV's trajectory. While the overall optimization problem is nonconvex, we tackle it by applying a block successive convex approximation (BSCA) approach to divide the original problem into three subproblems and solve them separately. Then, an overall iterative algorithm is proposed to obtain the final design with guaranteed convergence. Our proposed low-complexity algorithm incorporates 3D trajectory design and resource management to optimize the effective average secrecy throughput of the communication system over the course of UAV-relay's mission. Simulation results demonstrate significant performance improvements compared to various benchmark schemes and provide useful design insights on the coding blocklengths and transmit powers along the trajectory of the UAV.

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release 14 of long term evolution (LTE), which includes new features to enable direct vehicle-to-vehicle (V2V) communications. LTE-V2V promises a more efficient use of the channel compared to IEEE 802.11p thanks to an improved PHY layer and the use of orthogonal resources at the MAC layer. In LTE-V2V, a key role is played by the resource allocation algorithm and increasing efforts are being made to design new solutions to optimize the spatial reuse.In this context, an important aspect still little studied, is therefore that of identifying references that allow: 1) to have a perception of the space in which the resource allocation algorithms move; and 2) to verify the performance of new proposals. In this work, we focus on a highway scenario and identify two algorithms to be used as a minimum and maximum reference in terms of the packet reception probability (PRP). The PRP is derived as a function of various parameters that describe the scenario and settings, from the application to the physical layer. Results, obtained both in a simplified Poisson point process scenario and with realistic traffic traces, show that the PRP varies considerably with different algorithms and that there is room for the improvement of current solutions.

We study a fair resource sharing problem, where a set of resources are to be shared among a group of agents. Each agent demands one resource and each resource can serve a limited number of agents. An agent cares about what resource they get as well as the externalities imposed by their mates, who share the same resource with them. Clearly, the strong notion of envy-freeness, where no agent envies another for their resource or mates, cannot always be achieved and we show that even deciding the existence of such a strongly envy-free assignment is an intractable problem. Hence, a more interesting question is whether (and in what situations) a relaxed notion of envy-freeness, the Pareto envy-freeness, can be achieved. Under this relaxed notion, an agent envies another only when they envy both the resource and the mates of the other agent. In particular, we are interested in a dorm assignment problem, where students are to be assigned to dorms with the same capacity and they have dichotomous preference over their dormmates. We show that when the capacity of each dorm is 2, a Pareto envy-free assignment always exists and we present a polynomial-time algorithm to compute such an assignment. Nevertheless, the result breaks immediately when the capacity increases to 3, in which case even Pareto envy-freeness cannot be guaranteed. In addition to the existential results, we also investigate the utility guarantees of (Pareto) envy-free assignments in our model.

Superdirective array may achieve an array gain proportional to the square of the number of antennas $M^2$. In the early studies of superdirectivity, little research has been done from wireless communication point of view. To leverage superdirectivity for enhancing the spectral efficiency, this paper investigates multi-user communication systems with superdirective arrays. We first propose a field-coupling-aware (FCA) multi-user channel estimation method, which takes into account the antenna coupling effects. Aiming to maximize the power gain of the target user, we propose multi-user multipath superdirective precoding (SP) as an extension of our prior work on coupling-based superdirective beamforming. Furthermore, to reduce the inter-user interference, we propose interference-nulling superdirective precoding (INSP) as the optimal solution to maximize user power gains while eliminating interference. Then, by taking the ohmic loss into consideration, we further propose a regularized interference-nulling superdirective precoding (RINSP) method. Finally, we discuss the well-known narrow directivity bandwidth issue, and find that it is not a fundamental problem of superdirective arrays in multi-carrier communication systems. Simulation results show our proposed methods outperform the state-of-the-art methods significantly. Interestingly, in the multi-user scenario, an 18-antenna superdirective array can achieve up to a 9-fold increase of spectral efficiency compared to traditional multiple-input multiple-output (MIMO), while simultaneously reducing the array aperture by half.

In relay-enabled cellular networks, the intertwined nature of network agents calls for complex schemes to allocate wireless resources. Resources need to be distributed among mobile users while considering how relay resources are allocated, and constrained by the traffic rate achievable by base stations and over backhaul links. In this work, we derive a resource allocation scheme that achieves max-min fairness across mobile users. Furthermore, the optimal allocation is found with linear complexity with respect to the number of mobile users and relays.

In large-scale systems there are fundamental challenges when centralised techniques are used for task allocation. The number of interactions is limited by resource constraints such as on computation, storage, and network communication. We can increase scalability by implementing the system as a distributed task-allocation system, sharing tasks across many agents. However, this also increases the resource cost of communications and synchronisation, and is difficult to scale. In this paper we present four algorithms to solve these problems. The combination of these algorithms enable each agent to improve their task allocation strategy through reinforcement learning, while changing how much they explore the system in response to how optimal they believe their current strategy is, given their past experience. We focus on distributed agent systems where the agents' behaviours are constrained by resource usage limits, limiting agents to local rather than system-wide knowledge. We evaluate these algorithms in a simulated environment where agents are given a task composed of multiple subtasks that must be allocated to other agents with differing capabilities, to then carry out those tasks. We also simulate real-life system effects such as networking instability. Our solution is shown to solve the task allocation problem to 6.7% of the theoretical optimal within the system configurations considered. It provides 5x better performance recovery over no-knowledge retention approaches when system connectivity is impacted, and is tested against systems up to 100 agents with less than a 9% impact on the algorithms' performance.

In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance. Recently, Large-margin Softmax and Angular Softmax have been proposed to incorporate the angular margin in a multiplicative manner. In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works. We also emphasize and discuss the importance of feature normalization in the paper. Most importantly, our experiments on LFW BLUFR and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset. Our code has also been made available at //github.com/happynear/AMSoftmax