Efficient radio resource management (RRM) in 5G networks is increasingly challenged by the diverse quality of service (QoS) requirements of emerging applications and the growing uplink (UL) traffic from resource-constrained devices. Existing scheduling approaches often lack user and service-specific context, limiting their ability to guarantee timely and energy-efficient data transmission, particularly critical for the internet of things (IoT) and mission-critical services. In this work, we introduceQUEST, a QoS-aware UL scheduling framework that exploits the 5G QoS model alongside network and device context to efficiently allocate radio resources. Designed and evaluated in an indoor factory environment,QUESTsupports users with various heterogeneous 5QI services under dynamic multi-user conditions. Evaluation results, validated through both real-world measurements and 3GPP-compliant simulations, show thatQUESTconsistently outperforms traditional channel- and QoS-aware schedulers. It improves QoS compliance, reduces packet drops and serving time, and enhances energy efficiency. For users with stringent QoS demands, measurements show a 13% increase in successfully transmitted packets and a 6.2% reduction in delay for 50% of transmissions, compared to the best-performing baseline. Benchmarking against an optimal scheduler shows thatQUESTachieves the closest performance among baselines, while maintaining low complexity, making it a practical and scalable solution for 5G and beyond UL RRM.

The demands on networks are increasing at a fast pace. In particular, real-time applications have very strict network requirements. However, setting up a network that hosts real-time applications is a cost-intensive endeavor, especially for experimental systems such as testbeds. Systems that provide guaranteed real-time networking capabilities usually work with expensive, high-rate software-defined switches. In contrast, real-time networking systems based on low-cost hardware face the limitation of lower link speeds. This paper fills this gap and presents Low-Cost Deterministic Networking (LCDN), a system designed to work with inexpensive, common off-the-shelf switches and devices. LCDN works at Gigabit speed and enables powerful testbeds to host real-time applications with strict delay guarantees. LCDN’s performance is similar to industrial- and production-grade solutions. This paper also provides an evaluation of the determinism of a low-cost switch and a Raspberry Pi used as an end-device to demonstrate the applicability of LCDN for inexpensive, low-power systems.

Goal-oriented (GO) semantic communication facilitates scaling modern networks with growing real-time traffic generated within networked Cyber Physical Systems (CPSs). Network resource management in GO communication prioritizes data effectiveness for the application goal. This implies reducing network resources allocated to low-priority information. Existing GO approaches often lack generalization, because they tailor particular network schemes to particular applications. In the current work, we propose a practical GO scheme operating in the transport layer (TL) middleware, i.e., not requiring specific hardware or network structure. Using Reinforcement Learning (RL), the proposed GO RL TL captures the potential contribution of the currently sampled observed state to the real-time CPS process evolution at the remote monitor. Together with the network congestion level, the state’s effect on the application goal determines whether the distributed sensors deploying GO RL TL agents accept corresponding packets into the network or discard them. The offline environment for training uses the real data traces of traffic patterns and application dynamics. The model generalizes to arbitrary network and application setups present in traces by learning the corresponding inter-dependencies from data. The extensive hardware tests witness the adaptability of the proposed GO RL TL, as well as its superiority in application performance compared to competitors. GO RL TL improves remote estimation mean-squared error by $20 \%$ to $100 \%$ in static network conditions, and by $\sim 30 \%$ in the dynamic setup.

The growing demand for mobile data strains the licensed spectrum, prompting the need for efficient offloading strategies. New Radio in Unlicensed Spectrum (NR-U) leverages 5 GHz and 6 GHz bands but faces coexistence challenges with WiFi and Bluetooth due to interference. This paper proposes three reinforcement learning-based models to optimize Channel Occupancy Time (COT) in NR-U networks. The first uses Qlearning for fair coexistence with WiFi under Listen-BeforeTalk (LBT) rules. The second enhances energy efficiency by jointly optimizing COT and power control. The third applies Dyna-Q+ to accelerate learning and adapt to dynamic traffic loads. Simulations over 500 scenarios show all models achieve Jain’s Fairness Index above 0.9, ensuring equitable spectrum sharing. The energy-efficient model reduces power consumption by up to 20% without compromising performance. These results demonstrate the potential of intelligent learning-based approaches for robust and efficient NR-U and WiFi coexistence in shared spectrum environments.

The proliferation of 5G technology necessitates advanced network management strategies to ensure optimal performance and reliability. Digital Twin (DT)s have emerged as a promising paradigm for modeling and simulating complex systems like the 5G Radio Access Network (RAN). In this paper, we present TwinRAN, a DT of the 5G RAN built leveraging the Azure DT platform. TwinRAN is built on top of the Open RAN (O-RAN) architecture and is agnostic to the vendor of the underlying equipment. We demonstrate three applications using TwinRAN and evaluate the required resources and their performance for a network with 800 users and eight gNBs. We first evaluate the performance and limitations of the Azure DT platform, measuring the latency under different conditions. The results from this evaluation allow us to optimize TwinRAN for the DT platform it uses. Then, we present the system's architectural design, emphasizing its components and interactions. We propose that two types of twin graphs be simultaneously maintained on the cloud. The first is for intercell operations, keeping a broad overview of all the cells in the network. The second twin graph where each cell is spawned in a separate Azure DT instance for more granular operation and monitoring of intracell tasks. We evaluate the performance and operating costs of TwinRAN for each of the three applications. The TwinRAN DT in the cloud can keep track of its physical twin within a few hundred milliseconds, extending its utility to many 5G network management tasks - some of which are shown in this paper. The novel framework for building and maintaining a DT of the 5G RAN presented in this paper offers network operators enhanced capabilities, empowering efficient deployments and management.

Industry 4.0 has brought to attention the need for a connected, flexible, and autonomous production environment. The New Radio (NR)-sidelink, which was introduced by the third-generation partnership project (3GPP) in Release 16, can be particularly helpful for factories that need to facilitate cooperative and close-range communication. Automated Guided Vehicles (AGVs) are essential for material handling and carriage within these environments, and using NR-sidelink communication can further enhance their performance. An efficient resource allocation mechanism is required to ensure reliable communication and avoid interference between AGVs and other wireless systems in the factory using NR-sidelink. This work presents a simulation analysis of the 3GPP standardized resource allocation algorithm for NR-sidelink in an industrial scenario with a use case of cooperative-carrying AGVs. We suggest further improvements that are tailored to the quality of service (QoS) requirements of an indoor factory communication scenario with cooperative AGVs. The use of NR-sidelink communication has the potential to help meet the QoS requirements for different Industry 4.0 use cases. This work can be a foundation for further improvements in NR-sidelink in 3GPP Release 18 and beyond.

The rising prevalence of real-time applications that require deterministic communication over mobile networks necessitates the joint operation of both mobile and fixed network components. This joint operation requires designing components that interact between the two technologies to provide users with latency and packet loss guarantees. In this work, we demonstrate a fully integrated 5G-DetNet that can guarantee the end-to-end demands of different flows. Moreover, we show how such a network can be implemented using low-cost hardware and open-source software, making it accessible to many 5G testbeds. The features demonstrated in this work are a network manager that does the routing and scheduling, an application function in the 5G core that interfaces with the network manager, and a network-side translator for user-plane management and de-jittering of the real-time streams.

In this paper, we study binary tree-algorithms that exploit a combination of multi-packet reception (MPR) and successive interference cancellation (SIC), which so far has not been considered in the literature. Specifically, we assume that the receiver is capable of successfully decoding any collision of up to and including$K$concurrent packet transmissions and can perform SIC along the tree. We show a number of novel results for this type of tree algorithms. We first derive the basic performance parameters, which are the expected length of the collision resolution interval and the throughput normalized with$K$, conditioned on the number of contending users. We then analyze their asymptotic behaviour, identifying an oscillatory component that amplifies as$K$increases. In the next step, we derive the maximum stable throughput (MST) for the gated and windowed access assuming Poisson arrivals. We show that for windowed access, the bound on MST normalized with$K$increases with$K$. Finally, we discuss practical issues related to implementation of such scheme, as well as compare it to slotted ALOHA-based schemes that exploit both$K$-MPR and SIC.

In the above article, the authors propose$d$-ary SICTA and derive the expected conditional length of the collision resolution interval, optimal splitting probability and the maximum stable throughput (MST) for$d \geq 2$under stationary ergodic packet arrivals. In this correction, we show that the premise of the analysis for$d > 2$and consequentially the results presented for$d > 2$do not hold.

In this paper, we analyze binary-tree algorithms in a setup in which the receiver can perform multi-packet reception (MPR) of up to and including K packets simultaneously. The analysis addresses both traffic-independent performance as well as performance under Poisson arrivals. For the former case, we show that the throughput, when normalized with respect to the assumed linear increase in resources required to achieve K-MPR capability, tends to the same value that holds for the single-reception setup. However, when coupled with Poisson arrivals in the windowed access scheme, the normalized throughput increases with K, and we present evidence that it asymptotically tends to 1. We also provide performance results for the modified tree algorithm with K-MPR in the clipped access scheme. To the best of our knowledge, this is the first paper that provides an analytical treatment and a number of fundamental insights in the performance of tree-algorithms with MPR.

Unlike its cousin 3SAT, the NAE-3SAT (not-all-equal-3SAT) problem has the property that spectral/SDP algorithms can efficiently refute random instances when the constraint density is a large constant (with high probability). But do these methods work immediately above the “satisfiability threshold”, or is there still a range of constraint densities for which random NAE-3SAT instances are unsatisfiable but hard to refute? We show that the latter situation prevails, at least in the context of random regular instances and SDP-based refutation. More precisely, whereas a random d-regular instance of NAE-3SAT is easily shown to be unsatisfiable (whp) once d ≥ 8, we establish the following sharp threshold result regarding efficient refutation: If d < 13.5 then the basic SDP, even augmented with triangle inequalities, fails to refute satisfiability (whp); if d > 13.5 then even the most basic spectral algorithm refutes satisfiability (whp).

Estimators computed from adaptively collected data do not behave like their non-adaptive brethren.Rather, the sequential dependence of the collection policy can lead to severe distributional biases that persist even in the infinite data limit. We develop a general method – $\mathbf{W}$-decorrelation – for transforming the bias of adaptive linear regression estimators into variance. The method uses only coarse-grained information about the data collection policy and does not need access to propensity scores or exact knowledge of the policy.We bound the finite-sample bias and variance of the $\mathbf{W}$-estimator and develop asymptotically correct confidence intervals based on a novel martingale central limit theorem. We then demonstrate the empirical benefits of the generic $\mathbf{W}$-decorrelation procedure in two different adaptive data settings: the multi-armed bandit and the autoregressive time series.

We provide the first information theoretical tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in detection of latent community structure without nodes covariates and a large body of empirical work using diverse heuristics for combining node covariates with graphs for inference. The tightness of our analysis implies in particular, the information theoretic necessity of combining the different sources of information. Our analysis holds for networks of large degrees as well as for a Gaussian version of the model.

Maximum A posteriori Probability (MAP) inference in graphical models amounts to solving a graph-structured combinatorial optimization problem. Popular inference algorithms such as belief propagation (BP) and generalized belief propagation (GBP) are intimately related to linear programming (LP) relaxation within the Sherali-Adams hierarchy. Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares (SOS) hierarchy based on semidefinite programming (SDP) can provide superior guarantees. Unfortunately, SOS relaxations for a graph with $n$ vertices require solving an SDP with $n^{\Theta(d)}$ variables where $d$ is the degree in the hierarchy. In practice, for $d\ge 4$, this approach does not scale beyond a few tens of variables. In this paper, we propose binary SDP relaxations for MAP inference using the SOS hierarchy with two innovations focused on computational efficiency. Firstly, in analogy to BP and its variants, we only introduce decision variables corresponding to contiguous regions in the graphical model. Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style method, and develop a sequential rounding procedure. We demonstrate that the resulting algorithm can solve problems with tens of thousands of variables within minutes, and outperforms BP and GBP on practical problems such as image denoising and Ising spin glasses. Finally, for specific graph types, we establish a sufficient condition for the tightness of the proposed partial SOS relaxation.

We develop an information-theoretic view of the stochastic block model, a popular statistical model for the large-scale structure of complex networks. A graph G from such a model is generated by first assigning vertex labels at random from a finite alphabet, and then connecting vertices with edge probabilities depending on the labels of the endpoints. In the case of the symmetric two-group model, we establish an explicit `single-letter' characterization of the per-vertex mutual information between the vertex labels and the graph, when the graph average degree diverges. The explicit expression of the mutual information is intimately related to estimation-theoretic quantities, and -in particular- reveals a phase transition at the critical point for community detection. Below the critical point the per-vertex mutual information is asymptotically the same as if edges were independent of the vertex labels. Correspondingly, no algorithm can estimate the partition better than random guessing. Conversely, above the threshold, the per-vertex mutual information is strictly smaller than the independent-edges upper bound. In this regime there exists a procedure that estimates the vertex labels better than random guessing.

In sparse principal component analysis we are given noisy observations of a low-rank matrix of dimension $n\times p$ and seek to reconstruct it under additional sparsity assumptions. In particular, we assume here each of the principal components $v_1,\dots,v_r$ has at most $s_0$ non-zero entries. We are particularly interested in the high dimensional regime wherein $p$ is comparable to, or even much larger than $n$. In an influential paper, Johnstone and Lu (2004) introduced a simple algorithm that estimates the support of the principal vectors $v_1,\dots,v_r$ by the largest entries in the diagonal of the empirical covariance. This method can be shown to identify the correct support with high probability if $s_0\le K_1\sqrt{n/\log p}$, and to fail with high probability if $s_0\ge K_2 \sqrt{n/\log p}$ for two constants $0 Here we analyze a covariance thresholding algorithm that was recently proposed by Krauthgamer, Nadler, Vilenchik, et al. (2015). On the basis of numerical simulations (for the rank-one case), these authors conjectured that covariance thresholding correctly recover the support with high probability for $s_0\le K\sqrt{n}$ (assuming $n$ of the same order as $p$). We prove this conjecture, and in fact establish a more general guarantee including higher-rank as well as $n$ much smaller than $p$. Recent lower bounds (Berthet and Rigollet, 2013; Ma and Wigderson, 2015) suggest that no polynomial time algorithm can do significantly better. The key technical component of our analysis develops new bounds on the norm of kernel random matrices, in regimes that were not considered before. Using these, we also derive sharp bounds for estimating the population covariance, and the principal component (with $\ell_2$-loss). [abs][pdf][bib] © JMLR 2016. (edit, beta) Mastodon

Given a large data matrix A∈\mathbbR^n\times n, we consider the problem of determining whether its entries are i.i.d. from some known marginal distribution A_ij∼P_0, or instead A contains a principal submatrix A_\sf Q,\sf Q whose entries have marginal distribution A_ij∼P_1≠P_0. As a special case, the hidden (or planted) clique problem is finding a planted clique in an otherwise uniformly random graph. Assuming unbounded computational resources, this hypothesis testing problem is statistically solvable provided |\sf Q|\ge C \log n for a suitable constant C. However, despite substantial effort, no polynomial time algorithm is known that succeeds with high probability when |\sf Q| = o(\sqrtn). Recently, \citemeka2013association proposed a method to establish lower bounds for the hidden clique problem within the Sum of Squares (SOS) semidefinite hierarchy. Here we consider the degree-4 SOS relaxation, and study the construction of \citemeka2013association to prove that SOS fails unless k\ge C\,n^1/3/\log n. An argument presented by \citeBarakLectureNotes implies that this lower bound cannot be substantially improved unless the witness construction is changed in the proof. Our proof uses the moment method to bound the spectrum of a certain random association scheme, i.e. a symmetric random matrix whose rows and columns are indexed by the edges of an Erdös-Renyi random graph.

Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations of sparse PCA: a spiked Wigner and spiked Wishart (or spiked covariance) model. We analyze an Approximate Message Passing (AMP) algorithm to estimate the underlying signal and show, in the high dimensional limit, that the AMP estimates are information-theoretically optimal. As an immediate corollary, our results demonstrate that the posterior expectation of the underlying signal, which is often intractable to compute, can be obtained using a polynomial-time scheme. Our results also effectively provide a single-letter characterization of the sparse PCA problem.

We consider the problem of finding optimal, fair and distributed power-rate strategies to achieve the sum capacity of the Gaussian multiple-access block-fading channel. The transmitters have access to only their own fading coefficients, while the receiver has access to all of the fading coefficients. We propose a distributed strategy called the `midpoint' strategy which is optimal when the system cannot tolerate outage. In addition, we demonstrate a successive decoding scheme that can achieve this maximal sum-rate. In presence of outage, we show that the strategies based on a single threshold are suboptimal.