This paper addresses the joint user identification and channel estimation (JUICE) problem in machine-type communications under the practical spatially correlated channels model with unknown covariance matrices. Furthermore, we consider an MTC network with hierarchical user activity patterns following an event-triggered traffic mode. Therein the users are distributed over clusters with a structured sporadic activity behavior that exhibits both cluster-level and intra-cluster sparsity patterns. To solve the JUICE problem, we first leverage the concept of strong priors and propose a hierarchical-sparsity-inducing spike-and-slab prior to model the structured sparse activity pattern. Subsequently, we derive a Bayesian inference scheme by coupling the expectation propagation (EP) algorithm with the expectation maximization (EM) framework. Second, we reformulate the JUICE as a maximum a posteriori (MAP) estimation problem and propose a computationally-efficient solution based on the alternating direction method of multipliers (ADMM). More precisely, we relax the strong spike-and-slab prior with a cluster-sparsity-promoting prior based on the long-sum penalty. We then derive an ADMM algorithm that solves the MAP problem through a sequence of closed-form updates. Numerical results highlight the significant performance gains obtained by the proposed algorithms, as well as their robustness against various assumptions on the sparse activity behavior of the users.

We consider a multi-source relaying system where independent sources randomly generate status update packets which are sent to the destination with the aid of a relay through unreliable links. We develop transmission scheduling policies to minimize the weighted sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints. We formulate a stochastic control optimization problem and solve it using a constrained Markov decision process (CMDP) approach and a drift-plus-penalty method. The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method. We theoretically analyze the structure of optimal policies for the MDP problem and subsequently propose a structure-aware algorithm that returns a practical near-optimal policy. Using the drift-plus-penalty method, we devise a near-optimal low-complexity policy that performs the scheduling decisions dynamically. We also develop a model-free deep reinforcement learning policy for which the Lyapunov optimization theory and a dueling double deep Q-network are employed. The complexities of the proposed policies are analyzed. Simulation results are provided to assess the performance of our policies and validate the theoretical results. The results show up to 91% performance improvement compared to a baseline policy.

This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a clustered user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. We formulate the JUICE problem as a maximum a posteriori probability (MAP) problem with properly chosen priors to incorporate the partial knowledge of the UEs’ clustered activity and the unknown covariance matrices. We derive a computationally-efficient algorithm based on alternating direction method of multipliers (ADMM) to solve the MAP problem iteratively via a sequence of closed-form updates. Numerical results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances for clustered user activity patterns.

We study status updating under two-way delay in a system consisting of a sampler, a sink, and a controller residing at the sink. The controller drives the sampling process by sending request packets to the sampler. Upon receiving a request, the sampler generates a sample and transmits the status update packet to the sink. Transmissions of both request and status update packets encounter random delays. We develop optimal control policies to minimize the average age of information (AoI) using the tools of Markov decision processes in two scenarios. We begin with the system having at most one active request, i.e., a generated request for which the sink has not yet received a status update packet. Then, as the main distinctive feature of this paper, we initiate pipelined-type status updating by studying a system having at most two active requests. Furthermore, we conduct AoI analysis by deriving the average AoI expressions for the Zero-Wait-1, Zero-Wait-2, and Wait-1 policies. According to the Zero-Wait-1 policy, whenever a status update packet is delivered to the sink, a new request packet is inserted into the system. The Zero-Wait-2 policy operates similarly, except that the system can hold two active requests. According to the Wait-1 policy, whenever a status update packet is delivered to the sink, a new request is sent after a waiting time which is a function of the current AoI. Numerical results illustrate the performance of each status updating policy under varying system parameter values.

Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC). However, MTC is characterized by unprecedented traffic in cellular networks, thus posing a challenge to its successful incorporation. In this work, we propose a unified framework for amicable coexistence of MTC and HTC. We consider a heterogeneous network where machine-type devices coexist with enhanced mobile broadband (eMBB) devices and propose transceiver techniques that promote efficient signal recovery from these devices. For this, we present an eMBB pilot and MTC data generation strategy that facilitates joint coherent decoding of eMBB data and non-coherent decoding of MTC data. Furthermore, we assess the feasibility of coexistence using receiver operating characteristics, outage probability, and normalized mean square error (NMSE). Our numerical results reveal that a harmonious coexistence of the heterogeneous services can be achieved with properly configured average signal-to-noise ratios and pilot length.

We consider a resource-constrained IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by either commanding the corresponding sensor to send a fresh status update or retrieving the most recently received measurement from the cache. We aim to find a control policy at the edge node to minimize the average age of information (AoI) of the received measurements upon requests, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. We develop a low-complexity algorithm – termed relax-then-truncate – and prove that it is asymptotically optimal as the number of sensors goes to infinity. Numerical results assess the performance of the proposed method.

We consider a multi-source status update system consisting of multiple independent sources, one server, and one sink. The packets of the sources are generated according to Poisson processes and served according to a generally distributed service time. We consider a system with no waiting buffer and model it as a multi-source M/G/1/1 queueing model. We introduce a source-aware preemptive packet management policy and subsequently derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, when a packet arrives, the possible packet of the same source in the system is replaced by the fresh packet. Simulation results show the performance of the packet management policy.

We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users’ requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. First, we derive a Markov decision process model and propose an iterative algorithm that obtains an optimal policy. Then, we develop an asymptotically optimal low-complexity algorithm – termed relax-then-truncate – and prove that it is optimal as the number of sensors goes to infinity. Simulation results illustrate that the proposed relax-then-truncate approach significantly reduces the average on-demand AoI compared to a request-aware greedy policy and a weighted AoI policy, and also depict that it performs close to the optimal solution even for moderate numbers of sensors.

We consider a multi-source status update system, where each source generates status update packets according to a Poisson process which are then served according to a generally distributed service time. For this multi-source M/G/1/1 queueing model, we consider a self-preemptive packet management policy and derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, an arriving fresh packet preempts the possible packet of the same source in the system. Furthermore, we derive the MGFs of the AoI and peak AoI for the globally preemptive and non-preemptive policies, for which only the average AoI and peak AoI have been derived earlier. Finally, we use the MGFs to derive the average AoI and peak AoI in a two-source M/G/1/1 queueing model under each policy. Numerical results show the effect of the service time distribution parameters on the average AoI. The results also highlight the importance of higher moments of the AoI.

We consider a status update system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors, subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem, we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problem versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than$60~\%$saving in the average total transmit power compared to a baseline policy.

Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted$\ell _{2,1}$-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximuma posterioriprobability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

We consider a multi-source relaying system where the sources independently and randomly generate status update packets which are sent to the destination with the aid of a buffer-aided relay through unreliable links. We formulate a stochastic optimization problem aiming to minimize the sum average age of information (AAoI) of sources under per-slot transmission capacity constraints and a long-run average resource constraint. To solve the problem, we recast it as a constrained Markov decision process (CMDP) problem and adopt the Lagrangian method. We analyze the structure of an optimal policy for the resulting MDP problem that possesses a switching-type structure. We propose an algorithm that obtains a stationary deterministic near-optimal policy, establishing a benchmark for the system. Simulation results show the effectiveness of our algorithm compared to benchmark algorithms.

Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC). The sparse pattern in users’ activity allows to solve the JUICE as a compressed sensing problem in a multiple measurement vector (MMV) setup. This paper addresses the JUICE under the practical spatially correlated fading channel. We formulate the JUICE as an iterative reweighted ℓ2,1-norm optimization. We develop a computationally efficient alternating direction method of multipliers (ADMM) approach to solve it. In particular, by leveraging the second-order statistics of the channels, we reformulate the JUICE problem to exploit the covariance information and we derive its ADMM-based solution. The simulation results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances.

Block-sparse signal recovery without knowledge of block sizes and boundaries, such as those encountered in multi-antenna mmWave channel models, is a hard problem for compressed sensing (CS) algorithms. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse recovery based on popular CS based regularizers with the function input variable related to total variation (TV). Contrary to conventional approaches that impose the regularization on the signal components, we regularize the SBL hyperparameters. This iterative TV-regularized SBL algorithm employs a majorization-minimization approach and reduces each iteration to a convex optimization problem, enabling a flexible choice of numerical solvers. The numerical results illustrate that the TV-regularized SBL algorithm is robust to the nature of the block structure and able to recover signals with both block-patterned and isolated components, proving useful for various signal recovery systems.

We consider a multi-source status update system in which status updates are transmitted as packets containing the measured value of the monitored process and a time stamp representing the time when the sample was generated. The packets of each source are generated according to a Poisson process and served according to an exponentially distributed service time. We assume that the received status update packets need further processing before being used (hence, computation intensive). This is mathematically modeled by an additional server at the sink. The sink server serves the packets according to an exponentially distributed service time. We introduce two packet management policies, a preemptive policy and a blocking policy, and derive the moment generating function (MGF) of the AoI of each source under the both policies. In the both policies, the system can contain at most two packets, one at the transmitter server and one at the sink server. In the preemptive policy, a new arriving packet preempts any possible packet that is currently under service regardless of the packet’s source index. In the blocking policy, when a server is busy at the arrival instant of a packet, the arriving packet is blocked and cleared. We assume that the same preemptive/blocking policy is employed in both the transmitter and sink server. Numerical results are provided to assess the results.

As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control. We consider an IoT status update system with users, energy harvesting sensors, and a cache-enabled edge node. The users receive time-sensitive information about physical quantities, each measured by a sensor. Users demand for the information from the edge node whose cache stores the most recently received measurements from each sensor. To serve a request, the edge node either commands the sensor to send an update or retrieves the aged measurement from the cache. We aim at finding the best actions of the edge node to minimize the average AoI of the served measurements at the users, termed on-demand AoI. We model this problem as a Markov decision process and develop reinforcement learning (RL) algorithms: model-based value iteration and model-free Q-learning. We also propose a Q-learning method for the realistic case where the edge node is informed about the sensors’ battery levels only via the status updates. The case under transmission limitations is also addressed. Furthermore, properties of an optimal policy are characterized. Simulation results show that an optimal policy is a threshold-based policy and that the proposed RL methods significantly reduce the average cost compared to several baselines.

We study the information freshness under three different source aware packet management policies in a status update system consisting of two independent sources and one server. The packets of each source are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We derive the average age of information (AoI) of each source using the stochastic hybrid systems (SHS) technique for each packet management policy. In Policy 1, the queue can contain at most two waiting packets at the same time (in addition to the packet under service), one packet of source 1 and one packet of source 2. When the server is busy at an arrival of a packet, the possible packet of the same source waiting in the queue (hence, source-aware) is replaced by the arrived fresh packet. In Policy 2, the system (i.e., the waiting queue and the server) can contain at most two packets, one from each source. When the server is busy at an arrival of a packet, the possible packet of the same source in the system is replaced by the fresh packet. Policy 3 is similar to Policy 2 but it does not permit preemption in service, i.e., while a packet is under service all new arrivals from the same source are blocked and cleared. Numerical results are provided to assess the fairness between sources and the sum average AoI of the proposed policies.

A reconfigurable intelligent surface (RIS) can shape the radio propagation by passively changing the directions of impinging electromagnetic waves. The optimal control of the RIS requires perfect channel state information (CSI) of all the links connecting the base station (BS) and the mobile station (MS) via the RIS. Thereby the channel (parameter) estimation at the BS/MS and the related message feedback mechanism are needed. In this paper, we adopt a two-stage channel estimation scheme for the RIS-aided millimeter wave (mmWave) MIMO channels using an iterative reweighted method to sequentially estimate the channel parameters. We evaluate the average spectrum efficiency (SE) and the RIS beamforming gain of the proposed scheme and demonstrate that it achieves high-resolution estimation with the average SE comparable to that with perfect CSI.

We consider a status update system consisting of two independent sources, one server, and one sink. The packets of different sources are generated according to the Poisson process and the packets are served according to an exponentially distributed service time. We consider the following packet management policy. When the system is empty, any arriving packet immediately enters the server; when the server is busy, a packet of a source waiting in the queue is replaced if a new packet of the same source arrives. We derive the average age of information (AoI) of the considered M/M/1 queueing model by using the stochastic hybrid systems (SHS) technique. Numerical results are provided to show the effectiveness of the proposed policy.

We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-term cost which trade-offs between the age of information and energy consumption. We propose a practical reinforcement learning approach that finds an optimal policy without knowing the exact battery levels of the sensors. Simulation results show that the proposed method significantly reduces the average cost compared to several baseline methods.

Information freshness is crucial in a wide range of wireless applications where a destination needs the most recent measurements of a remotely observed random process. In this paper, we study the information freshness of a single-server multi-source M/M/1 queueing model under a first-come first-served (FCFS) serving policy. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the multi-source M/M/1 queueing model. Simulation results are provided to validate the derived exact expression for the average AoI.

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters.

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS - the remote RDF - is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.

In the context of the next generation wireless networks, freshness of status update packets is essential for enabling the services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of a multi-source M/G/1 first-come first-served (FCFS) queueing model, where each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated using the average age of information (AoI). To this end, we derive a closed-form expression for the average AoI of each source. As particular cases of our general expressions, we also derive closed-form expressions of the average AoI for both multi-source M/M/1 and single-source M/G/1 queueing models.

This paper addresses lossy distributed source coding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by each sensor, followed by the joint reconstruction of the sources at the decoder. We develop a novel complexity-constrained distributed variable-rate quantized CS method, which minimizes a weighted sum between the mean square error signal reconstruction distortion and the average encoding rate. The encoding complexity of each sensor is restrained by pre-quantizing the encoder input, i.e., the CS measurements, via vector quantization. Following the entropy-constrained design, each encoder is modeled as a quantizer followed by a lossless entropy encoder, and variable-rate coding is incorporated via rate measures of an entropy bound. For a two-sensor system, necessary optimality conditions are derived, practical training algorithms are proposed, and complexity analysis is provided. Numerical results show that the proposed method achieves superior compression performance as compared with baseline methods, and lends itself to versatile setups with different performance requirements.

We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a sensor observes a sparse information source indirectly. The compressed noisy measurements are communicated to the decoder for signal reconstruction with the aim to minimize the mean square error distortion. An analytically tractable lower bound to the remote rate-distortion function (RDF), i.e., the conditional remote RDF, is derived by providing support side information to the encoder and decoder. For this setup, the best encoder separates into an estimation step and a transmission step. A variant of the Blahut-Arimoto algorithm is developed to numerically approximate the remote RDF. Furthermore, a novel entropy coding based quantized CS method is proposed. Numerical results illustrate the main rate-distortion characteristics of the lossy CS, and compare the performance of practical quantized CS methods against the proposed limits.

Lossy compressed sensing (CS) of a sparse source is studied. A lower bound to the best achievable compression performance in a finite rate CS setup is established by providing support side information to the encoder and decoder. The rate-distortion problem is formulated via remote source coding and conditional rate-distortion theory. The best encoder separates into an estimation step and a rate-dependent transmission step. Numerical results illustrate the rate-distortion behavior of the scheme.

This paper considers sequential compressed acquisition and progressive reconstruction of spatially and temporally correlated sensor data streams in wireless sensor networks (WSNs) via compressed sensing (CS). We develop a sequential framework based on sliding window processing, in which the sink can efficiently reconstruct the current sensors' readings from a sequence of periodically delivered CS measurements by exploiting the joint compressibility via Kronecker sparsifying bases. Specifically, we derive a recursive CS recovery method which utilizes the estimates from the preceding decoding instants via a regularization and reweighted ℓ1-minimization to improve the reconstruction accuracy of sensor data streams while reducing the necessary communications. As beneficial features, the method produces estimates for the current sensors' readings without additional decoding delay, and, via adjusting the window size, it can dynamically trade-off between the CS recovery performance and decoding complexity. Numerical results show that our proposed method achieves higher reconstruction accuracy with a smaller number of required transmissions, and with lower decoding delay and complexity as compared to those of the state of the art CS methods.

This paper addresses compressed acquisition and progressive reconstruction of spatially and temporally correlated signals in wireless sensor networks (WSNs) via compressed sensing (CS). We propose a novel method based on sliding window processing, where the sink periodically collects CS measurements of sensor samples, and then, instantaneously reconstructs current WSN samples by exploiting the spatio-temporal correlation via Kronecker sparsifying bases. By using previous estimates as prior information, the method can progressively improve the reconstruction accuracy of the signal ensemble. Furthermore, the method can control the trade-off between decoding delay and complexity. Numerical results demonstrate that the proposed method can recover WSN data samples from CS measurements with higher reconstruction accuracy, yet with lower decoding delay and complexity, as compared to the state of the art methods.

We consider a distributed total transmit power minimization in a multi-hop single-sink data gathering wireless sensor network by jointly optimizing the resource allocation and the routing with given source rates. An inherent coupling in optimal routing and resource allocation is taken into account via cross-layer optimization to increase the energy efficiency of the network. Instead of distributing the solution process horizontally by commonly used dual decomposition, we apply consensus optimization in conjunction with the alternating direction method of multipliers (ADMM). By duplicating flow variables, the problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Numerical examples show that the proposed algorithm converges significantly faster as compared to the state of the art methods based on the dual decomposition. Additionally, the algorithm is appealing for practical implementation due to its low local communication overhead, robust operation in slightly changing channel conditions and scalability to large networks.

This paper proposes a fast distributed optimization algorithm for total transmit power minimization in single-sink data gathering wireless sensor networks. Many of the existing decentralized optimization algorithms addressing cross-layer design over the physical and network layer are based on dual decomposition. Our design includes joint power and routing optimization with given source rates by using consensus mechanism in conjunction with alternating direction method of multipliers (ADMM). Thus, the problem is decoupled across the nodes via introducing local copies of the variables, which are then iteratively driven into consensus with the ADMM. By the numerical experiments, the proposed distributed algorithm is shown to converge significantly faster to near optimal solutions with a small amount of local variable exchange as compared to the existing methods based on the dual decomposition.