Multiple Time Slots Research Articles

2D Generalized Optical Spatial Modulation for MIMO-OWC Systems

In this paper, a novel two-dimensional (2D) generalized optical spatial modulation (GOSM) scheme is proposed for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. By grouping multiple successive time slots as one time block, 2D GOSM mapping can be performed not only in the space domain but also in the time domain. Specifically, two types of 2D GOSM mapping schemes are designed, including 2D-1 and 2D-2 GOSM mappings. Moreover, to address the high complexity issue of optimal joint maximum-likelihood (ML) detection and the noise amplification and error propagation issues of zero-forcing-based ML (ZF-ML) detection, a deep neural network (DNN)-aided detection scheme is further designed for 2D GOSM systems. Simulation results demonstrate the superiority of the proposed 2D GOSM scheme with deep learning-aided detection for high-speed and low-complexity MIMO-OWC systems. More specifically, a remarkable 3.4-dB signal-to-noise ratio (SNR) gain can be achieved by 2D GOSM in comparison to the conventional one-dimensional (1D) GOSM, when applying the DNN-aided detection.

Precoding Design for Multi-User MISO Systems With Delay-Constrained and -Tolerant Users

In both academia and industry, multi-user multiple-input single-output (MU-MISO) techniques have shown enormous gains in spectral efficiency by exploiting spatial degrees of freedom. So far, an underlying assumption in most of the existing MU-MISO design has been that all the users use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering delay-constrained users whose blocklength tends to be finite. In this paper, we consider a heterogeneous setting in MU-MISO systems where delay-constrained users and delay-tolerant users coexist, called a DCTU-MISO network. To maximize the sum spectral efficiency in this system, we present the spectral efficiency for delay-tolerant users and provide a lower bound of the spectral efficiency for delay-constrained users. We consider an optimization problem that maximizes the sum spectral efficiency of delay-tolerant users while satisfying the latency constraint of delay-constrained users, and propose a generalized power iteration (GPI) precoding algorithm that finds a principal precoding vector. Furthermore, we extend a DCTU-MISO network to the multiple time slots scenario and propose a recursive GPI precoding algorithm. In simulation results, we validate proposed methods outperform baseline schemes and present the effect of network parameters on the average sum spectral efficiency.

Deep Learning-Based Overhead Minimizing Hybrid Beamforming for Wideband mmWave Systems

Hybrid beamforming (HBF) design for wideband millimeter-wave (mmWave) systems has two challenges: 1) design complexity due to frequency selectivity; 2) large pilot overhead and feedback overhead due to heavy dependence on the channel information. This letter proposes a deep learning-based HBF method for wideband mmWave systems. First, we simplify the complex HBF design problem into a network optimization problem by designing the loss function with spectral efficiency as an optimization objective. Then, we replace the instantaneous channel state information with channel statistics information of multiple time slots to achieve low system overhead. Specifically, we develop an HBF network (HBFNet) based on convolutional neural network with the channel covariance matrix (CCM) as input and the hybrid beamformer as output. Meanwhile, we design a constraint layer to satisfy the constant modulus constraint and power constraint in HBF design. In addition, we adopt the imperfect CCM for training to make the proposed method more robust.

RIS-Aided Ground-Aerial NOMA Communications: A Distributionally Robust DRL Approach

A reconfigurable intelligent surface (RIS) aided air-to-ground uplink non-orthogonal transmission framework is investigated for next generation multiple access. Occupying the same spectrum resource, unmanned aerial vehicle (UAV) users and ground users (GUs) are connected to terrestrial cellular networks via the uplink non-orthogonal multiple access (NOMA) protocol. As the flight safety is important for employing UAVs in civil airspace, the collision avoidance mechanism has to be considered during the flight. Therefore, a joint optimization problem of the UAV trajectory design, RIS configuration, and uploading power control is formulated for maximizing the network sum rate, while ensuring the UAV’s fight safety and satisfying the minimum data rate requirements of both the UAV and GU. The resultant problem is a sequential decision making one across multiple coherent time slots. Besides, the unknown locations of obstacles bring uncertainties into the decision making process. To tackle this challenging problem, a sample-efficient deep reinforcement learning (DRL) algorithm is proposed to optimize the UAV trajectory, RIS configuration, and power control simultaneously. Moreover, considering the ambiguous uncertainties in the environment, a distributionally robust DRL algorithm is further proposed to provide the worst-case performance guarantee. Numerical results demonstrate that the two proposed DRL algorithms outperform the conventional ones in terms of learning efficiency and robustness. It is also shown that the network sum rate is significantly improved by the proposed RIS-NOMA scheme compared to the conventional RIS-orthogonal multiple access (OMA) scheme and the case where no RIS is deployed.

Compressive Sensing-Based Joint Activity and Data Detection for Grant-Free Massive IoT Access

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.

Periodic Updates for Constrained OCO with Application to Large-Scale Multi-Antenna Systems

In many dynamic systems, decisions on system operation are updated over time, and the decision maker requires an online learning approach to optimize its strategy in response to the changing environment. When the loss and constraint functions are convex, this belongs to the general family of online convex optimization (OCO). In existing OCO works, the environment is assumed to vary in a time-slotted fashion, while the decisions are updated at each time slot. However, many wireless communication systems permit only periodic decision updates, <i>i.e.</i> each decision is fixed over multiple time slots, while the environment changes between the decision epochs. The standard OCO model is inadequate for these systems. Therefore, in this work, we consider periodic decision updates for OCO. We aim to minimize the accumulation of time-varying convex loss functions, subject to both short-term and long-term constraints. Feedback information about the loss functions within the current update period may be delayed and incomplete. We propose an efficient algorithm, termed Periodic Queueing and Gradient Aggregation (PQGA), which employs novel periodic queues together with possibly multi-step aggregated gradient descent to update the decisions over time. We derive upper bounds on the dynamic regret, static regret, and constraint violation of PQGA. As an example application, we study the performance of PQGA for network virtualization in a large-scale multi-antenna system shared by multiple wireless service providers. Simulation results show that PQGA converges fast and substantially outperforms the current best alternative.

An Opportunistic Power Control Scheme for Mitigating User Location Tracking Attacks in Cellular Networks

Cellular networks have been successfully evolved over the decades. Especially, Long-Term Evolution (LTE) has been exceedingly successful and the security threats against LTE systems have increased rapidly. Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracking</i> LTE user devices has been shown to be effective as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporary</i> user identifiers (IDs) are easily extracted and used to locate targeted devices by passive eavesdroppers. We notice that naive approaches, such as frequent updates of temporary user IDs, are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insufficient</i> to mitigate user-tracking attacks since the new and old temporary IDs for the same user device are easily <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linkable</i> by adversaries who can measure the wireless channel characteristics between the user device and herself. In this paper, we propose an opportunistic uplink power control scheme to minimize the probability of successful user tracking by an adversary whose location is unknown. We devise the notion of average inference error probability in order to measure the level of users’ location privacy. Moreover, we derive the closed-form expression of the approximated average inference error probability and formulate an optimization problem to maximize the average inference error probability under a constraint of an allowable power budget for each user. Against a passive adversary, our proposed power control scheme effectively degrades an adversary’s inference ability by 50% when 10 users are scheduled in each transmission time slot, which will lead to almost 100% inference error at the adversary over multiple time slots.

Data structures and simulation algorithms for flow-shop scheduling with resource availability constraints

Flow-shop scheduling problems are classic examples of multi-resource and multi-operation optimization problems. This paper demonstrates our model and simulation for solving Flow-shop scheduling optimization problems with resource availability constraints to minimize makespan. Working hours, transfer times and setup times are taken into account. Working hours could be interpreted in two ways. In the first option, operations must fit into a single available time slot. In the second option, the simulation could cut operations across multiple time slots. We propose a simulation algorithm that accounts for both scenarios and could be used with any permutation-based exact, heuristic, or search algorithm.

Compressed Sensing Based Multiuser Detection of Grant-Free NOMA With Dynamic User Activity

In this letter, by exploiting both the temporal correlation of active user and signal statistical information, we propose a compressed sensing based multi-user detection for uplink grant-free non-orthogonal multiple access where the user activity varies dynamically in one frame consisting of multiple time slots. In the proposed method, channel estimation and data detection are implemented jointly by efficient slot-wise generalized approximate message passing (GAMP) algorithm, active user detection (AUD) is implemented by message passing (MP) algorithm, and GAMP and MP exchanges information through turbo iterations. Specifically, the user activity in each time slot and active user correlation in one frame are introduced as variable and check nodes, respectively, to tackle the dynamic user activity directly in the factor graph for MP-based AUD. Finally, the performance of the proposed method is demonstrated by computer simulations.

Opportunistic Bits in Short-Packet Communications: A Finite Blocklength Perspective

In this paper, the concept of opportunistic bits (OBs) is developed in short-packet communications and investigated from a finite blocklength perspective. In the OB-based transmission, the data unit of a packet is divided into two parts: OBs and conventional bits (CBs). The OBs are not physically transmitted but used to indicate the index of the time slot (TS) when the packet containing CBs is transmitted. The loading of a bulk of OB-based packets into multiple TSs can be modelled as a Repeated Balls-into-Bins process with a multi-queue storage. If the bulk is not large enough, certain combination(s) of OBs will not appear, which leaves certain TS(s) empty and hence reduces the TS load efficiency. To evaluate the OB-based transmission performance, we formulate its maximal payload rate and TS load efficiency. With the aid of these two formulations, the energy gain, the goodput, and the latency of OB-based short-packet communications are derived and obtained in analytical forms. For achieving further insights, illustrative numerical results on the resource utilisation efficiency and the performance not only substantiate the advantages of the OB-based transmission over the conventional but also provide useful tools and specifications for its design in massive short-packet communications.

Dynamic spectrum cartography via canonical polyadic tensor decomposition

Spectrum cartography aims at estimating multidimensional radio map from limited geographical observations. Recovering unobserved elements in a radio map, from sequential spectrum observations over a long period in large geographic areas, poses major challenges to real-time requirement and storage. As historical information from dynamic spectrum observations is not well utilized, traditional spectrum cartography methods, which are statically applied on single or multiple time slots, are not well suitable for time-varying scenarios. To address these challenges, we propose two novel radio map reconstruction algorithms based on tensor canonical polyadic decomposition (CPD), called dynamic window size based CPD (DW-CPD) and incremental CPD (I-CPD). The dynamic window size of DW-CPD is derived based on Kruskal condition of tensor CPD, and I-CPD is derived based on an exponentially weighted least squares criterion. We prove that the solution of I-CPD converges to a stationary point of the tensor completion problem, which validates that the proposed I-CPD is suitable for applications in estimating dynamic CP factor. Simulations results show that DW-CPD and I-CPD based algorithms have similar estimation performance for dynamic spectrum cartography, and are better than the existing methods. Due to the overall advantage of performance, running time and storage, I-CPD based algorithm is preferable for real-time applications.

On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user-devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point (AP), each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing a nested descent algorithm, primal-dual subgradient and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, resulting in better wireless charging performance. The charged energy over an extended period of multiple time-slots both with and without computation offloading can be substantial. Wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices.

Data Collection in Radio Frequency (RF) Charging Internet of Things Networks

This letter considers minimizing the time required to collect L bits from each Radio Frequency (RF)-energy harvesting device serviced by a multi-antenna Hybrid Access Point (HAP). We outline a Mixed Integer Non-Linear Program (MINLP) to determine the transmit power allocation of the HAP and devices over multiple time slots. We also outline a Receding Horizon Control (RHC) approach coupled with a Gaussian Mixture Model (GMM) to optimize the transmit power allocation of the HAP. Our results show that the performance of our approach is within 5% of the optimal solution.

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Combining Power Allocation and Superposition Coding for an Underlay Two-way Decode-and-forward Scheme

Analysis of Random Access in NB-IoT Networks With Three Coverage Enhancement Groups: A Stochastic Geometry Approach

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.

Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems

In this paper, we study the channel estimation problem in correlated massive multiple-input-multiple-output (MIMO) systems with a reduced number of radio-frequency (RF) chains. Importantly, other than the knowledge of channel correlation matrices, we make no assumption as to the structure of the channel. To address the limitation in the number of RF chains, we employ hybrid beamforming, comprising a low dimensional digital beamformer followed by an analog beamformer implemented using phase shifters. Since there is no dedicated RF chain per transmitter/receiver antenna, the conventional channel estimation techniques for fully-digital systems are impractical. By exploiting the fact that the channel entries are uncorrelated in its eigen-domain, we seek to estimate the channel entries in this domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Under a total energy budget, we aim to design the hybrid transmit beamformer (precoder) and the receive beamformer (combiner) in each training time slot, in order to estimate the channel using the minimum mean squared error criterion. To this end, we choose the precoder and combiner in each time slot such that they are aligned to transmitter and receiver eigen-directions, respectively. Further, we derive a water-filling-type expression for the optimal energy allocation at each time slot. This expression illustrates that, with a low training energy budget, only significant components of the channel need to be estimated. In contrast, with a large training energy budget, the energy is almost equally distributed among all eigen-directions. Simulation results show that the proposed channel estimation scheme can efficiently estimate correlated massive MIMO channels within a few training time slots.

Random Interleaving Multiplexing Based IRSA Random Access System for Satellite-Enabled Internet-of-Things

A random access framework based on random interleaving multiplexing (RIM) is proposed to further improve throughput for internet of things in satellite networks, which resort to the state-of-art physical layer techniques that can resolve multiple packet collisions issue. The interleavers are randomly selected by active devices from a set of available interleavers, which are used to differentiate signals of different devices sharing a subframe that consists of multiple time slots. The message passing detector (MPD) is utilized to decode collided packets. By means of transmission diversity and interference cancellation, the proposed scheme can be linked with the irregular repetition slotted ALOHA (IRSA), the RIM-IRSA protocol is proposed. The analysis of packet loss ratio (PLR) of the RIM-IRSA is given by using the density evolution method, and the optimal degree distributions are given. Simulation results show that the RIM-IRSA outperforms traditional IRSA protocol.

Efficient formulation for transportation scheduling of single refinery multiproduct pipelines

Multiproduct pipeline transportation scheduling is a complex operations research problem that is characterized by the movement of the cargo rather than the carrier. Hence, it cannot be solved using vehicle routing methods. While most formulations for short-term scheduling adopt a continuous-time representation, they often lead to suboptimal solutions because of the dependence on the number of time slots in the grid, which is difficult to predict. Furthermore, some of these formulations have poor linear relaxations due to the presence of inefficient big-M constraints. In this paper, we develop a discrete-time mixed integer linear programming (MILP) model for the detailed scheduling of a straight pipeline with a single refinery and multiple depots. The proposed formulation rigorously detects interface material generated between adjacent products and considers planned shutdowns in pipeline segments due to maintenance operations, as well as local market demands occurring at multiple intermediate due dates. The main novelty is that continuous tasks can span multiple time slots to enforce minimum batch sizes on injection and delivery nodes, which allows for the model to generate better schedules than those obtained with previously proposed formulations. To ensure an efficient model by design, we rely on generalized disjunctive programming (GDP) and on the convex hull reformulation of disjunctions, which results in stronger and often more computationally efficient formulations. We present numerical results for a set of benchmark instances and show that the proposed model applies to large-scale industrial cases.

Mixed-Timescale Channel Estimation for MIMO Relay Multi-User Systems Based on the PARAFAC Decomposition

This letter presents novel mixed-timescale channel estimation algorithms for uplink multiple-input multiple-output (MIMO) relay multi-user systems, considering the mixed-timescale property of practical dual-hop channel. With the one-stage training (OST) scheme, we formulate the channel estimation problem based on the parallel factor (PARAFAC) decomposition using the received pilots at multiple time slots, where the second-hop channel is viewed as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-timescale</i> variable and the first-hop channel is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-timescale</i> . Then, the alternating least-square (LS) fitting method in batch mode (Batch-ALS) and online recursive alternating LS (Online-RALS) algorithms are proposed for mixed-timescale channel estimation. Simulation results show that the proposed algorithms improve the estimation performance significantly compared to the existing single-timescale channel estimation algorithms.

A network flow-based model for operations planning in home health care delivery

PurposeTo solve the problem, a mathematical model is proposed; it relies on a directed acyclic graph (DAG), in which arcs are used to indicate whether a pair of appointments can be assigned to the same route or not (and so to the same care worker). The proposed model aims at minimizing the personnel required to meet daily demand and balancing workloads among the workers while considering the varying traffic patterns derived from traffic congestion.Design/methodology/approachThis paper aims at providing solution approaches for addressing the problem of assigning care workers to deliver home health-care (HHC) services, demanding different skills each. First, a capacity planning problem is considered, where it is necessary to define the number of workers required to satisfy patients' requests and then, patients are assigned to the care workers along with the sequence followed to visit them, thus solving a scheduling problem. The benefits obtained by permitting patients to propose multiple time slots where they can be served are also explored.FindingsThe results indicate that the problem can be efficiently solved for medium-sized instances, that is, up to 100 daily patient requests. It is also indicated that asking patients to propose several moments when they can receive services helps to minimize the need for care workers through more efficient route allocations without affecting significantly the balance of the workloads.Originality/valueThis article provides a new framework for modeling and solving a HHC routing problem with multiskilled personnel. The proposed model can be used to identify efficient daily plans and can handle realistic characteristics such as time-dependent travel times or be extended to other real-life applications such as maintenance scheduling problems.