We consider the problem of allocating 5G radio resources over wireless communication links to control a series of independent low-latency wireless control systems common in industrial settings. Each control system sends state information to the base station to compute control signals under tight latency requirements. Such latency requirements can be met by restricting the uplink traffic to a single subframe in each 5G frame, thus ensuring a millisecond latency bound while leaving the remaining subframes available for scheduling overhead and coexisting broadband traffic. A linear assignment problem can be formulated to minimize the expected number of packet drops, but this alone is not sufficient to achieve good performance. We propose an optimal scheduling with respect to a control operation cost that allocates resources based on current control system needs. The resulting control-aware scheduling method is tested in simulation experiments that show drastically improved performance in 5G settings relative to control-agnostic scheduling under the proposed time-sliced frame structure.

We consider the problem of allocating radio resources over wireless communication links to control a series of independent low-latency wireless control systems common in industrial settings. Supporting wireless control in time sensitive settings requires fast data rates over wireless links, which comes at the cost of reliability. It is challenging to meet both latency and reliability requirements with an equal or arbitrary allocation of resources. We thus propose a novel control-aware approach to the low-latency scheduling problem in which we incorporate control and channel state information in allocating bandwidth and data rates across the wireless links. Control systems that are in desirable states are given modest requirements on error rates, while systems in undesirable states are given more priority. We derive control-aware packet error rate targets for each system to satisfy stability goals and make scheduling decisions to meet such targets while reducing total transmission time. The resulting control-aware based method is tested in simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.

We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are necessary in enabling time-sensitive control systems with high sampling rates to operate over wireless links. Enabling low-latency through fast data rates comes at the cost of reliability in the form of higher packet error rates due to channel noise. However, the impact of such communication link errors on the control system performance depends dynamically on the control system state. We propose a novel control-aware communication design to the low-latency resource allocation problem. In our proposed method, we incorporate both control and channel state information in scheduling transmissions across time slots, frequency bands, and data rates using the next-generation Wi-Fi scheduling architecture. Control systems that are closer to instability or further from a desired range in a given control cycle are given higher packet delivery rate targets to meet. Rather than a simple priority ranking, we derive precise adaptive packet error rate targets for each system needed to satisfy control-specific performance requirements. We use these adaptive rate targets to make scheduling decisions that reduce total transmission time. The resulting control-aware low-latency scheduling (CALLS) method is tested in numerous simulation experiments that demonstrate its effectiveness in meeting control-based goals under tight latency constraints relative to control-agnostic scheduling.

Many industrial automation systems rely on time-synchronized (and timely) communication among sensing, computing, and actuating devices. Advances in Ethernet enabled by time-sensitive networking (TSN) standards, being developed by the IEEE 802.1 TSN Task Group, are significantly improving time synchronization as well as worst case latencies. Next-generation industrial systems are expected to leverage advances in distributed time coordinated computing and wireless communications to enable greater levels of automation, efficiency, and flexibility. Significant progress has been made in extending accurate time synchronization over the air (e.g., 802.1AS profile for IEEE 802.11/Wi-Fi). Given the inherently unreliable, varying capacity and latency prone characteristics associated with wireless communications, proving the feasibility of worst case latency performance over the wireless medium is a major research challenge. More specifically, understanding what levels of capacity, reliability, and latency could be guaranteed over wireless links with high reliability are important research questions to guide the development of new radios, protocols, and time coordinated applications. This paper provides an overview of the potential applications, requirements, and unique research challenges to extend TSN capabilities over wireless. The paper also describes advances in wireless technologies (e.g., next-generation 802.11 and 5G standards) toward achieving reliable and accurate time distribution and timeliness capabilities. It also provides a classification of wireless applications and a reference architecture for enabling the integration of wired and wireless TSN capabilities in future industrial automation systems.

In this paper, we study rate allocation mechanisms that allocate available transmission rate of a particular secondary user (SU) among its different classes of services assuming that a specific channel assignment algorithm across active SUs is in place. In particular, we formulate the rate allocation mechanism of a SU between its two different classes of services namely, delay sensitive (DS) and best effort (BE) services as a Markov decision process. Then the optimal rate allocation mechanism that minimizes the average queuing delay of DS packets while guaranteeing the packet loss probabilities of both classes of packets is obtained using a linear programming technique. Since the optimal rate allocation mechanism can be complex for implementation in a SU device due to its probabilistic nature, we also study a low-complexity suboptimal rate allocation mechanism. For this suboptimal scheme, we develop a queuing analytic model to measure and compare different data-link layer performance parameters e.g., packet loss probability and queuing delay distribution. The developed analytic model for the suboptimal scheme is further useful for a call admission decision. We compare the performances of the suboptimal rate allocation mechanism with its optimal counterpart. Presented simulation results show that the suboptimal mechanism has a quite similar performance as the optimal mechanism.

In this paper, we study rate allocation mechanisms that allocate available transmission rate of a particular cognitive radio (CR) user among its different class of services. In particular, we formulate the rate allocation mechanism of a CR user between its two different class of services namely, delay sensitive (DS) and best effort (BE) services as a Markov decision process. Then the optimal rate allocation mechanism that minimizes the average queuing delay of DS service while guaranteeing the packet loss probabilities of both class of services is obtained using a linear programming technique. Since the optimal rate allocation mechanism can be complex to implement in practice, we study a low-complexity suboptimal rate allocation mechanism. For this suboptimal scheme, we develop a queuing analytic model in order to measure different quality parameters. Selected numerical results show that the performance of suboptimal rate allocation mechanism is quite similar to the optimal rate allocation mechanism for the considered system parameters. The developed queuing analytic model is also useful for call admission controller design when the suboptimal scheme is employed.

In this paper, we propose robust power allocation and admission control schemes for providing probabilistically constrained quality of service (QoS) in selective relaying based decode-and-forward (DF) cooperative cellular systems. The proposed schemes are robust against imperfect channel state information (CSI) in slow fading while optimizing the total uplink transmit power in these cooperative wireless networks. At first, we derive novel closed-form solutions for the optimization problem, where the objective is to minimize total uplink transmit power while meeting the probabilistic QoS guarantees for a given number of admitted users. This is achieved by approximating the probabilistic optimization problem into a convex deterministic form and then by deriving closed form analytical solutions for power allocation using Karush-Kuhn-Tucker (KKT) conditions. The closed-form property of these solutions allows us later to develop a very low-complexity suboptimal algorithm for joint admission control and power allocation in presence of imperfect CSI and selective relaying. We also conduct comprehensive simulation experiments to demonstrate the effectiveness of our proposed schemes and to highlight the benefits gained from considering channel estimation errors in resource allocation for cooperative cellular systems.

In this paper, we develop robust power allocation schemes for cognitive radio networks (CRNs) that can operate in multiple bands with cooperative relays considering uncertainty among the channels of secondary user (SU) network and among the channels of SU transmitters to primary user (PU) receivers. Our objective is to formulate the robust design optimization problems taking into account the interference threshold in the PU band specified by the regulatory guidelines. To optimally allocate power with channel uncertainty, two robust algorithms are developed: (i) the worst-case optimization, where the interference constraints are satisfied for all channels contained in some bounded uncertainty regions, and (ii) the probabilistically constrained optimization, where interference constraints are satisfied with certain probabilities. We show that the formulated problems are convex, which can be efficiently solved. Numerical results show the effectiveness of the proposed schemes and the implications of ignoring the uncertainties among different channels when designing power allocation schemes for CRNs.

In this paper, we develop a power allocation scheme that works robustly under channel uncertainty for a decode-and-forward (DF) cooperative cellular multi-user network with multiple fixed relays. We propose a method for selective relaying, where cooperation takes place only if it is beneficial for the network in terms of total transmit power minimization or source power savings. Our objective is to provide energy efficiency by minimizing the power consumption of the network while meeting the rate requirements of the users. We consider the probabilistically constrained optimization approach, in which quality of service (QoS) constraint is satisfied for all users with certain probabilities. We transform the probabilistic optimization problem into a deterministic one and derive closed form analytical solutions. Simulation results show the effectiveness of our proposed algorithm.

In this paper, we propose joint relay selection and power allocation algorithms that can work robustly under imperfect channel knowledge for a decode-and-forward (DF) cellular relay network. The objective is to minimize the uplink transmit power of the network taking each user's target data rate as the quality of service (QoS) constraint in the presence of imperfect channel state information (CSI). We consider the worst-case optimization approach, in which QoS constraint is satisfied for all users assuming both probabilistic and deterministic channel estimation error models. In this optimization framework, equivalent convex formulations are derived for the nonlinear optimization problems that are often combinatorially hard to solve in their original forms. After relay selection, efficient centralized as well as distributed power allocation algorithms scalable with respect to the size of the networks are developed. We also consider the case of power constrained networks where the objective is to provide QoS in the presence of limited power budgets on source and relays. The robust optimization problem is reformulated accordingly and efficient solution is provided. Numerical results show the effectiveness of the proposed algorithms and demonstrate the implications of ignoring channel estimation errors while developing relay selection and power allocation algorithms.

In this paper, we propose a joint relay selection and power allocation algorithm for a multi-user, multi relay cellular network using decode-and-forward cooperation strategy with imperfect knowledge of channels. The objective is to minimize the total uplink power of the network taking each user's target data rate as the quality of service constraint under imperfect channel state information (CSI), assuming some statistical knowledge is known about the channel estimation error. Joint relay selection and power allocation is a mixed integer programming problem which is combinatorially hard. For that an efficient sub-optimal solution with low complexity using convex relaxation approach is proposed. After relay selection, a centralized power allocation scheme is developed to optimally allocate the power among the users and the selected relay nodes. Then a low-complexity distributed algorithm is proposed based on a standard primal decomposition approach, where each selected relay can independently minimize its own power. Numerical results demonstrate the effectiveness of the proposed algorithms.

In this paper, we propose centralized and distributed power allocation algorithms for a multi-user, multi relay cellular network using decode-and-forward (DF) cooperation strategy taking channel uncertainty into account. The objective is to minimize the total uplink power of the network taking each user's target data rate as the quality of service (QoS) constraint under imperfect channel state information (CSI). We consider the worst-case optimization approach, in which QoS constraint is satisfied for all channels contained in some uncertainty region. First, a centralized power allocation scheme is developed to optimally allocate the power among the users and the relay nodes. Then, a suboptimal distributed algorithm is proposed based on a standard primal decomposition approach, where each relay can independently and separately minimize its own power. The proposed solutions are based on second order cone programming (SOCP), which is computationally efficient. Simulation results show that the performance of the suboptimal distributed solution is near-optimal and reveals the fact that DF cooperation strategy is more efficient in total power reduction and more robust under channel uncertainty over non-cooperative systems.

Dynamic resource availability and lack of central control unit offer many challenges while designing medium access control (MAC) protocol for a distributed cognitive radio network (DCRN). In this paper, we propose a novel MAC design for DCRN which provides an efficient approach to address quality of service (QoS) requirements of delay sensitive applications by defining higher priority to such applications during channel reservation. It also combats other major challenges such as efficient spectrum utilization, multi-channel hidden terminal problem (MHTP) and collision with primary user (PU) due to sensing error at SU. We develop an analytical framework to study the performance of the proposed protocol. We then compare the performance of proposed protocol with those of two existing protocols. Comparison results show that proposed MAC outperforms the existing protocols by providing better throughput and reducing DCRN users' collision probability with PUs in presence of sensing error. The results achieved from the analytical model and validated by simulations show that our simple yet efficient design identifies and fulfils the QoS requirements of delay sensitive applications, achieves excellent spectrum utilization, shows superb robustness in presence of sensing errors and handles MHTP effectively.

High demand for mobile voice puts forth the requirement of high VoIP capacity over WiMAX networks. Although WiMAX networks are well-known for high-speed internet access on the go, VoIP capacity over WiMAX is low due to large control overhead. In this paper, we propose Group Resource Allocation (GRA) mechanism that reduces the control overhead and hence increases VoIP Capacity over WiMAX. In GRA multiple users are provided allocation as a group. Grouping is based on common traffic and channel characteristics and uses bitmaps to signal resource allocation information to users. Grouping of users and use of bitmaps reduces control overhead. Simulation results show that a 40% gain in VoIP capacity over WiMAX can be achieved with Group Resource Allocation.

In this paper, we propose a novel multiuser cooperative communication scheme using random network coding for uplink wireless transmissions. The participating users transmit a linear combination of their new data packet and the packets successfully decoded due to preceding transmissions by other users, thus sending the new information as well as relaying the information for other users at the same time. The coefficients used for linear combination are selected at random from the finite field used to carry out the network coding. At the base station, original data packets are recovered from the received packets by solving the system of linear equations in the finite field. The proposed scheme is very robust against inter-user channel noise and can offer very high diversity gain in moderate inter-user channel conditions. Moreover, the transmission delay required by the proposed scheme is 2N as compared to N² in the traditional Time Division Multiple Access (TDMA) cooperative systems. The performance benefits of the proposed scheme are demonstrated through simulation analysis.

Cognitive radio (CR) is a recent innovation based on an intelligent radio design paradigm that can vastly improve utilization of valuable radio spectrum. However, successful deployment of CR technology in next generation wireless networks requires many new research challenges to be addressed, especially in the medium access control (MAC) layer. In this paper, we propose a novel MAC design, referred as Opportunistic Multichannel Cognitive MAC (OMC-MAC), for distributed CR networks to address some of these most important design issues. We also develop an analytical framework to study the performance of our proposed protocol in terms of important performance measures. The results achieved from the analytical model and validated by simulation, show that our simple yet efficient design achieves excellent spectrum utilization, shows superb robustness in presence of sensing error and handles multichannel hidden terminal problem very effectively.

We develop a queuing analytic model to study cross-layer effects on Quality of Service (QoS) performance for downlink transmission in a Vertical Bell Laboratories Layered Space-Time Architecture (V-BLAST) Multiple Input Multiple Output (MIMO) wireless system exploiting multiuser diversity. As in multiuser Single Input Single Output (SISO) systems, multiuser diversity has been shown to have a great potential in improving system throughput in multiuser MIMO systems as well. In a V-BLAST MIMO system, the transmit antennas can carry parallel streams and multiple users can be scheduled for transmissions at the same time. We consider a multiuser diversity scheme that can effectively exploit this extra dimension in multiuser scheduling. To investigate the cross-layer aspect of the performance improvement, we perform a queuing analysis to derive buffer statistics, queuing delay distribution, packet throughput and loss rate experienced in the data link layer when this multiuser diversity technique is deployed in the system. We also present selected numerical results to show how the queuing model can help us to relate these important QoS measures to relevant physical layer and traffic parameters. Usefulness of the developed analytical model is also demonstrated through example applications.

We develop a queueing analytic framework to study the data link layer quality-of-service performance measures for cognitive radio users in an infrastructure-based dynamic spectrum access environment. In order to allocate the available spectrum white spaces among the cognitive radio users in a spectrum overlay scenario, an opportunistic scheduling scheme is considered. The queueing model considers bursty traffic arrival pattern at the cognitive radio user ends, finite buffer size, activity of primary users (i.e., dynamic channel availability), and correlated channel fading. We present a step-by-step procedure to derive the delay distribution, average throughput, and packet loss rate for the cognitive radio users. The proposed framework facilitates cross-layer design for improved QoS experience in cognitive radio networks. Usefulness of the developed analytical model is demonstrated through example applications.

The IEEE 802.11e standard developed by IEEE 802.11TGe intends to provide the quality of service (QoS) support in IEEE 802.11-based wireless local area networks (WLANs) through the introduction of hybrid coordination function (HCF). The HCF controlled channel access (HCCA) designed as a part of HCF is the medium access mechanism for parameterized QoS and is suitable for multimedia applications requiring hard QoS guarantees. The standard also defines a reference scheduler to complement HCCA in meeting these guarantees. In this paper, we investigate the performance of the reference scheduler described in the standard by using a novel queueing analytic framework. The analysis reveals the performance deficiencies of the reference scheduler. Afterwards, to overcome these deficiencies, we propose a new scheduling scheme, namely, the prediction and optimization-based HCCA (PRO-HCCA), based on the insights gained from the queueing analysis. Simulation experiments show that the proposed scheme overcomes the problems of the reference scheduler and successfully enables the HCCA to fulfill the QoS guarantees for multimedia applications.

Cognitive radio (CR) technology is an innovative radio design philosophy in order to increase the spectrum utilization by exploiting the unused spectrum in dynamically changing environments. Specifically, the CR technology will allow a group of potential users (referred to as secondary users) to access available spectrum resources unused by primary users (PUs) for whom the band has been licensed. The spectrum available for secondary users (SUs) depends on PUs' activity in the licensed spectrum. In this paper, we develop a queuing analytic framework to study important performance measures experienced by SUs in a CR network. We study queuing delay and buffer statistics of SUs' packets by modeling PUs' activity as a two state Markov chain and SUs' channel quality variation as a finite state Markov chain (FSMC). In order to allocate available channels among the SUs, an opportunistic channel allocation scheme is considered. The proposed framework facilitates to design an admission controller for SUs' network in order to maintain a given quality service (QoS) requirement which is specified in the form of statistical delay guarantee.

The IEEE 802.11e standard developed by IEEE 802.11TGe intends to provide the quality of service (QoS) support for the popular 802.11 based wireless local area networks (WLAN) through the introduction of hybrid coordination function (HCF). The HCF controlled channel access (HCCA) designed as a part of HCF is the medium access mechanism for parameterized QoS and is suitable for multimedia applications requiring hard QoS guarantees. The standard also defines scheduling and admission control schemes to complement HCCA in meeting these guarantees. However, the reference scheduler has several limitations which render it incapable of providing QoS guarantees to many multimedia applications. In this paper, we propose a new scheduling scheme which overcomes these limitations to provide critical performance guarantees. Simulation experiments show that the proposed scheme successfully enables the HCCA to fulfill the QoS guarantees for wide variety of multimedia applications.

The IEEE 802.11e draft standard currently being developed by IEEE 802.11TGe proposes to enable the much needed Quality of Service (QoS) support for the popular 802.11 based wireless local area networks (WLAN) through the introduction of Hybrid Coordination Function (HCF). The HCF Controlled Channel Access (HCCA) designed as a part of HCF is the medium access mechanism for parameterized QoS and is suitable for multimedia applications requiring hard QoS guarantees. The draft standard also defines scheduling and admission control schemes to complement HCCA in meeting these guarantees. However, most of the popular multimedia applications generate Variable Bit Rate (VBR) traffic that brings challenge to the HCCA and its scheduler and admission controller design. This paper introduces a novel queueing analytic framework that will be useful to analyze the performance of HCCA in provisioning required QoS for VBR traffic applications. The analysis also provides important insights that could be useful to improve the HCCA scheduler and admission controller designs.

A zero-redundancy estimator is defined by a weighted sum of Krichevsky-Trofimov (KT) sequential probability estimators i.e., the minimax Bayes of the memoryless process, over all possible alphabets. This estimator is effective for non-binary sources whose alphabet is embedded in a larger alphabet. We propose a new weighting recursive computation. Next we use the estimator to construct a finite window predictor for lossless data compressor, and we show that its average redundancy for memoryless source has optimal 1st order asymptotics.

Provisioning quality of service (QoS) in Web servers has gained immense importance because Web servers are a major part of the Internet. To deliver the pledged QoS, Web service providers need control over the allocation of the resources in their Web servers. Control is also necessary for reaching the optimal resource allocation through proper service differentiation. In this paper, we propose and investigate an analytic approach that enables the service providers to deploy a differentiated service policy that offers this control. The proposed service policy is configurable by tunable control parameters. We devise the relationships between the performance measures and these parameters by adopting a unique queuing theoretic approach. Once these relationships are established, we describe how these parameters can be set to their most appropriate values depending on the objectives of the service providers. We illustrate the usefulness of our approach by conducting the analysis on a real Web trace.