Statistical Queueing Constraints Research Articles

Throughput Analysis of Low-Latency IoT Systems With QoS Constraints and Finite Blocklength Codes

Internet of Things (IoT) is a promising paradigm to connect massive number of devices in future wireless communications while satisfying various quality of service (QoS) requirements. In this paper, we consider a QoS-constrained IoT system operating with finite blocklength (FBL) codes to support low latency communications. Two data arrival models are considered, namely, constant-rate arrival and ON-OFF discrete-time Markov arrival. The throughput performance is studied for both arrival models under statistical queuing constraints and deadline limits. For the scenario with instantaneous channel state information (CSI), we derive the QoS-constrained throughput expressions for both arrival models. Subsequently, an instantaneous-CSI-driven optimal power control algorithm is proposed to maximize the throughput, while guaranteeing a certain reliability target. In addition, we consider a scenario with only average CSI being available at the transmitter and propose to apply hybrid automatic repeat request (HARQ) schemes to improve the FBL performance. The decoding error probability and the outage probability are first characterized, following which the distribution of transmission period is derived. Furthermore, the throughput expressions are provided for both types of arrivals. Via numerical analysis, the impact of error probability, fixed transmission rate, coding blocklength, and QoS constraints on the throughput is studied.

Throughput-Delay Tradeoffs With Finite Blocklength Coding Over Multiple Coherence Blocks

This paper investigates the performance of wireless systems that employ finite-blocklength channel codes for transmission and operate under queuing constraints in the form of limitations on buffer overflow or delay violation probabilities. A block fading model, in which fading stays constant in each coherence block and changes independently between blocks, is considered. It is assumed that channel coding is performed over multiple coherence blocks. A simple ARQ scheme with error-free feedback without any delay is considered. The channel coding rate with given maximal error probability is considered as the service rate and is incorporated into the effective capacity formulation, which characterizes the maximum constant arrival rate that can be supported under statistical queuing constraints. Performances of variable-rate and fixed-rate transmissions are studied. The optimum error probability for variable-rate and fixed-rate transmissions is shown to be unique. The limiting performance as the number of blocks increases is characterized. The tradeoffs and the interactions between the throughput, the number of coherence blocks over which channel coding is performed, error probabilities, channel coherence duration, and queuing constraints are identified.

NOMA-Based Energy-Efficient Wireless Powered Communications

In this paper, we study the performance of non-orthogonal multiple access (NOMA) schemes in wireless powered communication networks (WPCN) focusing on the system energy efficiency (EE). We consider multiple energy harvesting user equipments (UEs) that operate based on harvest-then-transmit protocol. The uplink information transfer is carried out by using power-domain multiplexing, and the receiver decodes each UE's data in such a way that the UE with the best channel gain is decoded without interference. In order to determine optimal resource allocation strategies, we formulate optimization problems considering two models, namely half-duplex and asynchronous transmission, based on how downlink and uplink operations are coordinated. In both cases, we have concave-linear fractional problems, and hence Dinkelbach's method can be applied to obtain the globally optimal solutions. Thus, we first derive analytical expressions for the harvesting interval, and then we provide an algorithm to describe the complete procedure. Furthermore, we incorporate delay-limited sources and investigate the impact of statistical queuing constraints on the energy-efficient allocation of operating intervals. We formulate an optimization problem that maximizes the system effective-EE while UEs are applying NOMA scheme for uplink information transfer. Since the problem satisfies pseudo-concavity, we provide an iterative algorithm using bisection method to determine the unique solution. In the numerical results, we observe that broadcasting at higher power level is more energy efficient for WPCN with uplink NOMA. Additionally, exponential decay QoS parameter has considerable impact on the optimal solution, and in the presence of strict constraints, more time is allocated for downlink interval under half-duplex operation with uplink TDMA mode.

On the Throughput of Multi-Source Multi-Destination Relay Networks With Queueing Constraints

In this paper, the throughput of relay networks with multiple source–destination pairs under queueing constraints has been investigated for both variable-rate and fixed-rate schemes. When channel side information (CSI) is available at the transmitter side, transmitters can adapt their transmission rates according to the channel conditions, and achieve the instantaneous channel capacities. In this case, the departure rates at each node have been characterized for different system parameters, which control the power allocation, time allocation, and decoding order. In the other case of no CSI at the transmitters, a simple automatic repeat request (ARQ) protocol with fixed rate transmission is used to provide reliable communication. Under this ARQ assumption, the instantaneous departure rates at each node can be modeled as an ON–OFF process, and the probabilities of ON and OFF states are identified. With the characterization of the arrival and departure rates at each buffer, stability conditions are identified, and an effective capacity analysis is conducted for both cases to determine the system throughput under statistical queueing constraints. In addition, for the variable-rate scheme, the concavity of the sum rate is shown for certain parameters, helping to improve the efficiency of parameter optimization. Finally, through numerical results, the influence of system parameters and the behavior of the system throughput are identified.

Wireless Throughput and Energy Efficiency With Random Arrivals and Statistical Queuing Constraints

Throughput and energy efficiency in fading channels are studied in the presence of randomly arriving data and statistical queuing constraints. In particular, Markovian arrival models, including discrete-time Markov, Markov fluid, and Markov-modulated Poisson sources, are considered. Employing the effective bandwidth of time-varying sources and the effective capacity of time-varying wireless transmissions, maximum average arrival rates in the presence of statistical queuing constraints are characterized. For the two-state (ON/OFF) source models, throughput is determined in a closed form as a function of the source statistics, channel characteristics, and quality of service (QoS) constraints. Throughput is further studied in certain asymptotic regimes. Furthermore, energy efficiency is analyzed by determining the minimum energy per bit and a wideband slope in the low signal-to-noise ratio regime. Overall, the impact of source characteristics, QoS requirements, and channel fading correlations on the throughput and energy efficiency of wireless systems is identified.

The Impact of Statistical Delay Constraints on the Energy Efficiency in Fading Channels

This work analyzes the energy efficiency in fading channels under statistical delay constraints, imposed as limitations on the maximal delay violation probabilities. The Maximum Constant Arrival Rates are characterized with the aid of the notion of effective capacity, based on which the spectral efficiency-bit energy tradeoff is analyzed in the low-SNR regime. The analysis is performed under two assumptions: 1) perfect channel side information (CSI) at receiver only and 2) perfect CSI at both the transmitter and receiver. It is proven that the bit energy required at zero spectral efficiency is indeed the minimum bit energy. The expressions for the minimum bit energy and wideband slope are obtained. It is shown that the performances of the low-power and wideband regimes are the same, while the statistical queueing constraints have different behaviors. It is found that the minimum bit energy requirements increase as the statistical delay constraints become more stringent. Moreover, it is demonstrated that the minimum bit energy required is the same as that attained when only statistical queueing constraints are enforced in the wideband regime, while the bit energy increment is larger to achieve the same nonzero spectral efficiency. Overall, the impact upon the energy efficiency of statistical delay constraints is quantified.

Energy Efficiency of Hybrid-ARQ under Statistical Queuing Constraints

In this paper, energy efficiency of hybrid automatic repeat request (HARQ) schemes with statistical queuing constraints is studied for both constant-rate and random Markov arrivals by characterizing the minimum energy per bit and wideband slope. In particular, two queuing models are considered. Specifically, when outage occurs, the transmitter keeps the packet, lowers its priority, and attempts to retransmit it later in the first queue model, while the packet is discarded and removed from the buffer in the second queue model. For both models, energy efficiency is investigated when outage constraints, statistical queuing constraints, and deadline constraints are imposed. The deadline constraint provides a limitation on the number of retransmissions or equivalently the number of HARQ rounds. Under these assumptions, closed-form expressions are obtained for the minimum energy per bit and wideband slope for HARQ with chase combining, and comparisons among different arrival models are made. For instance, it is shown that stricter queuing constraints and more bursty sources degrade the energy efficiency by lowering the wideband slope. In the numerical results, analytical characterizations are verified through simulations. Moreover, the impact of source variations/burstiness, deadline constraints, outage probability, and queuing constraints on the energy efficiency is analyzed.

On the Throughput of Hybrid-ARQ Under Statistical Queuing Constraints

Hybrid automatic repeat request (HARQ) is a high-performance communication protocol, leading to the effective use of the wireless channel and the resources with only limited feedback about the channel state information (CSI) to the transmitter. In this paper, the throughput of HARQ with incremental redundancy (IR) and fixed transmission rate is studied in the presence of statistical queuing constraints imposed as limitations on buffer overflow probabilities. In particular, tools from the theory of renewal processes and stochastic network calculus are employed to characterize the maximum arrival rates that can be supported by the wireless channel when HARQ-IR is adopted. Effective capacity is employed as the throughput metric, and a closed-form expression for the effective capacity of HARQ-IR is determined for small values of the quality-of-service (QoS) exponent. The impact of the fixed transmission rate, queuing constraints, and hard-deadline limitations on the throughput is investigated, and comparisons with type-I HARQ and HARQ with Chase Combining are provided.

Transmission Strategies in Multiple-Access Fading Channels With Statistical QoS Constraints

Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical queueing constraints, is analyzed in a multiuser scenario. In particular, the effective capacity region of fading multiple-access channels in the presence of quality of service (QoS) constraints is studied. Perfect channel side information is assumed to be available at both the transmitters and the receiver. It is initially assumed that the transmitters send the information at a fixed power level and, hence, do not employ power control policies. Under this assumption, the performance achieved by superposition coding with successive decoding techniques is investigated. It is shown that varying the decoding order with respect to the channel states can significantly increase the achievable throughput region. In the two-user case, the optimal decoding strategy is determined for the scenario in which the users have the same QoS constraints. The performance of orthogonal transmission strategies is also analyzed. It is shown that for certain QoS constraints, time-division multiple access can achieve better performance than superposition coding if fixed successive decoding order is used at the receiver side. In the subsequent analysis, power control policies are incorporated into the transmission strategies. The optimal power allocation policies for any fixed decoding order over all channel states are identified. For a given variable decoding-order strategy, the conditions that the optimal power control policies must satisfy are determined, and an algorithm that can be used to compute these optimal policies is provided.

MIMO Wireless Communications Under Statistical Queueing Constraints

The performance of multiple-input multiple-output (MIMO) wireless systems is investigated in the presence of statistical queueing constraints. Queuing constraints are imposed as limitations on buffer violation probabilities. The performance under such constraints is captured through the effective capacity formulation. A detailed analysis of the effective capacity is carried out in the low-power, wideband, and high signal-to-noise ratio (SNR) regimes. In the low-power analysis, expressions for the first and second derivatives of the effective capacity with respect to SNR at SNR = 0 are obtained under various assumptions on the degree of channel state information at the transmitter. Transmission strategies that are optimal in the sense of achieving the first and second derivatives are identified. It is shown that while the first derivative does not get affected by the presence of queueing constraints, the second derivative gets smaller as the constraints become more stringent. Through the energy efficiency analysis, this is shown to imply that the minimum bit energy requirements do not change with more strict limitations but the wideband slope diminishes. Similar results are obtained in the wideband regime if rich multipath fading is being experienced. On the other hand, sparse multipath fading with bounded number of degrees of freedom is shown to increase the minimum bit energy requirements in the presence of queueing constraints. Following the low-SNR study, the impact of buffer limitations on the high-SNR performance is quantified by analyzing the high-SNR slope and the power offset in Rayleigh fading channels. Finally, numerical results are provided to illustrate the theoretical findings, and to demonstrate the interactions between the queueing constraints and spatial dimensions over a wide range of SNR values.

Secure Wireless Communication and Optimal Power Control Under Statistical Queueing Constraints

In this paper, secure transmission of information over fading broadcast channels is studied in the presence of statistical queueing constraints. Effective capacity is employed as a performance metric to identify the secure throughput of the system, i.e., effective secure throughput. It is assumed that perfect channel side information (CSI) is available at both the transmitter and the receivers. Initially, the scenario in which the transmitter sends common messages to two receivers and confidential messages to one receiver is considered. For this case, the effective secure throughput region, which is the region of constant arrival rates of common and confidential messages that can be supported by the buffer-constrained transmitter and fading broadcast channel, is defined. It is proven that this effective throughput region is convex implying that time-sharing between any two viable transmission and power control strategies results in effective throughput values inside the region. Then, the optimal power control policies that achieve the boundary points of the effective secure throughput region are investigated and an algorithm for the numerical computation of the optimal power adaptation schemes is provided. Additionally, the throughput region achieved by time-division multiplexing of common and confidential messages is explored. Subsequently, the special case in which the transmitter sends only confidential messages to one receiver is addressed in more detail. For this case, effective secure throughput is formulated and two different power adaptation policies are studied. These power adaptation policies are compared with the opportunistic ones that are optimal in the absence of quality of service (QoS) constraints. It is shown that opportunistic schemes, in which data transmission with high rates and high power occurs only when the main channel is much better than the eavesdropper channel, are no longer optimal under buffer constraints, and the transmitter should send the data at a certain moderate rate and power even when the main channel strength is comparable to that of the eavesdropper channel to avoid buffer overflows.

Energy Efficiency in the Low-SNR Regime under Queueing Constraints and Channel Uncertainty

Energy efficiency of fixed-rate transmissions is studied in the presence of queueing constraints and channel uncertainty. It is assumed that neither the transmitter nor the receiver has channel side information prior to transmission. The channel coefficients are estimated at the receiver via minimum mean-square-error (MMSE) estimation with the aid of training symbols. It is further assumed that the system operates under statistical queueing constraints in the form of limitations on buffer violation probabilities. The optimal fraction of power allocated to training is identified. Spectral efficiency-bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation. In particular, it is shown that the bit energy increases without bound in the low-power regime as the average power vanishes. A similar conclusion is reached in the wideband regime if the number of noninteracting subchannels grow without bound with increasing bandwidth. On the other hand, it is proven that if the number of resolvable independent paths and hence the number of noninteracting subchannels remain bounded as the available bandwidth increases, the bit energy diminishes to its minimum value in the wideband regime. For this case, expressions for the minimum bit energy and wideband slope are derived. Overall, energy costs of channel uncertainty and queueing constraints are identified, and the impact of multipath richness and sparsity is determined.