Statistical Quality Of Service Constraints Research Articles

Buffer-Aided Relaying in NOMA-Based MTC Networks With Finite Blocklength and Statistical QoS Constraints

Machine-type communication (MTC) is one of the main enabling technologies to support various applications with diverse quality of service (QoS) requirements. Finite blocklength transmission has great potential in meeting the strict delay requirements of delay-sensitive MTC devices (MTCDs), while also causing loss of network capacity due to the decoding error probability. Aiming at this problem, we introduce uplink non-orthogonal multiple access (NOMA) and buffer-aided relaying to assist the finite blocklength transmission with delay requirements for improving the achievable effective capacity (EC), which is defined as the maximum short-packet constant arrival rate under specific statistical QoS constraints. To solve the EC maximization problem, we derive the closed-form expression of time allocation coefficient. Then we establish a concave lower bound of EC using successive convex approximation (SCA) for power allocation of MTCDs, and formulate a non-cooperative game based distributed power allocation algorithm for relay. Furthermore, a joint time and power allocation (JTPA) algorithm is proposed to implement joint resource allocation. Simulation results show that under finite blocklength and statistical QoS constraints, adopting buffer-aided relaying can improve EC by 41.82% compared with no-buffer relaying. Moreover, the achievable EC of JTPA algorithm is only 3.12% lower than that of exhaustive search while reducing complexity.

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Unmanned aerial vehicles (UAVs) as aerial base stations have attracted great attention in emergency communication networks due to flexible deployment. With the popularization of smart devices, the demand for multimedia services is increasing in disaster relief. Therefore, it is very important to significantly improve throughput of unmanned aerial vehicle base station (UAV-BS) while ensuring quality-of-service (QoS) of multimedia traffic. In this paper, we consider a UAV-BS to serve a group of users in the downlink who have different statistical delay-bound QoS requirements in an emergency situation. We formulate a problem to maximize the sum statistical-QoS-guaranteed throughput (effective capacity) of all users by jointly optimizing the UAV's 3D location, power and bandwidth allocation under each user's statistical QoS requirement constraint. The formulated problem is a non-linear non-convex optimization problem, which is very difficult to solve. To this end, we propose an efficient iteration algorithm based on the block coordinate descent and successive convex optimization techniques to solve it. Specifically, we decouple the primary problem into three sub-problems, which can be approximated as easy-to-solve convex optimization problems. In each iteration, three sub-problems are alternately optimized. Finally, numerical simulation results prove the effectiveness of our proposed algorithm compared with benchmarks.

Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints

In this article, we study the performance of an uplink non-orthogonal multiple access (NOMA) network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We first propose novel closed-form expressions for the EC in a two-user NOMA network and show that in the high signal-to-noise ratio (SNR) region, the “strong” NOMA user, referred to as U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , has a limited EC, assuming the same delay constraint as the “weak” user, referred to as U1. We demonstrate that for the weak user U1, OMA and NOMA have comparable performance at low transmit SNRs, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. Furthermore, we show that at high transmit SNRs, irrespective of whether the application is delay tolerant, or not, the performance gains of NOMA over OMA for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and OMA over NOMA for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> remain unchanged. When the delay QoS of one user is fixed, the performance gap between NOMA and OMA in terms of total EC increases with decreasing statistical delay QoS constraints for the other user. Next, by introducing pairing, we show that NOMA with user-pairing outperforms OMA, in terms of total uplink EC. The best pairing strategies are given in the cases of four and six users NOMA, raising once again the importance of power allocation in the optimization of NOMA's performance.

Optimal Resource Allocation for Energy-Harvesting Communication Networks Under Statistical QoS Constraints

In this paper, we characterize the optimal strategies focusing on the throughput and the system energy efficiency of wireless-powered communication networks (WPCNs) in the presence of delay-limited sources. Each energy-harvesting user equipment is assumed to be subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent θ. Correspondingly, the time allocation strategies for downlink energy harvesting and uplink information transfer depend on these QoS constraints, potentially overriding the doubly near-far problem of WPCNs. We consider the non-orthogonal transmission and time-division multiple access protocols for the uplink information transfer of WPCN, and for both cases, we formulate energy efficiency and throughput maximizing problems to obtain the globally optimal solution that satisfy the statistical QoS constraints. Since these optimization problems fall into concave or pseudo-concave categories, Karush-Kuhn-Tucker conditions are necessary and sufficient for global optimality, using which we obtain analytical expressions for the optimal operating intervals. In addition, in several cases, due to difficulty in providing closed-form expressions, we develop algorithms to solve the problems numerically. Finally, we provide the simulation results to confirm and further analyze the theoretical characterizations. We mainly observe that QoS constraints primarily affect the optimal time allocation policies as well as achievable rate distribution among the users, enabling us to overcome the doubly near-far problem of energy-harvesting communications networks pointed out as one of the critical issues in the literature.

Outage Effective Capacity of Buffer-Aided Diamond Relay Systems Using HARQ-IR

In this paper, transmission over buffer-aided diamond relay systems under statistical quality of service (QoS) constraints is studied. The statistical QoS constraints are imposed as limitations on delay violation probabilities. In the absence of channel state information (CSI) at the transmitter, truncated hybrid automatic repeat request-incremental redundancy (HARQ-IR) is incorporated to make better use of the wireless channel and the resources for each communication link. The packets that cannot be successfully received upon the maximum number of transmissions will be removed from buffer, i.e., outage occurs. The outage effective capacity of a communication link is defined as the maximum constant arrival rate to the source that can be supported by the goodput departure processes, i.e., the departure that can be successfully received by the receiver. Then, the outage effective capacity for the buffer-aided diamond relay system is obtained for HARQ-IR incorporated transmission strategy under the end-to-end delay constraints. It is shown that HARQ-IR can achieve the same performance as the one achieved with the decode-and-forward protocol with perfect CSI at the transmitters under certain delay constraints.

Achievable Throughput of Energy Harvesting Fading Multiple-Access Channels Under Statistical QoS Constraints

This paper studies the achievable throughput of fading multiple-access channels (MACs) with energy harvesting transmitters subject to statistical quality of service (QoS) constraints in the form of limitations on the buffer overflow probability. Effective capacity, which characterizes the maximum constant arrival rate that a given process can support while satisfying the QoS constraints, is employed as the performance metric. Perfect channel state information (CSI) and energy arrivals are assumed to be available at both the transmitters and the receiver. With the assumption of naive power control scheme, in which the transmission power level is irrespective of the CSI and decided by the instantaneous harvested energy, the effective throughput regions of time division multiple access and superposition coding (SC) with fixed decoding are characterized. In the special case of the same QoS constraints, the optimal decoding order strategy for SC with variable decoding order is obtained. With the assumption that the channel states and harvested energy in all time slots are known at the transmitters, the point-to-point link is first revisited. Then, for a given decoding order strategy in two-user MACs, a suboptimal power control policy based on the average energy arrivals and causal energy and channel information is proposed and shown to achieve performance close to the optimal one.

Heterogeneous Statistical QoS Provisioning Over Wireless Powered Sensor Networks

The wireless energy transfer, which is a promising technology for the wireless sensor networks (WSNs), can efficiently solve the energy scarcity that results from the application boosting. However, not only the energy scarcity, but also the quality of service (QoS) guarantees need to be taken into account for the WSNs. Different types of applications in the WSNs impose the new challenge on heterogeneous QoS provisioning for the WSNs. To solve the above problem, in this paper, we develop the joint downlink energy assignment and uplink power control scheme with the heterogeneous statistical QoS provisioning (HeP) for wireless powered sensor networks (WPSNs). In particular, we build up the HeP model, where the aggregate effective capacity (AEC) is defined as the aggregate throughput under the statistical QoS constraints for the WPSNs. Based on the mode, we formulate the AEC maximization problems for uniformed time division and dynamic time allocation scenarios, respectively. For the uniformed time division scenario, we divide the AEC maximization problem into the hybrid access point determined downlink energy assignment problem and the sensor node determined uplink power control problem. Then, we solve these problems and obtain the corresponding closed-form solutions. For the dynamic time allocation scenario, we develop the joint time allocation, downlink energy assignment, and uplink power control scheme to maximize the AEC and iteratively derive the scheme. Extensive simulations are conducted to demonstrate the effect of heterogeneous statistical QoS on our developed resource allocation schemes for WPSNs. The results show that the HeP resource allocation schemes are superior to the schemes with homogeneous statistical QoS guarantees.

Optimal ALOHA-Like Random Access With Heterogeneous QoS Guarantees for Multi-Packet Reception Aided Visible Light Communications

There is a paucity of random access protocols designed for alleviating collisions in visible light communication (VLC) systems, where carrier sensing is hard to achieve due to the directionality of light. To resolve the problem of collisions, we adopt the successive interference cancellation (SIC) algorithm to enable the coordinator to simultaneously communicate with multiple devices, which is referred to as the multi-packet reception (MPR) capability. However, the MPR capability could be fully utilized only when random access algorithms are properly designed. Considering the characteristics of the SIC aided random access VLC system, we propose a novel effective capacity (EC)-based ALOHA-like distributed random access algorithm for MPR-aided uplink VLC systems having heterogeneous quality-of-service (QoS) guarantees. First, we model the VLC network as a conflict graph and derive the EC for each device. Then, we formulate the VLC QoS-guaranteed random access problem as a saturation throughput maximization problem subject to multiple statistical QoS constraints. Finally, the resultant non-concave optimization problem is solved by a memetic search algorithm relying on invasive weed optimization and differential evolution. We demonstrate that our derived EC expression matches the Monte Carlo simulation results accurately, and the performance of our proposed algorithms is competitive.

Optimal Resource Allocation for Buffer-Aided Relaying With Statistical QoS Constraint

We consider a three-node buffer-aided relaying network with statistical quality-of-service (QoS) constraint in terms of maximum acceptable end-to-end queue-length bound outage probability. In particular, we study the adaptive link selection relaying problem that aims to maximize the constant supportable arrival rate $\mu$ to the source (i.e., the effective capacity). Fixed and adaptive source and relay power allocation are investigated. By employing asymptotic delay analysis, we first convert the QoS constraint into minimum QoS exponent constraints at the source and relay queues. We then derive the link selection and power allocation solutions as functions of the instantaneous link conditions and QoS exponents using Lagrangian approach. Solutions for various special cases of link conditions and QoS constraints are presented. Moreover, we compare the effective capacities of the proposed relaying schemes and other existing schemes under different link conditions and QoS constraints. Illustrative results indicate that the proposed schemes offer substantial performance gains, and power adaption outperforms fixed power allocation at low signal-to-noise power ratio (SNR) region or under loose QoS constraints.

Energy-Efficient Resource Allocation for MIMO-OFDM Systems Serving Random Sources With Statistical QoS Requirement

This paper optimizes resource allocation that maximizes the energy efficiency (EE) of wireless systems with statistical quality of service (QoS) requirement, where a delay bound and its violation probability need to be guaranteed. To avoid wasting energy when serving random sources over wireless channels, we convert the QoS exponent, a key parameter to characterize statistical QoS guarantee under the framework of effective bandwidth and effective capacity, into multi-state QoS exponents dependent on the queue length. To illustrate how to optimize resource allocation, we consider multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A general method to optimize the queue length based bandwidth and power allocation (QRA) policy is proposed, which maximizes the EE under the statistical QoS constraint. A closed-form optimal QRA policy is derived for massive MIMO-OFDM system with infinite antennas serving the first order autoregressive source. The EE limit obtained from infinite delay bound and the achieved EEs of different policies under finite delay bounds are analyzed. Simulation and numerical results show that the EE achieved by the QRA policy approaches the EE limit when the delay bound is large, and is much higher than those achieved by existing policies considering statistical QoS provision when the delay bound is stringent.

Energy-Efficiency Optimization for MIMO-OFDM Mobile Multimedia Communication Systems With QoS Constraints

It is widely recognized that, in addition to the quality-of-service (QoS), energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy-efficiency model is first proposed for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) mobile multimedia communication systems with statistical QoS constraints. Employing the channel-matrix singular value decomposition (SVD) method, all subchannels are classified by their channel characteristics. Furthermore, the multichannel joint optimization problem in conventional MIMO-OFDM communication systems is transformed into a multitarget single-channel optimization problem by grouping all subchannels. Therefore, a closed-form solution of the energy-efficiency optimization is derived for MIMO-OFDM mobile multimedia communication systems. As a consequence, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems. Simulation comparisons validate that the proposed EEOPA algorithm can guarantee the required QoS with high energy efficiency in MIMO-OFDM mobile multimedia communication systems.

Energy-Efficient Power Allocation for Delay-Sensitive Multimedia Traffic Over Wireless Systems

With the extremely high penetration rate of wireless broadband devices, the telecommunication industry predicts that the mobile data traffic will double almost every year for the next five years with more than two-thirds of the future traffic being mobile video. This trend clearly indicates that future mobile broadband networks will face the dual challenge of supporting large data volumes while providing reliable service for delay-sensitive mobile multimedia streams. This explosive demand for mobile multimedia communications has significantly increased not only spectrum requirements but also energy consumption. Energy-efficient communication is becoming a prime concern for mobile multimedia communications partially due to the fact that improvements in battery technology occur on a much slower scale than gains in processing power and energy consumption of electronic devices. Furthermore, the ever-increasing demand for wireless services and ubiquitous network access comes at the expense of a growing carbon footprint for the mobile wireless communication industry. This paper addresses resource allocation to maximize the energy efficiency of a wireless link under statistical quality-of-service (QoS) constraints for mobile multimedia traffic. In the energy-efficiency analysis, both circuit and transmit power values of mobile devices are taken into account. The joint impact of statistical QoS constraints, underlying circuit power consumption, transmission power, and spectral bandwidth is considered. The unique globally optimal power allocation scheme for delay-sensitive multimedia traffic is characterized. Intuition about optimal strategies is obtained by looking at both the low-signal-to-noise-ratio (SNR) regime and the high-SNR regime.

Achievable Throughput Regions of Fading Broadcast and Interference Channels under QoS Constraints

Transmission over fading broadcast and interference channels in the presence of quality of service (QoS) constraints is studied. Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical QoS constraints, is employed as the performance metric. In the broadcast scenario, the effective capacity region achieved with superposition coding and successive interference cancellation is identified and is shown to be convex. Subsequently, optimal power control policies that achieve the boundary points of the effective capacity region are investigated, and an algorithm for the numerical computation of the optimal power adaptation schemes for the two-user case is provided. In the interference channel model, achievable throughput regions are determined for three different strategies, namely treating interference as noise, time division with power control and simultaneous decoding. It is demonstrated that as in Gaussian interference channels, simultaneous decoding expectedly performs better (i.e., supports higher arrival rates) when interfering links are strong, and treating interference as noise leads to improved performance when the interfering cross links are weak while time-division strategy should be preferred in between. When the QoS constraints become more stringent, it is observed that the sum-rates achieved by different schemes all diminish and approach each other, and time division with power control interestingly starts outperforming others over a wider range of cross-link strengths.

Resource allocation for OFDM relay systems with statistical QoS guarantees

SUMMARYBy integrating the concept of effective capacity, we propose the resource allocation schemes for subcarrier‐pair based OFDM decode‐and‐forward and amplify‐and‐forward relay systems for quality‐of‐service (QoS) guarantees. The objective is to maximize the system throughput subject to a given statistical delay QoS constraint. First, we pair the subcarriers over the source‐relay channel and the relay‐destination channel by the descending order of the subcarriers' channel gains. Second, by making use of the derived equivalent end‐to‐end channel gains of the subcarrier pairs, we apply joint water‐filling power allocation over the subcarrier pairs and then partition the power of the subcarrier pairs between the source and the relay. We prove that as the equivalent end‐to‐end channel gains of the subcarrier pairs are given, the combination of sorted subcarrier pairing and joint water‐filling power allocation is jointly optimal to maximize the effective capacity. The simulation results show that our proposed schemes achieve the highest effective capacity for OFDM decode‐and‐forward and amplify‐and‐forward relay systems as compared with other existing schemes. The results also verify that our proposed schemes can efficiently provide different levels of delay QoS guarantees, even if under the stringent delay QoS constraints. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Optimal Resource Allocation for Spectrum Sensing Based Cognitive Radio Networks with Statistical QoS Guarantees

Resource allocation under spectrum sensing based dynamic spectrum sharing strategy is a critically important issue for cognitive radio networks (CRNs), because they need to not only satisfy the interference constraint caused to the primary users (PUs), but also meet the quality-of-service (QoS) requirements for the secondary users (SUs). In this paper, we develop the optimal spectrum sensing based resource allocation scheme for the delay QoS constrained CRNs. Specifically, we aim at maximizing the maximum constant arrival rate of the SU that can be supported by the time-varying service process subject to the given statistical delay QoS constraint. In our derived power allocation scheme, not only the average transmit and interference power constraints are considered, but also the impact of the PUs’ transmission to the CRNs and the PUs’ spectrum-occupancy probability are taken into consideration. Moreover, the spectrum sensing errors are also taken into consideration. Simulation results show that, (1) the effective capacity of the secondary link decreases when the statistical delay QoS constraint becomes stringent; (2) given the QoS constraint, the effective capacity of the secondary link varies with the interference power constraint and the SNR of the primary link.

Energy efficiency in multiaccess fading channels under QoS constraints

In this article, transmission over multiaccess fading channels under quality-of-service (QoS) constraints is studied in the low-power and wideband regimes. QoS constraints are imposed as limitations on the buffer violation probability. The effective capacity, which characterizes the maximum constant arrival rates in the presence of such statistical QoS constraints, is employed as the performance metric. A two-user multiaccess channel model is considered, and the minimum bit energy levels and wideband slope regions are characterized for different transmission and reception strategies, namely time-division multiple-access (TDMA), superposition coding with fixed decoding order, and superposition coding with variable decoding order. It is shown that the minimum received bit energies achieved by these different strategies are the same and independent of the QoS constraints in the low-power regime, while they vary with the QoS constraints in the wideband regime. When wideband slope regions are considered, the suboptimality of TDMA with respect to superposition coding is proven in the low-power regime. On the other hand, it is shown that TDMA in the wideband regime can interestingly outperform superposition coding with fixed decoding order. The impact of varying the decoding order at the receiver under certain assumptions is also investigated. Overall, energy efficiency of different transmission strategies under QoS constraints are analyzed and quantified.

Performance Analysis of Cognitive Radio Systems under QoS Constraints and Channel Uncertainty

In this paper, performance of cognitive transmission over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users (SUs) are assumed to initially perform channel sensing to detect the activities of the primary users, and then attempt to estimate the channel fading coefficients through training. Energy detection is employed for channel sensing, and different minimum mean-square-error (MMSE) estimation methods are considered for channel estimation. In both channel sensing and estimation, erroneous decisions can be made, and hence, channel uncertainty is not completely eliminated. In this setting, performance is studied and interactions between channel sensing and estimation are investigated. Following the channel sensing and estimation tasks, SUs engage in data transmission. Transmitter, being unaware of the channel fading coefficients, is assumed to send the data at fixed power and rate levels that depend on the channel sensing results. Under these assumptions, a state-transition model is constructed by considering the reliability of the transmissions, channel sensing decisions and their correctness, and the evolution of primary user activity which is modeled as a two-state Markov process. In the data transmission phase, an average power constraint on the secondary users is considered to limit the interference to the primary users, and statistical limitations on the buffer lengths are imposed to take into account the QoS constraints of the secondary traffic. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

Effective capacity region of two-user opportunistic spectrum access

The effective capacity region of the two-user opportunistic spectrum access (OSA) system in Rayleigh fading environment is derived in this paper. Although OSA is a contemporary research topic, little attention has been given to the capacity analysis under quality-of-service (QoS) constraints. Hence, we use the effective capacity, which is a powerful tool to analyze the QoS requirements in wireless communication systems, to study the sustainable packet arrival rate of the secondary users while meeting statistical QoS constraints. We derive the effective capacity region of the two-user OSA system and propose a method to obtain the capacity region boundary. In addition, by combining the finite state channel model and the activity of the primary user, an extended finite state channel model for the two-user OSA system is also proposed.

Adaptive Modulation in Spectrum-Sharing Channels Under Delay Quality-of-Service Constraints

We propose a variable-rate variable-power M-level quadrature amplitude modulation (MQAM) scheme employed under delay quality-of-service (QoS) constraints over spectrum-sharing channels. An underlay cognitive radio system, with one primary user and a secondary user, with constraints on interference leakage imposed by the primary receiver is considered. The transmission parameters of the secondary user are set optimally such that the successful communications for the primary user in terms of a minimum rate to be supported is satisfied, irrespective of co-existence with the secondary user. We obtain interference constraints that, when satisfied by the secondary users, is sufficient to satisfy the service outage requirement of the primary user. We further study the performance of the secondary user's link employing an adaptive MQAM scheme when, on top of the aforementioned interference constraint, the secondary user is also required to satisfy a statistical delay QoS constraint. Considering two modulation schemes, i.e., continuous MQAM and discrete MQAM with restricted constellations, we obtain the effective capacity of the secondary user's link and derive the optimum power allocation that maximizes the effective capacity of the secondary user.

Effective Capacity Analysis of Cognitive Radio Channels for Quality of Service Provisioning

In this paper, the performance of cognitive radio systems is studied when the secondary users operate under statistical quality of service (QoS) constraints. In the cognitive radio channel model, secondary users initially perform channel sensing, and then engage in data transmission at two different average power levels depending on the channel sensing results. A state transition model is constructed to model this cognitive transmission channel. Statistical QoS constraints are imposed as limitations on buffer violation probabilities. Effective capacity of the cognitive radio channel, which provides the maximum throughput under such QoS constraints, is determined. This analysis is conducted for fixed-power/fixed-rate, fixed-power/variable-rate, and variable-power/variable-rate transmission schemes under different assumptions on the availability of channel side information (CSI) at the transmitter. The interactions and tradeoffs between the throughput, QoS constraints, and channel sensing parameters (e.g., sensing duration and threshold, and detection and false alarm probabilities) are investigated. The performances of fixed-rate and variable-rate transmission methods are compared in the presence of QoS limitations. It is shown that variable schemes outperform fixed-rate transmission techniques if the detection probabilities are high. Performance gains through adapting the power and rate are quantified and it is shown that these gains diminish as the QoS limitations become more stringent.