Average Aggregate Throughput Research Articles

An Analytical Model for the Aggregate Throughput of IEEE 802.11ah Networks under the Restricted Access Window Mechanism.

The IEEE 802.11ah is an amendment to the IEEE 802.11 standard to support the growth of the Internet of Things (IoT). One of its main novelties is the restricted access window (RAW), which is a channel access feature designed to reduce channel contention by dividing stations into RAW groups. Each RAW group is further divided into RAW slots, and stations only attempt channel access during the RAW slot they were assigned to. In this paper, we propose a discrete-time Markov chain model to evaluate the average aggregate throughput of IEEE 802.11ah networks using the RAW mechanism under saturated traffic and ideal channel conditions. The proposed analytical model describes the behavior of an active station within its assigned RAW slot. A key aspect of the model is the consideration of the event of RAW slot time completion during a station’s backoff operation. We study the average aggregate network throughput for various numbers of RAW slots and stations in the network. The numerical results derived from our analytical model are compared to computer simulations based on an IEEE 802.11ah model developed for the ns-3 simulator by other researchers, and its performance is also compared to two other analytical models proposed in the literature. The presented results indicate that the proposed analytical model reaches the closest agreement with independently-derived computer simulations.

Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning

Grant-free multiple access (GFMA) is a promising paradigm to efficiently support uplink access of Internet of Things (IoT) devices. In this paper, we propose a deep reinforcement learning (DRL)-based pilot sequence selection scheme for GFMA systems to mitigate potential pilot sequence collisions. We formulate a pilot sequence selection problem for aggregate throughput maximization in GFMA systems with specific throughput constraints as a Markov decision process (MDP). By exploiting multiagent DRL, we train deep neural networks (DNNs) to learn near-optimal pilot sequence selection policies from the transition history of the underlying MDP without requiring information exchange between the users. While the training process takes advantage of global information, we leverage the technique of factorization to ensure that the policies learned by the DNNs can be executed in a distributed manner. Simulation results show that the proposed scheme can achieve an average aggregate throughput that is within 85% of the optimum, and is 31%, 128%, and 162% higher than that of acknowledgement-based GFMA, dynamic access class barring, and random selection GFMA, respectively. Our results also demonstrate the capability of the proposed scheme to support IoT devices with specific throughput requirements.

Intelligent Spectrum Resource Allocation Based on Joint Optimization in Heterogeneous Cognitive Radio

In conventional cognitive radio (CR), only when a primary user (PU) is detected to be absent, the idle spectrum can be accessed by a secondary user (SU), in addition, the allocation processes for spectrum resources of CR such as sensing time and transmission power are often independent. In this paper, to improve the SU's throughput in a heterogeneous CR, an intelligent spectrum resource allocation based on joint optimization is proposed. The proposed scheme allows the SU to access the PU' s spectrum not only in overlay mode but also in underlay mode. The SU accesses the spectrum with full power when PU is absent and with controlled power when PU is present. The transmission rates in the cases of perfect transmission, false alarm transmission, spectrum sharing transmission and interference transmission are analyzed, respectively, which are then used to achieve the average aggregate throughput of the SU. We have formulated spectrum resource allocation as a joint optimization problem of subchannel transmission power and spectrum sensing time, which can make the SU achieve the maximal throughput subject to the limits of minimal detection probability, maximal total power and maximal interference power for the SU. We propose an alternating diretion optimization (ADO) based joint optimization algorithm to solve the joint optimization problem. The simulation results have indicated that the proposed heterogeneous CR is superior to the traditional CR.

Joint sensing time and power allocation in cognitive networks with amplify-and-forward cooperation

Cognitive radio is a novel approach to cope with spectrum scarcity, in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently. However, it is difficult to avoid the interference between licensed and unlicensed users in various scenarios. This paper analyzes the jointly optimized allocation of sensing time and power for a two-user, amplify-and-forward (AF) cognitive network developed by maximizing the average aggregate throughput of its secondary network. In particular, this paper discusses diverse cooperation ratios for different scenarios and a unique cooperation ratio in spite of scenario changes. The observations of experiment results indicate that the sensing duration is within a strict interval. The results show that the optimized sensing time is 14.111 ms and the aggregate throughput equals to 1.1451 bps/Hz which are tractable by sequential optimization. This result indicates that by adopting the fixed cooperation ratios, the achievable throughput of the system is decreased. The system innovatively creates multiple independent fading channels to achieve technological diversity among partners.

An Achievable Throughput Scaling Law of Wireless Device-to-Device Caching Networks With Distributed MIMO and Hierarchical Cooperations

In this paper, we propose a new caching scheme for a random wireless device-to-device (D2D) network of $n$ nodes with local caches, where each node intends to download files from a prefixed library via D2D links. Our proposed caching delivery includes two stages, employing distributed MIMO and hierarchical cooperations, respectively. The distributed MIMO is applied to the first stage between source nodes and neighbors of the destination node. The induced multiplexing gain and diversity gain increase the number of simultaneous transmissions, improving the throughput of the network. The hierarchical cooperations are applied to the second stage to facilitate the transmissions between the destination node and its neighbors. The two stages together exploit spatial degrees of freedom as well as spatial reuse. We develop an uncoded random caching placement strategy to serve this cooperative caching delivery. Analytical results show that the average aggregate throughput of the network scales almost linearly with $n$ , with a vanishing outage probability. Furthermore, we derive an explicit expression of the optimal throughput as a function of system parameters, such as pathloss factor under a target outage probability. Analytical and numerical results demonstrate that our proposed scheme outperforms existing ones when the local cache size is limited.

Flow Allocation for Maximum Throughput and Bounded Delay on Multiple Disjoint Paths for Random Access Wireless Multihop Networks

In this paper, we consider random access, wireless, multi-hop networks, with multi-packet reception capabilities, where multiple flows are forwarded to the gateways through node disjoint paths. We explore the issue of allocating flow on multiple paths, exhibiting both intra- and inter-path interference, in order to maximize average aggregate flow throughput (AAT) and also provide bounded packet delay. A distributed flow allocation scheme is proposed where allocation of flow on paths is formulated as an optimization problem. Through an illustrative topology it is shown that the corresponding problem is non-convex. Furthermore, a simple, but accurate model is employed for the average aggregate throughput achieved by all flows, that captures both intra- and inter-path interference through the SINR model. The proposed scheme is evaluated through Ns2 simulations of several random wireless scenarios. Simulation results reveal that, the model employed, accurately captures the AAT observed in the simulated scenarios, even when the assumption of saturated queues is removed. Simulation results also show that the proposed scheme achieves significantly higher AAT, for the vast majority of the wireless scenarios explored, than the following flow allocation schemes: one that assigns flows on paths on a round-robin fashion, one that optimally utilizes the best path only, and another one that assigns the maximum possible flow on each path. Finally, a variant of the proposed scheme is explored, where interference for each link is approximated by considering its dominant interfering nodes only.

A Distributed Opportunistic MAC Protocol for Multichannel Wireless Networks

We propose a distributed opportunistic medium access control (MAC) scheme for maximizing the expected aggregate throughput in a multichannel wireless network such as a clustered orthogonal frequency-division multiple access (OFDMA) network. In our proposed scheme, each user attempts to send only on its best channel and transmits if the best-channel gain is higher than a given threshold, which is dynamically updated depending on previous idle and collision situations. In this way, with our proposed scheme, in a homogeneous system where the channel fading distribution is identical for all users, the best user for each channel is obtained in a distributed manner. We also obtain the optimal values of the thresholds so that the probability of successful transmission is maximized and a minimal number of transmission opportunities are wasted (e.g., due to collision or idle transmissions). In the asymptotic limit of a large number of users and sufficiently long transmission slot duration, we show that, in comparison with the optimal centralized scheme, the throughput loss for our proposed scheme goes to zero. Furthermore, we extend our distributed opportunistic MAC scheme for a homogeneous system to that for a heterogeneous system where the channel fading distribution is heterogeneous across users. Throughput performances and signaling overhead are analyzed for the proposed distributed MAC schemes and compared with those of the existing schemes. Simulation results show that our proposed schemes significantly improve the average aggregate throughput when compared with the existing schemes.

A new sensing‐throughput tradeoff scheme in cooperative multiband cognitive radio network

SUMMARYA conventional cognitive radio network (CRN) uses the spectrum of the licensed primary network (PN) on the premise of detecting the absence of the PN by the spectrum sensing of the sensor node (SN). In this paper, a cooperative multiband CRN is considered, wherein the SNs are allowed to use some time of the transmission slot to relay PN data by cooperative communication, while using the remaining time of the transmission slot to forward its own data, over multiple sub‐bands during each frame, if the presence of PN is detected by cooperative spectrum sensing of the SNs in the sensing slot. A new sensing–throughput tradeoff scheme is formulated as a multi‐variable optimization problem, which maximizes the average aggregate throughput of the CRN over all the sub‐bands by jointly optimizing spectrum sensing time and sub‐band transmission power, subject to the constraints on the average aggregate throughput of the PN, the maximal aggregate power of each SN, and the false alarm and detection probabilities of each sub‐band. The bi‐level optimization method is adopted to obtain the optimal solution by dividing the multi‐variable optimization problem into two convex single‐variable sub‐optimization problems. The simulations show that there exists the optimal sensing time and sub‐band transmission power that maximize the average aggregate throughput of the CRN and, compared with the conventional scheme, the throughput obtained by the proposed scheme is outstanding. Copyright © 2014 John Wiley & Sons, Ltd.

Raptor codes‐aided relaying for vehicular infotainment applications

Even though the main motivation behind wireless access for vehicular environments was to support time-sensitive safety applications, typical Internet applications are also required to attract private investment that would help expand the network and reduce the cost of implementing the systems. It is well known that the Automatic Repeat reQuest scheme used in the current IEEE 802.11p standard is highly inefficient for vehicular applications. Therefore a raptor-coded decode-and-forward relaying scheme with an efficient feedback channel, named the ‘r-DF scheme’ for infrastructure-to-vehicle infotainment applications is proposed in this study. The scheme is evaluated using a multi-layered simulator combining a realistic IEEE 802.11p physical and MAC layer design considering the presence of interference in a highway and an urban scenario. The simulation shows that the scheme achieved up to double the average aggregate throughput and tens of decoding time improvements over the state-of-the-art.

ADMAC with Integrated Destination Discovery for Ad Hoc Networks

Directional antennas have shown to increase spatial reuse by allowing multiple transmitters and receivers to concurrently communicate using directional beams as long as they do not significantly interfere with each other. This appreciably increases average aggregate throughput of the network. Generally, for high throughput performance the directional MAC (Medium Access Control) protocols choose a random direction for destination's location and subsequent data transmissions. Under situations of heavy load, high mobility and narrow beam-width, frequent updates are required to track the destinations. However, frequent updates may degrade the effective throughput of the network. Hence, we propose a novel Adaptive Directional MAC (ADMAC) protocol with integrated destination discovery that estimates destination's possible search span and then initiates transmission in that search span direction. Another major contribution is the average throughput performance comparison between last sector (LS), random sector (RS) and search span approaches. Average throughput results show an improvement of up to 40 % and greater than 400 %, when compared to the LS and the RS based DMAC protocols, respectively.