Multi-packet Reception Capability Research Articles

Energy-Efficiency Optimization With Model Convexification for Wireless Ad Hoc Networks With Multi-Packet Reception Capability

Energy efficiency is a significant requirement of resource management and design optimization in information networks. In this work, we propose an iterative fractional programming framework embedded with a distributed primal-dual extra-gradient projection algorithm, which addresses a wide class of the energy-efficiency optimization problems in wireless ad hoc networks with full-duplex radios and multi-packet reception capability. Specifically, we propose a model convexification mechanism by joining an affine transformation and an exponential transformation into the nonlinear fractional programming, which enables us to deal with the challenge arising from the complexity and non-convex structure of the original problem. With the model convexification, we can map the non-convex power control space into a convex space and equivalently derive a sequence of convex subproblems, which relaxes the convexity assumption widely adopted in the existing literature. We further propose a distributed primal-dual algorithm based on extra-gradient projection to solve the convex subproblem at each iteration of the fractional programming. The convergence of the proposed iterative fractional programming and the distributed optimization method is theoretically proven. Numerical results also verify the proposed method and demonstrate its superior performance over other representative distributed and centralized schemes in terms of achieving global energy efficiency.

Multi-round transmission and contention protocol in WLANs with multi-packet reception

Nowadays, there are many scenarios in Wi-Fi networks where many devices compete for network access via an Access Point to transmit their information. Thus, the ability to separate multiple packets sent simultaneously using advanced digital processing techniques in the physical layer, known as Multi-Packet Reception (MPR), has become crucial. As this feature may increase network throughput, medium access control (MAC) protocols that efficiently exploit the MPR capability have been investigated. This paper proposes a new MAC protocol that exploits the MPR capability in 802.11 RTS/CTS access mechanism networks. It is based on the use of multiple contention and transmission rounds. In this sense, the MPR MAC protocol schedules several transmission periods once the contention phase finishes. Multiple transmission rounds reduce the overhead and substantially increase throughput. The analytical expressions that support the proposed approach are derived and verified by simulation. The results show that the proposed MAC protocol can yield close to 94% of the ideal performance using only four transmission rounds, compared with 80% of the previous approaches in the literature.

Transmission power allocation for remote estimation with multi-packet reception capabilities

In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e.g. with the latest wireless technologies such as 5G and WiFi. To this end we introduce a general model where packet arrival probabilities are determined based on the received Signal-to-Interference-and-Noise Ratio and with two different receivers design schemes, one implementing standard multi-packet reception technique and one implementing Successive Interference Cancellation decoding algorithm in addition. Then we cast the power allocation problem as an optimization task where the mean error covariance at the remote estimator is minimized, while penalizing the mean transmission power consumption. For the infinite-horizon problem we show the existence of a stationary optimal policy, while for the finite-horizon case we derive some structural properties under the special scenario where the overall system to be estimated can be seen as a set of independent subsystems. Numerical simulations illustrate the improvement given by the proposed receivers, especially when SIC is used, over orthogonal schemes that schedule only one sensor transmission at a time in order to avoid interference.

Performance of Flow Allocation with Successive Interference Cancelation for Random Access WMNs

In this study, we explore the gain that can be achieved by jointly allocating flow on multiple paths and applying successive interference cancelation (SIC), for random access wireless mesh networks with multi-packet reception capabilities. We explore a distributed flow allocation scheme aimed at maximizing average aggregate flow throughput, while also providing bounded delay when SIC is employed. The aforementioned scheme is evaluated both in terms of delay and throughput, and is also compared with other simple flow allocation schemes. We present simulation results from three illustrative topologies. Our results show that the gain for the scheme with SIC, when compared with a variant that treats interference as noise (IAN), can be up to 15.2%, for an SINR threshold value equal to 0.5. For SINR threshold values as high as 2.0 however, SIC does not always result in higher throughput. In some scenarios, the gain of SIC over IAN is insignificant, while in some others treating interference as noise proves to be better. The reason is that, although SIC improves the throughput on a specific link, it also increases the interference imposed on neighboring receivers. We also show that the gain from applying SIC is more profound in cases of a large degree of asymmetry among interfering links.

Transmission scheduling for multi-process multi-sensor remote estimation via approximate dynamic programming

In this paper, we consider a remote estimation problem where multiple dynamical systems are observed by smart sensors, which transmit their local estimates to a remote estimator over channels prone to packet losses. Unlike previous works, we allow multiple sensors to transmit simultaneously even though they can cause interference, thanks to the multi-packet reception capability at the remote estimator. In this setting, the remote estimator can decode multiple sensor transmissions (successful packet arrivals) as long as their signal-to-interference-and-noise ratios (SINR) are above a certain threshold. In this setting, we address the problem of optimal sensor transmission scheduling by minimizing a finite horizon discounted expected estimation error covariance cost across all systems at the remote estimator, subject to an average transmission cost. While this problem can be posed as a stochastic control problem, the optimal solution requires solving a Bellman equation for a dynamic programming (DP) problem, the complexity of which scales exponentially with the number of systems being measured and their state dimensions. In this paper, we resort to a novel Least Squares Temporal Difference (LSTD) Approximate Dynamic Programming (ADP) based approach to approximating the value function. More specifically, an off-policy based LSTD approach, named in short Enhanced-Exploration Greedy LSTD (EG-LSTD), is proposed. We discuss the convergence analysis of the EG-LSTD algorithm and its implementation. A Python based program is developed to implement and analyse the different aspects of the proposed method. Simulation examples are presented to support the results of the proposed approach both for the exact DP and ADP cases.

Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices

In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The first node is connected to a power grid and it has randomly arriving data packets. Another energy harvesting (EH) sensor monitors a stochastic process and sends status updates to the destination. We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node. First, we consider a Probabilistic Random Access (PRA) policy where both nodes make independent transmission decisions based on some fixed probability distributions. We show that with this policy, the average AoI is equal to the average peak AoI, if the EH node only sends freshly generated samples. In addition, we derive the optimal solution in closed form, which reveals some interesting properties of the considered system. Furthermore, we consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory. Simulation results show that the DPP policy outperforms the PRA policy in various scenarios, especially when the destination node has low multi-packet reception capabilities.

Optimal optical camera communication-ALOHA random access algorithm aided visible light communication system

We propose an optical camera communication-based ALOHA-like (OCC-ALOHA) random access algorithm in which the hidden terminal issue is avoided. We use the spatial resolution of the image sensor of the OCC link to enable the access point (AP) to obtain multipacket reception capability. Then, we use the spot estimation algorithm to estimate the number of active terminals, the distance between the AP and terminal, and the emission angle of the terminal light-emitting diode. Based on the above estimation information, the double iteration algorithm is used to calculate the access probability of each terminal to achieve the optimization of the access fairness and system throughput rate. The results show that the throughput rate of the proposed scheme is better than that of carrier sense multiple access with collision avoidance with hidden terminals.

Multi-packet reception dynamic frame-slotted ALOHA for IoT: Design and analysis

Machine-to-Machine(M2M) Data Collection networks form an integral part of the internet of things wherein a Data Collector (DC) collects data from a large number of nodes. M2M communication protocols should not only be efficient, but they should also be easy to implement. In this regard, Frame Slotted aloha based Medium Access Control protocols have gained popularity for these networks. In the current paper, we propose and analyze the Dynamic Frame Slotted Aloha(DFSA) algorithm for an M2M system with DC having a multi-packet reception (MPR) capability. We develop a stochastic model for the proposed MPR-DFSA algorithm. Based on the Karp-Upfal-Widgerson inequality, we derive asymptotic expressions and bounds for (a) expected number of nodes contending in a given iteration of the algorithm and (b) expectation of the number of iterations the MPR DFSA algorithm takes to finish. Further per-slot throughput and transmission delay of the packet of the MPR-DFSA algorithm have been derived. The analysis is validated through simulation. We compared the proposed MPR DFSA protocol with random access protocols of LTE and Narrowband IoT standards. Finally, we evaluate the performance of MPR DFSA protocol under real-world IoT network scenarios.

Performance Analysis of Interference-Aware Multi-Packet Reception Networks

Decentralized wireless networks are gaining increasing popularity as they do not need a fixed infrastructure. Simultaneously, multiple research initiatives have led to different findings at the PHY layer of the wireless communication systems, which include Multi-Packet Reception (MPR) techniques that enable a receiver to decode multiple packets that are transmitted simultaneously. However, the distributed nature of decentralized wireless networks demands different network control policies that should take into account the MPR capabilities to increase the network performance. This work studies the performance of a wireless network composed of multiple transmitters that are willing to transmit to a single receiver. This receiver has MPR capability and adopts an Energy-based Sensing (EBS) technique to enable uplink users’ transmissions without interfering with the ongoing transmissions from other transmitters. The first remark to be made is that the MPR technique performance depends on the channel propagation conditions and on the amount of time the receiver needs to detect the spectrum’s occupancy state. However, it is shown that by increasing the number of samples needed to increase the sensing accuracy, the receiver may degrade its throughput, namely if the receiver is equipped with a single radio, that is sequentially used for sensing and transmitting (split-phase operation). The results presented in the paper show the impact of the channel propagation condition and EBS parameterization on wireless network throughput and the cases where the receiver MPR capture performance is greatly improved by the use of a spectrum sensing technique.

Cooperative Delay-Constrained Cognitive Radio Networks: Delay-Throughput Trade-Off With Relaying Full-Duplex Capability

In this paper, the problem of maximizing the secondary user (SU) throughput under a primary user (PU) quality of service (QoS) delay requirement is studied. Moreover, the impact of having a full-duplex capability at the SU on the network performance is investigated compared to the case of a SU with half-duplex capability. We consider a cooperative cognitive radio (CR) network in which the receiving nodes have multi-packet reception (MPR) capabilities. In our proposed model, the SU not only benefits from the idle time slots (i.e. when PU is idle), but also chooses between sharing the channel or cooperating with the PU in a probabilistic manner. We formulate our optimization problem to maximize the SU throughput under a PU QoS, defined by a delay constraint; the optimization is performed over the transmission modes selection probabilities of the SU. The resultant optimization problem is found to be a non-convex quadratic constrained quadratic programming (QCQP) optimization problem, which is, generally, an NP-hard problem. We devise an efficient approach to solve it and to characterize the network stability region under a delay constraint set on the PU. Numerical results, surprisingly, reveal that the network performance when full-duplex capability exists at the SU is not always better compared to that of a half-duplex SU. In fact, we demonstrate that a full-duplex capability at the SU can, in some cases, adversely influence the network stability performance, especially if the direct channel conditions between the SU and the destinations are worse than that between the PU and the destinations. In addition, we formulate a multi-objective programming (MOP) optimization problem to investigate the trade-off between the PU delay and the SU throughput. Our MOP approach allows for assigning relative weights for our two conflicting performance metrics, i.e., PU delay and SU throughput. Numerical results also demonstrate that our cooperation policy outperforms conventional cooperative and non-cooperative policies presented in previous works.

Performance Analysis of Grant-Free Multiple Access for Supporting Sporadic Traffic in Massive IoT Networks

Grant-free multiple access (GFMA) protocol has been regarded as a key element to support sporadic traffic generated from massive internet-of-things (IoT) networks. In GFMA protocol, each IoT device transmits data packets without grant from a base station (BS) via pre-reserved uplink resources. Packet collisions inherently occur when multiple IoT devices transmit packets by using the same radio resource, but the collision effect can be alleviated with multi-packet reception (MPR) capability of the BS. Since a number of studies have focused on improving the physical layer performance such as bit error rate, they may be hard to provide intuitions from the MAC layer perspective when a number of IoT devices sporadically generate uplink packets and attempt the GFMA. In this paper, we thoroughly investigate the GFMA from the MAC layer perspective. We provide an analytical framework based on a Markov chain to capture the performance of the GFMA in terms of packet transmission success probability, ergodic throughput, and access delay. Through simulations, we validate our analytical framework and verify the necessity of adopting MPR technique for supporting a massive number of IoT devices generating sporadic traffic.

Performance analysis of a cooperative wireless network with adaptive relays

In this work, we investigate a slotted-time relay assisted cooperative random access wireless network with multipacket (MPR) reception capabilities. MPR refers to the capability of a wireless node to successfully receive packets from more than two other modes that transmit simultaneously at the same slot. We consider a network of N saturated sources that transmit packets to a common destination node with the cooperation of two infinite capacity relay nodes. The relays assist the sources by forwarding the packets that failed to reach the destination. Moreover, the relays have also packets of their own to transmit to the destination. We further assume that the relays employ a state-dependent retransmission control mechanism. In particular, a relay node accordingly adapts its transmission probability based on the status of the other relay. Such a protocol is towards self-aware networks and leads to substantial performance gains in terms of delay. We investigate the stability region and the throughput performance for the full MPR model. Moreover, for the asymmetric two-sources, two-relay case we derive the generating function of the stationary joint queue-length distribution with the aid of the theory of boundary value problems. For the symmetric case, we obtain explicit expressions for the average queueing delay in a relay node without solving a boundary value problem. Extensive numerical examples are presented and provide insights on the system performance.

On the Throughput Region of Wireless Random Access Protocols with Multi-Packet Reception Using Multi-Objective Optimization

This paper presents a new approach for the analysis and characterization of the throughput region of wireless random access protocols enabled with multi-packet reception (MPR) capabilities. The derivation of a closed-form expression for the envelope of the throughput region under the assumption of an arbitrary number of terminals is an open issue in the literature. To partially fill this gap, a new method based on multi-objective optimization tools is herein presented. This innovative perspective allows us to identify the envelope of the throughput region as the Pareto frontier solution that results from maximizing simultaneously all individual terminal throughput functions. To simplify this problem, a modified MPR model is proposed that mimics the conditions of collision model protocols, but it also inserts new physical (PHY) layer features that allow concurrent transmission or MPR. The N-reception model is herein introduced, where collisions of up to N signals are assumed to be always correctly resolved from a population of J terminals, where N can be related to the number of antennas or degrees of freedom of the PHY-layer used at the receiver to resolve a collision. It is shown that by using this model and under the assumption of N = J - 1 , the Pareto frontier expression can be obtained as a simple extension of the ALOHA solution. Unfortunately, for cases with N < J - 1 , the structure of the resulting determinant matrix does not allow for a simple explicit solution. To overcome this issue, a symmetrical system is proposed, and the solution is obtained by the analysis of the roots of the resulting polynomial expression. Based on this result, an equivalent sub-optimal solution for the asymmetrical case is herein identified for systems where N < J - 1 . An extension to more general reception models based on conditional reception probabilities is also presented using the proposed equivalence between the symmetric and asymmetric solutions. The results intend to shed light on the performance of MPR systems in general, and in particular to advance towards the solution of the conjecture of the equivalence between throughput and stability regions in random access.

A randomized adaptive neighbor discovery for wireless networks with multi-packet reception capability

Neighbor discovery is a first step in the initialization of wireless networks in large-scale ad hoc networks. In this paper, we propose a randomized neighbor discovery scheme for wireless networks with a multi-packet reception (MPR) capability. We let the nodes to have different advertisement probabilities. The node gradually adjusts its probability according to its operation phases: greedy or slow-start. In the greedy phase, the node advertises aggressively while it does moderately in the slow-start phase. Initial phase and advertisement probability are determined randomly. Then, the nodes change the probability adaptively according to advertisements the reception state from the other nodes. In order to decide the reception state precisely, the exact number of nodes in the network is necessary. To make our proposed scheme work in case of no prior knowledge of the population, we propose a population estimation method based on a maximum likelihood estimation. We evaluate our proposed scheme through numerical analysis and simulation. Through the numerical analysis, we show that the discovery completion time is lower bounded in Θ(Nk) and upper bounded in Θ(NlnNk) when there exists N nodes with MPR-k capability. The bounds are the same as those of previous studies that propose static optimal advertisement probability. Through the simulation, we evaluate that our adaptive scheme outperforms in terms of discovery completion time, advertisement efficiency, and wasted time slot ratio than a scheme with static advertisement probability when the population of the network is unknown.

Optimal throughput performance in full-duplex relay assisted cognitive networks

In this paper, we study a full-duplex cooperative cognitive radio network with multiple full-duplex secondary users acting as potential relays for transmitting the packets of a primary user. In addition to having full-duplex capability, the receivers also have multi-packet reception capability allowing them to simultaneously decode packets incoming from different senders. Our objective is to maximize the sum throughput of the secondary users while stabilizing the primary and relay queues. Towards this objective, we characterize the optimal scheduling of primary and relay packets at the full-duplex secondary users. The resulting problem is non-convex, and thus, we transform it into a linear fractional problem by using the dominant system approach. This, in turn, facilitates an efficient numerical solution by the bisection method. We analyze the effects of different network parameters on the optimal solution numerically for a number of possible scenarios. Our numerical results demonstrate how the multi-packet reception and full-duplex capabilities, as well as the partial relaying and number of secondary users affect the primary and secondary users’ stable throughput revealing new insights into the performance of overlay cognitive networks. In particular, we demonstrate that full-duplex capability of secondary users together with multi-packet reception capability of the primary destination is the key in reaping the benefits of full-duplex cooperative cognitive communications.

Asymptotic Performance of Coded Slotted ALOHA With Multipacket Reception

In this letter, we develop a converse bound on the asymptotic load threshold of coded slotted ALOHA (CSA) schemes with $K$ -multipacket reception capabilities at the receiver. Density evolution is used to track the average probability of packet segment loss and an area matching condition is applied to obtain the converse. For any given CSA rate, the converse normalized to $K$ increases with $K$ , which is in contrast with the results obtained so far for slotted ALOHA schemes based on successive interference cancellation. We show how the derived bound can be approached using spatially coupled CSA.

Stable Throughput and Delay Analysis of a Random Access Network With Queue-Aware Transmission

In this paper, we consider a two-user and a three-user slotted ALOHA network with multi-packet reception (MPR) capabilities and a queue-aware transmission control. In this setting, the nodes can adapt their transmission probabilities and their transmission parameters based on the status of the other nodes. Each user has external bursty arrivals that are stored in their infinite capacity queues. We focus on the fundamental problem of characterizing the stable throughput region, as well as of investigating the queueing delay. For the two- and the three-user cases, we obtain the exact stability region, whereas in the former case, we also provide the conditions under which the stability region is a convex set. We perform a detailed mathematical analysis to study the queueing delay in the two-user case by formulating two boundary value problems, the solution of which provides the generating function of the joint stationary probability distribution of the queue size at user nodes. Furthermore, for the two-user symmetric case with MPR, we obtain a lower and an upper bound for the average delay without the need of solving a boundary value problem. In addition, we provide a closed form expression for the gap between the lower and the upper bound. The bounds as it is seen in the numerical results appear to be tight. Explicit expressions for the average delay are obtained for the symmetrical model with capture effect. We also provide a closed form expression for the optimal transmission probability that minimizes the average delay in the symmetric capture case. Finally, we evaluate numerically the presented theoretical results.

Channel-Aware Random Access in the Presence of Channel Estimation Errors

In this work, we consider the random access of nodes adapting their transmission probability based on the local channel state information (CSI) in a decentralized manner, which is called CARA. The CSI is not directly available to each node but estimated with some errors in our scenario. Thus, the impact of imperfect CSI on the performance of CARA is our main concern. Specifically, an exact stability analysis is carried out when a pair of bursty sources are competing for a common receiver and, thereby, have interdependent services. The analysis also takes into account the compound effects of the multipacket reception (MPR) capability at the receiver. The contributions in this paper are twofold: first, we obtain the exact stability region of CARA in the presence of channel estimation errors; such an assessment is necessary as the errors in channel estimation are inevitable in the practical situation. Secondly, we compare the performance of CARA to that achieved by the class of stationary scheduling policies that make decisions in a centralized manner based on the CSI feedback. It is shown that the stability region of CARA is not necessarily a subset of that of centralized schedulers as the MPR capability improves.

Stability Analysis of Frame Slotted Aloha Protocol

Frame Slotted Aloha (FSA) protocol has been widely applied in Radio Frequency Identification (RFID) systems as the de facto standard in tag identification. However, very limited work has been done on the stability of FSA despite its fundamental importance both on the theoretical characterization of FSA performance and its effective operation in practical systems. In order to bridge this gap, we devote this paper to investigating the stability properties of $p$ -persistent FSA by focusing on two physical layer models of practical importance, the models with single packet reception and multipacket reception capabilities. Technically, we model the FSA system backlog as a Markov chain with its states being backlog size at the beginning of each frame. The objective is to analyze the ergodicity of the Markov chain and demonstrate its properties in different regions, particularly the instability region. By employing drift analysis, we obtain the closed-form conditions for the stability of FSA and show that the stability region is maximized when the frame length equals the number of packets to be sent in the single packet reception model and the upper bound of stability region is maximized when the ratio of the number of packets to be sent to frame length equals in an order of magnitude the maximum multipacket reception capacity in the multipacket reception model. Furthermore, to characterize system behavior in the instability region, we mathematically demonstrate the existence of transience of the backlog Markov chain. Finally, the analytical results are validated by the numerical experiments.

Design and performance analysis of a MAC protocol for wireless LANs supporting multipacket reception

Recent advances in physical layer technologies, allows simultaneous reception of multiple packets by a node. This promising technology is called Multi-Packet Reception (MPR). In this paper, we analyze a modified IEEE 802.11 DCF based medium access control (MAC) protocol for Wireless Local Area Network (WLAN) with nodes having MPR capability. Our protocol modifies the backoff mechanism of the IEEE 802.11 DCF so that the backoff process quickly adapts to the prevailing traffic conditions while leveraging MPR. A Markov chain model for the backoff process of the nodes with MPR capability has been derived. Further a Markov chain model to capture the state of the MPR channel has been derived. Solving the Markov chains for stationary probabilities, we derive the saturated throughput and energy efficiency of our protocol. Effect of the number of nodes, MPR capability, contention window size, and sensing errors on the saturation throughput has been investigated. Proposed analytical model has been validated through extensive simulations. Our investigation has the potential to aid in the development of next generation WLAN MAC protocol.