Optimal Transmission Probability Research Articles

Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

The best thermoelectrics revisited in the quantum limit

The classical problem of best thermoelectrics, which was believed originally solved by Mahan and Sofo [Proc. Natl. Acad. Sci. USA 93, 7436 (1996)], is revisited and discussed in the quantum limit. We express the thermoelectric figure of merit (zT) as a functional of electronic transmission probability {{{mathcal{T}}}} by the Landauer–Büttiker formalism, which is able to deal with thermoelectric transport ranging from ballistic to diffusive regimes. We also propose to apply the calculus of variations to search for the optimal {{{mathcal{T}}}} giving the maximal zT. Our study reveals that the optimal transmission probability {{{mathcal{T}}}} is a boxcar function instead of a delta function proposed by Mahan and Sofo, leading to zT exceeding the well-known Mahan–Sofo limit. Furthermore, we suggest realizing the optimal {{{mathcal{T}}}} in topological material systems. Our work defines the theoretical upper limit for quantum thermoelectrics, which is of fundamental significance to the future development of thermoelectrics.

Energy-Efficient Covert Wireless Communication Through Probabilistic Jamming

In this work, we propose a novel energy-efficient covert wireless communication scheme through probabilistic jamming, where a jammer probabilistically transmits artificial noise (AN) with variable power against a warden’s detection. For the proposed scheme, the closed-form expressions for the warden’s detection error probability, the transmission outage probability, and the system energy efficiency are derived to reveal the impacts of the prior AN transmission probability on communication covertness, reliability, and system energy efficiency. To further improve the system energy efficiency, an optimization problem is formulated to determine the optimal prior AN transmission probability and the optimal transmit power of private information subject to a covertness constraint, which shows that continuous jamming is generally not optimal to maximize the energy efficiency in covert communication systems. Simulation results verify our analysis and show the superiority of proposed scheme comparing to a widely adopted scheme where the jammer always transmits AN with random transmit power.

Semi-Probabilistic Repetition Schemes for Sporadic URLLC Traffic in Multiuser Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) assisted grant-free access is a compelling approach to support uplink ultra-reliable low-latency communications (URLLCs). It is a natural idea to combine massive MIMO and repetition transmission to further improve the reliability. However, excessive repetitions may increase the collision probability in multiuser scenarios and thus limit the reliability improvement. In this paper, we propose a novel semi-probabilistic repetition (SPRe) scheme for the massive multiuser MIMO (MU-MIMO) systems with grant-free access. By introducing a transmission probability for the replicas, the SPRe is more flexible than the popular repetition scheme, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition, and covers the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition as a special case. Considering practical MU-MIMO systems with finite block-length coding, channel estimation errors, pilot collisions and the sporadic arrivals of URLLC traffic, the reliability of the proposed SPRe is thoroughly analyzed and derived in closed form. Through further asymptotic analysis, the optimal transmission probability to maximize the reliability of MU-MIMO is derived for the high-SNR regime and low-load scenarios. Moreover, the diversity gain of the proposed SPRe is also analyzed and it is proved that the optimal SPRe is superior to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition in terms of reliability. The analysis and superiority of the SPRe are finally verified through computer simulations.

Fairness-Constrained Rate Optimization of Multi-Link Slotted Aloha

This letter optimizes the data rate performance of multi-link slotted Aloha networks, in which Multi-Link Devices (MLDs) coexist with multiple groups of Single-Link Devices (SLDs), where different groups reside in distinct channels. MLDs can transmit/receive packets via multiple channels, causing interference to all groups of SLDs and severe rate unfairness issue. To ensure SLDs in different channels can acquire target ratios of data rate, explicit expressions of sum rate and group data rate are derived, based on which the optimal transmission probabilities of MLDs/SLDs for maximizing the sum rate without or with fairness constraints are obtained. The analysis reveals the coupling effect of the behavior of devices in different channels, the tradeoff between the optimal sum rate performance and the fairness, and the effect of channel fading on benefit of using multi-link transmission capability.

Optimal Transmission Probabilities of Information and Artificial Noise in Covert Communications

This letter studies a wireless covert communication system, where a jammer generating intermittent artificial noise (AN) is employed to confuse the warden. The transmission probabilities of information and AN are jointly optimized to maximize the communication covertness under a throughput requirement. We first analyse the detection performance of the warden and the throughput of the system, based on which we formulate the optimization problem. Then, we simplify this two-dimensional nonconvex problem to a one-dimensional problem and obtain the closed-form optimal solutions in special cases. The results illustrate that the optimal transmission probabilities of information and AN are 0.5 and 1, respectively, when the throughput requirement is less than a threshold. However, 0.5 and 1 are not optimal when the throughput requirement exceeds the aforementioned threshold, which has not been reported yet.

Optimal Probabilistic Repetition for Massive MIMO-Aided Grant-Free Short-Packet Transmissions

In this letter, we propose a novel probabilistic repetition scheme for the uplink massive MIMO system with grant-free access. Compared with canonical K-repetition, the proposed scheme is more flexible and covers the K-repetition as a special case. With the consideration of multiple practical factors, including the sporadic arrivals, the short-packet transmissions, probabilistic repetition, and channel estimation error, the reliability and throughput of the proposed probabilistic repetition assisted grant-free access scheme are theoretically analyzed. It is also proved that the optimal transmission probability for reliability maximization exists and can be found through gradient descent method. Simulation results validate the accuracy of our theoretical analysis, and also show that the proposed Optimal Probabilistic Repetition assisted grant-free Access scheme (OPRA) is superior to the popular modern grant-free random access with repetition transmission, that is, irregular repetition slotted ALOHA (IRSA).

When Aloha and CSMA Coexist: Modeling, Fairness, and Throughput Optimization

With the emerging unlicensed spectrum sharing and Machine-to-Machine communications, the coexistence performance of multiple devices with different access schemes operating at the same unlicensed bands has received significant research interests. As the two most representative random-access schemes, Aloha and Carrier Sense Multiple Access (CSMA) both found wide applications in unlicensed bands. Yet most of the studies have focused on the coexistence of CSMA-based networks, leaving the coexistence of Aloha-based and CSMA-based networks largely unexplored. The challenge originates from the lack of a coexistence model of slotted Aloha and CSMA. In this paper, the throughput performance of coexisting Aloha and CSMA networks is characterized and optimized by extending a unified analytical framework proposed for random-access networks. The analysis shows that different from the single-network case where the maximum throughput of CSMA is much higher thanks to carrier sensing, when Aloha and CSMA coexist, Aloha would significantly outperform CSMA if each network optimizes its own throughput performance without cooperation, leading to poor throughput performance for CSMA and severe unfairness. To achieve fair coexistence, inter-network cooperation is crucial. The optimal transmission probabilities of Aloha and CSMA for maximizing the total network throughput under a given throughput ratio are further derived, and applied to coexisting LTE Unlicensed and WiFi networks to optimize their coexistence performance.

Design and performance evaluation of successive interference cancellation based Slotted Aloha MAC protocol

As a result of densification, the performance of the wireless networks has become highly interference-limited and energy inefficient. To overcome this problem, interference mitigation techniques such as Successive Interference Cancellation (SIC) can be used to decode multiple packets simultaneously at the receiver. In this context, we analyze a SIC-based Slotted Aloha (SIC-SA) Medium Access Control (MAC) protocol for wireless networks. We derive expressions for packets decoding probability and optimal transmission probability of the nodes of the SIC-SA MAC protocol. Our derivation is based on the order statistics of Independent and Identical/non-Identical exponentially distributed received-signal-powers from the nodes under the Rayleigh channel condition. Throughput, delay, and energy efficiency of the SIC-SA MAC protocol have been derived and validated against simulation. The effect of path loss exponent, SINR threshold, and the number of nodes on the performance of SIC-SA have been studied. The performance of SIC-SA in a network of nodes distributed randomly according to the Poisson Point Process has been analyzed. Extension of our analysis to Power Domain Non-Orthogonal Multiple Access (NOMA) has been demonstrated. We also analyzed the impact of imperfect estimation of channel state information and imperfect SIC at the receiver. Results show an improvement in performance metrics of SIC-SA over the traditional Slotted Aloha.

A Robust Algorithm for LTE D2D (Sidelink) Discovery for Public Safety Communications

As public safety migrates from existing digital land mobile radio networks to fourth generation (4G) long-term evolution (LTE), a novel feature in the latter is the enablement of direct communications between devices. In this article, we examine the performance of the LTE device-to-device direct discovery process in out-of-coverage scenarios (Mode 2). We model individual discovery periods as a slotted random access protocol with half-duplex user equipment (UE). We outline a simulation framework using the open-source network simulator 3 (ns-3) and evaluate the discovery process’s performance as a function of the size of the resource pool, UE density, and the transmission probability. We establish that there exists an optimal transmission probability that minimizes discovery time for a given network configuration. However, because a UE cannot know the number of neighbors when it goes off-network, we develop a method to allow UEs to learn the optimal transmission probability during discovery. We compare our algorithm’s performance to the 3rd Generation Partnership Project standards prescribed method and others to demonstrate its superiority.

Controlling Virus Spread Using an Intermittent Quarantine Strategy on Multiplex Networks

Virus spreading on the Internet will negatively affect cybersecurity. An intermittent quarantine immunization strategy to control virus spreading when containing information diffusion is proposed herein. In this model, information and virus spread on different subnetworks and interact with each other. We further develop a heterogeneous mean-field approach with time delays to investigate this model and use Monte Carlo simulations to systematically investigate the spreading dynamics. For a relatively short intermittent period, the optimal information transmission probability of the virus will be significantly suppressed. However, when the intermittent period is extremely long; increasing the probability of information transmission can control the virus spreading as well as suppress the increase in the intermittent period. Finally, it is shown that the average degree of the two subnetworks does not qualitatively affect the spreading dynamics.

Performance Study of Cybertwin-Assisted Random Access NOMA

In this article, a cybertwin-assisted nonorthogonal random access (RA) system is presented, where the cybertwins of the physical devices at the access point (AP) collect the devices’ information and decide the transmission parameters on behalf of the devices to achieve the maximum system performance. Specifically, the system performance of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -persistent slotted CSMA system with nonorthogonal multiple access (NOMA) is analyzed, in which wireless devices transmit data to the ensure the received signal strength at the AP side is either high power or low power with certain probabilities. We first develop an analytical framework to quantify the successful transmission probability and the sum data rate as a function of the above probabilities. Accordingly, the feasible region of the number of high-power and low-power devices to ensure successful transmission is derived. With the analysis, nonconvex optimization problems are then formulated to maximize successful transmission probability and the sum data rate, respectively. To tackle the nonconvexity, an effective and fast-convergent iterative algorithm is designed to obtain the optimal transmission probabilities for the devices. Extensive simulations are conducted to validate our analytical results and demonstrate the benefits of NOMA in RA networks.

Achieving proportional fairness in WiFi networks via bandit convex optimization

In this paper, we revisit proportional fair channel allocation in IEEE 802.11 networks. Traditional approaches are either based on the explicit solution of the optimization problem or use iterative solvers to converge to the optimum. Instead, we propose an algorithm able to learn the optimal slot transmission probability only by monitoring the throughput of the network. We have evaluated this algorithm both (i) using the true value of the function to optimize and (ii) considering estimation errors. We provide a comprehensive performance evaluation that includes assessing the sensitivity of the algorithm to different learning and network parameters as well as its reaction to network dynamics. We also evaluate the effect of noisy estimates on the convergence rate and propose a method to alleviate them. We believe our approach is a practical solution to improve the performance of wireless networks as it does not require knowing the network parameters in advance. Yet, we conclude that the setting of the parameters of the algorithm is crucial to guarantee fast convergence.

Age of Information for Short-Packet Covert Communication

In this letter, we develop a new framework to jointly characterize covertness and timeliness of short-packet communications, in which a new metric named covert age of information (CAoI) is first proposed and then a closed-form expression for the average CAoI is derived. Our examination explicitly reveals the tradeoff between communication covertness and timeliness affected by the block-length, transmit power and prior transmission probability. Multiple transmission designs are tackled in order to minimize the average CAoI subject to covertness constraint, where the resultant differences relative to the designs with traditional metrics (e.g., effective covert rate, AoI) as objective functions are clarified. Our examination demonstrates that the optimal block-length is not the largest one, which is optimal in delay-constrained covert communication without considering the communication timeliness, and the optimal prior transmission probability may not be 1/2, which has been widely assumed in the literature of covert communications.

Random Access in IoT Using Naïve Bayes Classification

This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Collision-aware distributed detection with population-splitting algorithms

We consider the design of distributed detection algorithms for single-hop, single-channel wireless sensor networks in which sensor nodes send their local decisions to a fusion center (FC) by using a random access protocol. There is also limited time to collect local decisions before a final decision must be made. We thus propose and analyze a modified random access protocol in which the FC combines slotted ALOHA with a population-splitting algorithm called population-splitting-based random access (PSRA) and collision-aware distributed detection according to an estimate-then-fuse approach. Under the PSRA, only sensor nodes whose observations fall in a particular range of reliability will send their decisions in a specific frame by using slotted ALOHA. At the end of the collection time, the FC applies the collision-aware distributed detection to make a final decision. Here, the FC will first observe the state of each time slot—idle, successful, collision—in each frame, use this information to estimate the number of sensor nodes participating in each frame, and, then, compute a final decision using a population-based fusion rule. An approximation of the optimal transmission probability of the slotted ALOHA is determined to minimize the probability of error. Numerical results show that, unlike slotted-ALOHA-based data networks, the transmission probability maximizing the number of successful time slots does not optimize the performance of the proposed distributed detection. Instead, the proposed distributed detection performs best with a transmission probability that induces many collisions.

Analysis of Slotted ALOHA With an Age Threshold

We present a comprehensive steady-state analysis of threshold-ALOHA , a distributed age-aware modification of slotted ALOHA proposed in recent literature. In threshold-ALOHA, each terminal suspends its transmissions until the Age of Information (AoI) of the status update flow it is sending reaches a certain threshold $\Gamma $ . Once the age exceeds $\Gamma $ , the terminal attempts transmission with constant probability $\tau $ in each slot, as in standard slotted ALOHA. We analyze the time-average expected AoI attained by this policy, and explore its scaling with network size, $n$ . We derive the probability distribution of the number of active users at steady state, and show that as network size increases the policy converges to one that runs slotted ALOHA with fewer sources: on average about one fifth of the users is active at any time. We obtain an expression for steady-state expected AoI and use this to optimize the parameters $\Gamma $ and $\tau $ , resolving the conjectures in previous literature by confirming that the optimal age threshold and transmission probability are $2.2n$ and $4.69/n$ , respectively. We find that the optimal AoI scales with the network size as $1.4169n$ , which is almost half the minimum AoI achievable with slotted ALOHA, while the loss from the maximum throughput of $e^{-1}$ remains below 1%. We compare the performance of this rudimentary algorithm to that of the SAT policy [2] that dynamically adapts its transmission probabilities.

Cooperative Antenna Selection Method for Directional Antenna Ad Hoc Networks Based on ALOHA

In recent years, directional antennas or phased array antennas are being widely used in communication systems due to the higher antenna gains. However, without external time synchronization and angle synchronization, the unsynchronized node usually takes a long time to synchronize with the existing nodes due to the narrow beams. Although the multibeam transmission or the digital phased array antenna can reduce this problem, it is clear that the cost of the digital phased array antenna is currently too high. Without external time synchronization and angle synchronization, a cooperative antenna selection method based on directional antennas is proposed in this paper. Our method only uses the narrow beams to transmit and to receive and reduces the time for self‐synchronization. In this paper, we give the expression of the expected average time for the self‐synchronization of multiple nodes, transform the problem into the problem of finding the minimum value of the infinite norm of the sequence, and then propose a cooperative antenna selection method which calculates the optimal transmission probability distribution of the node in different directions through parameter sharing and relative geometric position relationship between nodes. Finally, we verify the proposed method through simulation, and the number of beams is set between 6 and 10. In a typical scenario of five nodes, our method reduces the maximum average self‐synchronization time by 50% averagely, compared with the traditional method which sends the different antenna beams at equal probability.

Performance Optimization of Cluster-Based MAC Protocol for VANETs

Recently, a novel cluster-based medium access control (CB-MAC) protocol has been proposed for vehicular ad hoc networks (VANETs). Though performance of CB-MAC protocol is affected by cluster size, no mechanism is provided to manage cluster size efficiently. In this paper, the performance of CB-MAC protocol is optimized for VANETs by optimizing transmission probability with cluster size. Each vehicle should adopt the optimum transmission probability in the cluster which can be obtained by tuning the number of clusters in VANET. Therefore, optimum number of clusters is defined based on the number of vehicles in VANETs. An analytical study based on Markov chain model is provided. Optimum transmission probability, and optimum number of clusters expressions are derived. The microscopic mobility model is generated in SUMO for practical scenario. Simulation results are presented which verify analytical (theoretical) analysis and show that the performance of CB-MAC protocol is maximized in terms of throughput, packet dropping rate (PDR), and delay. Throughput is remarkably increased, whereas PDR and delay are decreased.

Adaptive Transmission Control for Uplink/Downlink Fairness in Unsaturated CSMA Networks

In carrier sensing multiple access (CSMA)-based IEEE 802.11 systems, it is important for an access point (AP) and multiple stations to have access opportunity proportional to their traffic load, ensuring fairness between uplink (UL) and downlink (DL). This letter proposes an adaptive backoff algorithm based on estimation for the UL/DL backlog sizes in unsaturated CSMA networks. Based on the UL/DL backlog estimation, the optimal UL and DL transmission probabilities are derived as a function of the UL/DL backlog sizes. The proposed algorithm not only improves the overall system performance but also provides the near-perfect UL/DL fairness.