Reactive jammers pose a severe security threat to robotic-swarm networks by selectively disrupting inter-agent communications and undermining formation integrity and mission success. Conventional countermeasures such as fixed power control or static channel hopping are largely ineffective against such adaptive adversaries. This paper presents a multi-agent reinforcement learning (MARL) framework based on the QMIX algorithm to improve the resilience of swarm communications under reactive jamming. We consider a network of multiple transmitter–receiver pairs sharing channels while a reactive jammer with Markovian threshold dynamics senses aggregate power and reacts accordingly. Each agent jointly selects transmit frequency (channel) and power, and QMIX learns a centralized but factorizable action-value function that enables coordinated yet decentralized execution. We benchmark QMIX against a genie-aided optimal policy in a no–channel-reuse setting, and against local Upper Confidence Bound (UCB) and a stateless reactive policy in a more general fading regime with channel reuse enabled. Simulation results show that QMIX rapidly converges to cooperative policies that nearly match the genie-aided bound, while achieving higher throughput and lower jamming incidence than the baselines, thereby demonstrating MARL’s effectiveness for securing autonomous swarms in contested environments.

The present work aims to address key security vulnerabilities in Vehicular Ad Hoc Networks (VANETs) through an enhanced authentication scheme. This study evaluates the effectiveness of utilizing Elliptic Curve Cryptography (ECC) for improving security efficiency and compares it with conventional techniques used in state-of-the-art authentication protocols. The results demonstrate improvements in both computational and communication efficiency. A rigorous formal analysis using the Tamarin Prover confirms the robustness of the proposed security processes. Real-world implementation of the proposed protocol using Raspberry Pi devices is conducted and empirical performance tests using NS-3 (Network Simulator 3) show a 30% improvement in computational efficiency and a 25% reduction in authentication delay compared to conventional techniques, along with a 98% success rate in evading impersonation attacks and a 95% success rate in preventing replay attacks while maintaining similar efficiency levels.

Multiplexing Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) traffic on the same 5G New Radio (NR) air interface poses significant challenges due to extreme latency requirement of URLLC packets. This paper investigates the direct puncturing of URLLC traffic over eMBB transmissions, a method that, while guaranteeing immediate URLLC packet delivery, can severely degrade eMBB performance. To alleviate the adverse impact on eMBB, we present Cyrus—a deep reinforcement learning (DRL)-based puncturing solution for eMBB and URLLC multiplexing. Cyrus is tailored for the Open RAN (O-RAN) architecture and unifies the three control loops of O-RAN synergistically in its design of DRL-based solution. Not only does Cyrus meet the real-time requirements for URLLC but also it continuously updates and improves its scheduling policy based on changing network conditions. The effectiveness of Cyrus is demonstrated through link-level simulations for 5G NR, showing significant improvement in eMBB performance over the state-of-the-art, particularly as URLLC traffic increases.

We introduce a framework and optimal ‘fresh’ caching for a content distribution network (CDN) comprising a front-end local cache and a back-end database. The data content is dynamically updated at a back-end database and end-users are interested in the most-recent version of that content. We formulate the average cost minimization problem that captures the system’s cost due to the service of aging content as well as the regular cache update cost. We consider the cost minimization problem from two individual perspectives based on the available information to either side of the CDN: the back-end database perspective and the front-end local cache perspective. For the back-end database, the instantaneous version of content is observable but the exact demand is not. Caching decisions made by the back-end database are termed ‘push-based caching.’ For the front-end local cache, the age of content version in the cache is not observable, yet the instantaneous demand is. Caching decisions made by the front-end local cache are termed ‘pull-based caching.’ Our investigations reveal which type of information, updates, or demand dynamic, is of higher value towards achieving the minimum cost based on other network parameters including content popularity, update rate, and demand intensity.

In this work, we consider the problem of ‘fresh’ caching at distributed (front-end) local caches of content that is subject to ‘dynamic’ updates at the (back-end) database. We first provide new models and analyses of the average operational cost of a network of distributed edge-caches that utilizes wireless multicast to refresh aging content. We attack the problems of what to cache in each edge-cache and how to split the incoming demand amongst them (also called “load-splitting” in the rest of the paper) in order to minimize the operational cost. While the general form of the problem comes with an NP-hard Knapsack structure, we were able to completely solve the problem by judiciously choosing the number of edge-caches to be deployed over the network This reduces the complex problem to a solvable special case. Interestingly, our findings reveal that the optimal caching policy necessitates unequal load-splitting over the edge-caches even when all conditions are symmetric. Moreover, we find that edge-caches with higher load will generally cache fewer but relatively more popular content. We further investigate the tradeoffs between cost reduction and cache savings when employing equal and optimal load-splitting solutions for demand with Zipf($z$) popularity distribution. Our analysis reveals that equal load-splitting to edge-caches achieves close-to-optimal for less predictable demand ($z< 2$) while also saving in the cache size. On the other hand, for more predictable demand ($z>2$), optimal load-splitting results in substantial cost gains while decreasing the cache occupancy.

Existing content caching mechanisms are predominantly geared towards easy-access to content that is static once created. However, numerous applications, such as news and dynamic sources with time-varying states, generate ‘dynamic’ content where new updates replace previous versions. This motivates us in this work to study the freshness-driven caching algorithm for dynamic content, which accounts for the changing nature of data content. In particular, we provide new models and analyses of the average operational cost both for the single and distributed edge caching scenarios. In both scenarios, we characterize the performance of the optimal solution and develop algorithms to select the content and the update rate that the user(s) must employ to have low-cost access to fresh content. Moreover, our work reveals new and easy-to-calculate key metrics for quantifying the caching value of dynamic content in terms of their refresh rates, popularity, number of users in the distribute edge caching group, and the fetching and update costs associated with the optimal decisions. We compare the proposed freshness-driven caching strategies with benchmark caching strategies like cache the most popular content. Results demonstrate that freshness-driven caching strategies considerably enhance the utilization of the edge caches with possibly orders-of-magnitude cost reduction. Furthermore, our investigations reveal that the distributed edge caching scenario, benefiting from the multicasting property of wireless service to update the cached content, can be cost-effective compared to the single edge caching, as the number of edge caches increases.

We introduce a framework and provably-efficient schemes for ‘fresh’ caching at the (front-end) local cache of content that is subject to ‘dynamic’ updates at the (back-end) database. We start by formulating the hard-cache-constrained problem for this setting, which quickly becomes intractable due to the limited cache. To bypass this challenge, we first propose a flexible time-based-eviction model to derive the average system cost function that measures the system’s cost due to the service of aging content in addition to the regular cache miss cost. Next, we solve the cache-unconstrained case, which reveals how the refresh dynamics and popularity of content affect optimal caching. Then, we extend our approach to a soft-cache-constrained version, where we can guarantee that the cache use is limited with arbitrarily high probability. The corresponding solution reveals the interesting insight that ‘whether to cache an item or not in the local cache?’ depends primarily on its popularity level and channel reliability, whereas ‘how long the cached item should be held in the cache before eviction?’ depends primarily on its refresh rate. Moreover, we investigate the cost-cache saving trade-offs and prove that substantial cache gains can be obtained while also asymptotically achieving the minimum cost as the database size grows.

We introduce a framework and provably-efficient schemes for `fresh' caching at the (front-end) local cache of content that is subject to `dynamic' updates at the (back-end) database. We start by formulating the hard-cache-constrained problem for this setting, which quickly becomes intractable due to the limited cache. To bypass this challenge, we first propose a flexible time-based-eviction model to derive the average system cost function that measures the system's cost due to the service of aging content in addition to the regular cache miss cost. Next, we solve the cache-unconstrained case, which reveals how the refresh dynamics and popularity of content affect the optimal caching. Then, we extend our approach to a soft-cache-constrained version, where we can guarantee that the cache use is limited with arbitrarily high probability. The corresponding solution reveals the interesting insight that `whether to cache an item or not in the local cache?' depends primarily on its popularity level, whereas `how long the cached item should be held in the cache before eviction?' depends primarily on its refresh rate. Moreover, we investigate the cost-cache saving tradeoffs and prove that substantial cache gains can be obtained while also asymptotically achieving the minimum cost as the database size grows.

In this work, we consider the problem of ‘fresh’ caching at distributed (front-end) local caches of content that is subject to ‘dynamic’ updates at the (back-end) database. We first provide new models and analyses of the average operational cost of a network of distributed edge-caches that utilizes wireless multicast to refresh aging content. We attack the problems of what to cache in each edge-cache and how to split the incoming demand amongst them (also called "loadsplitting" in the rest of the paper) in order to minimize the operational cost. While the general form of the problem comes with an NP-hard Knapsack structure, we were able to completely solve the problem by judiciously choosing the number of edge-caches to be deployed over the network. Interestingly, our findings reveal that the optimal caching policy necessitates unequal load-splitting over the edge-caches even when all conditions are symmetric. Moreover, we find that edge- caches with higher load will generally cache fewer but relatively more popular content. We further investigate the tradeoffs between cost reduction and cache savings when employing equal and optimal load-splitting solutions for demand with Zipf(z) popularity distribution. Our analysis reveals that equal load-splitting to edge-caches achieves close-to-optimal for less predictable demand (z2), optimal load-splitting results in substantial cost gains while decreasing the cache occupancy.

In this work, we provide a comprehensive analysis of stability properties and delay gains that wireless multicasting capabilities, as opposed to more traditional unicast transmissions, can provide for content distribution in mobile networks. In particular, we propose a model and characterize the average queue-length (and hence average delay) performance of unicasting and various multicasting strategies for serving a dynamic user population at the wireless edge. First, we show that optimized static randomized multicasting (we call it `blind multicasting') leads to stable-everywhere operation irrespective of the network loading factor (given by the ratio of the demand rate to the service rate) and the content popularity distribution. In contrast, traditional unicasting suffers from unstable operation when the loading factor approaches one, although it outperforms blind multicasting at small loading factor levels. This motivates us to study `work-conserving multicast' policies next that always outperform unicasting while still offering stable-everywhere operation. Then, in the worst-case of uniformly-distributed content popularity, we explicitly characterize the scaling of the average queue-length (and hence delay) under a first-come-first-serve multicast strategy as a function of the database size and the loading factor. Consequently, this work provides the fundamental limits, as well as the guidelines, for the design and performance analysis of efficient multicasting strategies for wireless content distribution.

In this work, we provide a comprehensive analysis of stability properties and delay gains that wireless multicasting capabilities, as opposed to more traditional unicast transmissions, can provide for content distribution in mobile networks. In particular, we propose a model and characterize the average queue-length (and hence average delay) performance of unicasting and various multicasting strategies for serving a dynamic user population at the wireless edge. First, we show that optimized static randomized multicasting (we call it `blind multicasting') leads to stable-everywhere operation irrespective of the network loading factor (given by the ratio of the demand rate to the service rate) and the content popularity distribution. In contrast, traditional unicasting suffers from unstable operation when the loading factor approaches one, although it outperforms blind multicasting at small loading factor levels. This motivates us to study `work-conserving multicast' policies next that always outperform unicasting while still offering stable-everywhere operation. Then, in the worst-case of uniformly-distributed content popularity, we explicitly characterize the scaling of the average queue-length (and hence delay) under a first-come-first-serve multicast strategy as a function of the database size and the loading factor. Consequently, this work provides the fundamental limits, as well as the guidelines, for the design and performance analysis of efficient multicasting strategies for wireless content distribution.

Optical signal propagation through underwater channels is affected by three main degrading phenomena, namely, absorption, scattering, and fading. In this paper, we experimentally study the statistical distribution of intensity fluctuations in underwater wireless optical channels with random temperature and salinity variations, as well as the presence of air bubbles. In particular, we define different scenarios to produce random fluctuations on the water refractive index across the propagation path and, then, examine the accuracy of various statistical distributions in terms of their goodness of fit to the experimental data. We also obtain the channel coherence time to address the average period of fading temporal variations. The scenarios under consideration cover a wide range of scintillation index from weak to strong turbulence. Moreover, the effects of beam-expander-and-collimator (BEC) at the transmitter side and aperture averaging lens (AAL) at the receiver side are experimentally investigated. We show that the use of a transmitter BEC and/or a receiver AAL suits single-lobe distributions, such that the generalized Gamma and exponentiated Weibull distributions can excellently match the histograms of the acquired data. Our experimental results further reveal that the channel coherence time is on the order of 10-3s and larger which implies to the slow fading turbulent channels.

Vehicular ad hoc networks (VANETs) have attracted many researchers' attention in recent years. Due to the highly dynamic nature of these networks, providing guaranteed quality-of-service (QoS) video-on-demand (VOD) sessions is a challenging problem. In this paper, a new adaptive geographic routing scheme is proposed for establishing a simplex VOD transmission in urban environments. In this scheme, rather than one route, a number of independent routes are discovered between source and destination vehicles whose number of routes depends on the volume of the requested video and lifetime (span of time in which a route is almost fixed) for each route. A closed-form equation is derived for estimating the connectivity probability of a route, which is used to select best connected routes. Simulation results show the QoS parameters: packet loss ratio is decreased by 40.79% and freezing delay is significantly improved by 25 ms compared with those of junction-based multipath source routing at the cost of 2-ms degradation in the end-to-end delay.

Green communications has been paid much attention recently, to provide energy efficient networks with a high level of quality of service (QoS). In this study, the authors propose a cross‐layer scheduling scheme for the downlink of long‐term evolution networks, considering the buffer state which may be empty in some times. Their target is to minimise energy consumption under the constraints of limited average transmitting power in the base station, and maximum tolerable delay as QoS of heterogeneous users. In the proposed scheme, the authors use an effective capacity link layer model to consider the data link layer characteristics, such as probability of non‐empty buffer and probability of maximum delay, along with the physical layer parameters. Simulation results show more than 48% reduction of energy consumption in peak traffic loads.

Recently, cross-layer relay selection methods are proposed in favor of providing QoS requirements of users in multi-user cooperative networks as a way to achieve a better performance. However, most of them have failed to achieve an acceptable level of fairness in satisfying diverse QoS requirements in a multi-media service. On the other hand, reducing consumed power is essential today in cooperative networks. In this paper, we aim to improve fairness and reduce consumed power in a multi-user cooperative network with multi-media service. For this purpose, we propose a novel algorithm based on management of relay nodes' buffer space and the priority concept during relay selection. Simulation results certify that our algorithm outperforms previous ones in terms of system spectrum efficiency, fairness, and average consumed power.

The performance of cooperative communications is highly dependent on careful resource allocation such as relay selection. Considering individual QoS requirements in a multi user system, traditional cross-layer relay selection schemes, failed to achieve an acceptable level of fairness considering limited buffer capacity of relays. To address this problem, in this paper, we propose a buffer management-based best relay selection algorithm considering both physical and MAC layer parameters. Our simulation results show that the proposed algorithm outperforms traditional ones significantly in terms of fairness, system spectrum efficiency, and average transmission time delay. Moreover, the proposed algorithm can be implemented in a distributed fashion with no need to a central processor and with minimum required signaling among the nodes.

We study the problem of optimizing the symbol error probability (SEP) performance of cluster-based cooperative wireless sensor networks (WSNs). It is shown in the literature that an efficient relay selection protocol based on simple geographical information of the nodes to execute the cooperative diversity transmission, can significantly improve the SEP performance at the destination of such networks. Also, a similar analysis on the optimal power allocation for the source and the relay exists in the literature. However, we propose that to achieve the minimum SEP at the destination, a joint optimization of power allocation and relay selection should be accomplished. To this aim, we reformulate the SEP performance at the destination of a simple cluster-based cooperative WSN in the general form and solve the mentioned joint optimization problem efficiently for a relay deployment scenario. Simulation results demonstrate that the proposed joint optimization can effectively improve the SEP performance of the network for both 1-hop and multi-hop scenarios in comparison with the previous disjoint optimal relay deployment scheme.

In order to overcome the negotiation procedure bottleneck of the standard DCF in wireless mesh networks, the authors propose a new channel reservation function (CRF) that reduces the negotiation overhead of the DCF, which as a result reduces the overall transmission delay effectively without of any extra bandwidth consumption. Furthermore, the authors provide an analytical model for the proposed scheme for which the simulation results measure the amount that the new method can reduce the average total delay for both regular and fragmented mesh topologies demonstrating superiority of the new method over the classic 802.11 solution. Additionally, the authors extend the scheme to multichannel CRF upon which the proposed method can be used for multichannel applications.

A direct sequence spread spectrum (DS-SS) signal uses a large bandwidth so that its power spectral density can be well below the noise level. Hence, detecting the presence of such a signal is a difficult task. In this paper, we propose a new method called negentropy for the detection of DS-SS signal presence, and we compare its performance to that of using kurtosis function. The channel is AWGN plus fast flat or slow non-flat fading. Both methods require no prior knowledge of the spreading sequence used by the transmitter, as well as no need to the channel response of each path and timing of transmitted signals. Both methods measure the non-Gaussianity of the estimated autocorrelation of the received signal. Simulation results show that the negentropy outperforms especially in fading channels. Furthermore, both methods can determine the exact code length of a DS-SS signal.

A new beamforming method for direct sequence code division multiple access systems is presented. This method is based on Rayleigh quotient of the despread signal's covariance matrix. Two eigenvector analysis algorithms, simple steepest descend algorithm and an algorithm presented by Luo, are used to obtain the new beamforming method. It is shown that both algorithms result in the same updating equation for the weight vector. The proposed method needs only 2N+1 complex multiplications and 2N-1 complex additions for a single run, where N is the number of antennas in the array. Accuracy of the estimated angle-of-arrival (AOA) and the produced beam pattern are evaluated for the proposed method. Moreover, the performance of the new method in reducing the probability of bit error in is investigated in both fading and non-fading channels. Simulation results show that the proposed method has a satisfactory performance with the added advantage of having small computational complexity.

Direct sequence spread spectrum transmissions (DS-SS) are now widely used for secure communications, as well as for multiple access. Since DS-SS signals use a large bandwidth, their power spectral densities can be well below the noise level. Hence, such signals are difficult to be detected. In this paper, we propose two different methods (ratio peaks greater than threshold (RPT) and mean distance between peaks greater than threshold (MDP)) for detecting the presence of DS-SS signals hidden in the noise over flat or frequency selective fading channels. These methods require no prior knowledge of the spreading sequence used by the transmitter. Both methods use the estimated autocorrelation of the received signal. We also propose a new algorithm, which is based on the kurtosis function of the received signal autocorrelation. This new proposed algorithm performs better than RPT method. Simulation results show that these methods can detect a signal far below the noise level and the exact code length of the DS-SS signal can also be determined

In this paper we present and evaluate a low cost simple method for accurate time of arrival (TOA) estimation in multipath fading channels for positioning purposes. It is well known that TOA estimations are biased in multipath propagation and non-line-of-sight (NLOS) conditions. These biases will result in poor position estimations in wireless networks. Recently super-resolution methods have been widely used for high resolution TOA estimations. Although these methods result in accurate TOA estimates, but they need strong processors that in many cases makes them impractical. Here, using time domain properties of the estimated channel delay profile, we introduce a simple method to reduce the TOA estimation errors due to the multipath fading channels. In order to evaluate the proposed method, we simulate a UMTS network based on 3GPP standards. The positioning error using the proposed method will be compared with the ones obtained by MUSIC algorithm which is a famous and accurate super-resolution method. Simulation results show the effectiveness of the proposed method

A novel adaptive beamforming method for direct sequence code division multiple access (DS-CDMA) communication systems is presented in this paper. This method is based on finding the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the despread signal. The proposed beamforming method needs only 2N+1 complex multiplications and 2N-1 complex additions for a single run, where N is the number of antennas in the array. The performance of the proposed method in estimating the angle-of-arrival (AOA) of the desired signal, producing the antenna array pattern and decreasing the probability of bit error is evaluated in different propagation environments. The proposed algorithm has low computational load with satisfactory performance

Link adaptation of a wireless cellular network is of much attention due to the desire of maximizing both the average link throughput and coverage reliability, which have conflicting effect on each other. In this paper, the intra-cell link adaptation problem is formulated as a non-differentiable constrained optimization problem to maximize average link throughput while guaranteeing the best possible coverage reliability. To achieve this, a cutting plane optimization algorithm is employed. The performance of our method: adaptive modulation/coding with power management (AMCWPM) using proposed algorithm is compared with that of an adaptive modulation/coding (AMC) with no power management. Simulation results show that the performance of our method improves both average link throughput and coverage reliability by at least 10% while in each case the other parameter (i.e. respectively, coverage reliability and average link throughput) is hold as much as that of the AMC with no power management

This paper proposes a two-phase hybrid algorithm for optimal placement of radioports. The placement strategy minimizes the dynamic range of the channel, and thus reduces the peak transmit power needed by a wireless handset. The performance of the proposed algorithm is compared with that of an exhaustive search, as well as with that of a simplex algorithm. Simulation results show that the two-phase hybrid algorithm, although suboptimal, outperforms the simplex algorithm with only a modest increase in computation time. It has the added advantage of being quite insensitive to the seed solution.