Modern data center networks support a wide range of applications with dynamic and diverse traffic patterns, necessitating effective load balancing to ensure performance and scalability. This paper presents ARION, a novel distributed load balancing algorithm designed for leaf-spine architectures. ARION utilizes flow aggregation and real-time link utilization metrics, enabling each leaf switch to make informed, congestionaware routing decisions without requiring any hardware modifications to the spine switches. This approach enables effective load distribution while substantially reducing packet reordering and associated overhead. We establish a theoretical framework to assess ARION’s performance, including convergence properties and a performance bound, demonstrating that it operates within a factor of 2 of the optimal solution under heavy loads. Comprehensive simulations covering various workloads and traffic types reveal that ARION consistently outperforms common alternatives such as ECMP, CONGA, DRILL, and LetFlow, achieving a superior balance between optimal load distribution and minimal packet reordering. ARION offers a scalable, low-overhead solution for the evolving demands of data center environments.

We consider the problem where an active DecisionMaker (DM) is tasked to identify the true hypothesis using as few as possible observations while maintaining accuracy. The DM collects observations according to its determined actions and knows the distributions under each hypothesis. We propose a deterministic and adaptive multi-stage hypothesis-elimination strategy where the DM selects an action, applies it repeatedly, and discards hypotheses in light of its obtained observations. The DM selects actions based on maximal separation expressed by the distance between the parameter vectors of each distribution under each hypothesis. Close distributions can be clustered, simplifying the search and significantly reducing the number of required observations. Our algorithms achieve vanishing Average Bayes Risk (ABR) as the error probability approaches zero, i.e., the algorithm is asymptotically optimal. Furthermore, we show that the ABR is bounded when the number of hypotheses grows. Simulations are carried out to evaluate the algorithm's performance compared to another multi-stage hypothesis-elimination algorithm, where an improvement of several orders of magnitude in the mean number of observations required is observed.

We consider the standard Multi-User Multiple-Input-Multiple-Output (MU-MIMO) system, where only$K$active users, unknown in advance, out of$N$, wish to convey their messages to a single receiver. We derive two necessary lower bounds on the number of antennas (degrees of freedom) on both the receiver and transmitter sides, where the first holds for any MU-MIMO system and the second holds for any energy detection-based MU-MIMO. Then, we revisit techniques with identical scaling laws, such as compressive sensing and group testing, and discuss when the optimal antenna scaling laws for the MU-MIMO problem can be obtained. We also numerically evaluate the presented bounds and compare them against recent achievability results from the last ISIT.

The number of wireless devices continues to grow, and more antennas per device are added, increasing the challenge of efficient resource allocation, especially for uplink streams. To address this, we propose a novel massive multiple-user multiple-input-multiple-output (MU-MIMO) scheme based on Group Testing (GT) with non-cooperative self-scheduling users, reducing the required overhead and complexity. Specifically, we show that out of a population ofNdevices withMmessages each, it is possible for the base station (BS) to jointly identify and decode up toKdevices, unknown in advance, simultaneously without the BS applying any scheduling algorithm or collecting channel state information. The BS efficiently decodes the transmissions with vanishing error probability using onlyO(KlogNM) antennas, which implies order–optimal number of antennas.

Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests can influence the makeup of future tests. Using an information theoretic point of view, Aldridge 2012 showed that in the regime of a few defectives, adaptivity does not help much, as the number of tests required is essentially the same as for non-adaptive GT. Secure GT considers a scenario where there is an eavesdropper who may observe on average a fraction$\delta $of the tests results, yet should not be able to infer the status of the items. In the non-adaptive scenario, the number of tests required is$1/(1-\delta)$times the number of tests without the secrecy constraint. In this paper, we consider Secure Adaptive GT. Specifically, when during the makeup of the pools one has access to a private feedback link from the lab, of rate$R_{f}$. We prove that the number of tests required for both correct reconstruction at the legitimate lab, with high probability, and negligible mutual information at the eavesdropper is$1/min\{1,1-\delta +R_{f}\}$times the number of tests required with no secrecy constraint. Thus, unlike non-secure GT, where an adaptive algorithm has only a mild impact, under a security constraint it can significantly boost performance. A key insight is that not only the adaptive link should disregard the actual test results and simply send keys, these keys should be enhanced through a “secret sharing” scheme before usage. We derive sufficiency and necessity bounds that completely characterizes the Secure Adaptive GT capacity. Moreover, we consider additional models of Secure Adaptive GT, where we make a clear distinction between the lab performing the tests, and the doctor analyzing the results. Specifically, we consider curious but non-malicious, non-cooperating labs. Each lab gets a fraction$\delta $of pool-tests to perform. Yet, we want to keep each lab ignorant regarding the status of the items. In contrast, the doctor who gets all outcomes, should successfully decode. When there is a feedback from each lab, we show that even if a curious lab obviously sees its own feedback (i.e., it is locally-public to Eve), secure adaptive GT is still possible, and at a rate that can be equal to the one without a security constraint at all, by an application of the Leftover Hash Lemma, using the data of one lab to protect against another.

The number of wireless devices continues to grow, and more antennas per device are being added, increasing the challenge of efficient resource allocation, especially for uplink streams. To address this, we propose a novel massive multipleuser multiple-input-multiple-output (MU-MIMO) scheme based on Group Testing (GT) with non-cooperative self-scheduling users, reducing the required overhead and complexity. Specifically, we show that out of a population of N devices with $\mathcal{M}$ messages each, it is possible for the base station (BS) to jointly identify and decode up to K devices, unknown in advance, simultaneously and without any scheduling or channel state information. The BS efficiently decodes the transmissions with vanishing error probability using only $O(K\log N\mathcal{M})$ antennas, which implies order–optimal sum-rate.

Dynamic resource allocation mechanism is an essential building block in contemporary cloud computing environment, enabling the support of the large variability of incoming requests from an enormous number of applications utilizing such cloud infrastructure. In this article, we devise a dynamic resource allocation mechanism that optimizes the application’s profit under the set of costs and revenues while maintaining performance constraints. Specifically, we devise a decision-maker (DM) agent which formulates the joint admission control, scaling and load balancing problem as a stochastic process solvable by Markov decision process (MDP) which provides the optimal policy. Accordingly, at each time instance, the DM can determine based on the system’s current state, on the set of requirements and on the set of costs, whether to add or release a VM (scale-out or scale-in, respectively), whether to admit or reject an upcoming task and if admitting it, which VM to allocate it to. We explore the value function structure and provide insights with respect to the optimal policies produced from it. To address scalability issues of the detailed MDP solution we provide an alternative solution by abstract MDP which consolidates multiple system states into a single abstract state, hence can cope with much larger systems at the expense of slight performance degradation. To demonstrate the feasibility of the suggested scheme, we designed and implemented it, alongside with two traditional auto-scalers, on the Amazon Web Services (AWS) infrastructure. We ran numerous MATLAB simulations and AWS-based experiments which provided insights and demonstrated superiority against the traditional policies we compared with.

Distributed scheduling is an attractive approach for the Multiple-Access Channel (MAC). However, when a subset of the users access the channel simultaneously, distributed rate coordination is necessary, and is a major challenge, since the achievable rate of each user highly depends on the channels of other active users. That is, given a detection technique, e.g., Zero-Forcing (ZF), the rate at which a user can transmit depends on the channels other transmitting users have, a knowledge which is usually unavailable in distributed schemes. Fixing a rate and accepting some outage probability when this rate is too high is common practice in these cases. In this paper, we analyze the outage probability of a distributed, asymptotically optimal threshold-based scheduling algorithm under ZF. We rigorously evaluate the distribution of the relevant projections and give upper and lower bounds on the outage probability as a function of the algorithm parameters. At the limit of a large number of users, the bounds match, resulting in the true asymptotic characterization of the outage probability.

Covert communication conceals transmission of messages between Alice and Bob from an adversary, Willie, who tries to determine if a transmission took place or not. While covert communication in a basic, standard setting where all variables are known to Willie, results in the well-known square-root law, when a jammer is present and assists Alice by creating uncertainty in Willie's decoder, a strictly positive transmission rate is possible. In this work, we analyze the case where the jammer is equipped with multiple antennas. Specifically, we analyze the effect of multiple antennas at the jammer on Alice's transmission power and consequently on the transmission rate. We consider both the case where the channel knowledge of Willie is known as well as the case where it is unknown. We formulate several optimization problems for the transmission strategies of the jammer to maximize his assistance to Alice, in terms of maximizing Bob's received SNR and consequently the covert rate. When the channel information is known to the jammer, we show that under an achievable covertness scheme, the optimal strategy of the jammer is to perform beamforming towards a single direction with all his available power. This direction though, is not trivial, since it reflects a tradeoff point between minimizing the interference at Bob and maximizing the interference at Willie. When the channel knowledge is unknown, we show that the optimal strategy of the jammer is either to transmit isotropically to all directions or to the null-space of Bob, where this choice depends on certain channel conditions. This is in contrast to current schemes in the literature. Furthermore, we extend the optimization problems to the case where Bob is also equipped with multiple antennas, and provide insightful results, shown to be asymptotically optimal, accompanied by simulations.

The principal goal ofGroup Testing(GT) is to identify a small subset of “defective” items from a large population, by grouping items into as few test pools as possible. The test outcome of a pool is positive if it contains at least one defective item, and is negative otherwise. GT algorithms are utilized in numerous applications, and in many of them maintaining the privacy of the tested items, namely, keeping secret whether they are defective or not, is critical. In this paper, we consider a scenario where there is an eavesdropper (Eve) who is able to observe a subset of the GT outcomes (pools). We propose a new non-adaptiveSecure Group Testing(SGT) scheme based on information-theoretic principles. The new proposed test design keeps the eavesdropper ignorant regarding the items’ status. Specifically, when the fraction of tests observed by Eve is$0 \leq \delta < 1$, we prove that with the naive Maximum Likelihood (ML) decoding algorithm the number of tests required for both correct reconstruction at the legitimate user (with high probability) and negligible information leakage to Eve is$\frac {1}{1-\delta }$times the number of tests required with no secrecy constraint for the fixed$K$regime. By a matching converse, we completely characterize the Secure GT capacity. Moreover, we consider the Definitely Non-Defective (DND) computationally efficient decoding algorithm, proposed in the literature for non-secure GT. We prove that with the new secure test design, for$\delta < 1/2$, the number of tests required, without any constraint on$K$, is at most$\frac {1}{1/2-\delta }$times the number of tests required with no secrecy constraint.

Compute and Forward (CF) is a coding scheme which enables receivers to decode linear combinations of simultaneously transmitted messages while exploiting the linear properties of lattice codes and the additive nature of a shared medium. The scheme was originally designed for relay networks, yet, it was found useful in other communication problems, such as MIMO communication. Works in the current literature assume a fixed number of transmitters and receivers in the system. However, following the increase in communication networks density, it is interesting to investigate the performance of CF when the number of transmitters is large. In this work, we show that as the number of transmitters, L, grows, CF becomes degenerated, in the sense that a relay prefers to decode only one (strongest) user instead of any other linear combination of the transmitted codewords, treating the other users as noise. Moreover, the system's sum-rate tends to zero as well. This makes scheduling necessary in order to maintain the superior abilities CF provides. We thus examine the problem of scheduling for CF. We start with insights on why good scheduling opportunities can be found. Then, we provide an asymptotically optimal, polynomial-time scheduling algorithm and analyze its performance. We conclude that with proper scheduling, CF is not merely non-degenerated, but, in fact, provides a gain for the system sum-rate, up to the optimal scaling law of O(loglogL).

The 802.11ac is a significant landmark in wireless communications, as it pushes towards new rate limits by utilizing downlink multiuser multiple-input multiple-output (MIMO) beamforming to transmit data to various locations simultaneously. However, successful beamforming relies on intelligent user selection which requires, in turn, extensive overhead of channel calibration between the AP and each of the candidate users. The large overhead involved in the user selection procedure overwhelms the multiuser gain and hinders the utilization of multiuser MIMO. The phenomenon is even more acute when APs handle large groups of mobile users, which frequently associate and disconnect, making the process of acquiring channel state from all users and selecting the appropriate group even harder. Thus, the subtle relation between the achievable rate of a scheduling algorithm and the overhead it requires is significant for the 802.11ac performance analysis. In this paper, we provide a rigorous analysis of distributed algorithms that schedule a group of users for the downlink. In particular, we accommodate common scheduling methods for the 802.11ac protocol and analyze both their achievable rate and their calibration process overhead. Both analysis and extensive simulations depict the superiority of simple threshold-based methods in terms of the throughput.

Data collection in Wireless Sensor Networks (WSN) draws significant attention, due to emerging interest in technologies ranging from Internet of Things (IoT) networks to simple “Presence” applications, which identify the status of the devices (active or inactive). Numerous Medium Access Control (MAC) protocols for WSN, which can address the challenge of data collection in dense networks, were suggested over the years. Most of these protocols utilize the traditional layering approach, in which the MAC layer is unaware of the encapsulated packet payload, and therefore there is no connection between the data collected, the physical layer and the signaling mechanisms. Nonetheless, in many of the applications that intend to utilize such protocols, nodes may need to exchange very little information, and do so only sporadically, that is, while the number of devices in the network can be very large, only a subset wishes to transmit at any given time. Thus, a tailored protocol, which matches the signaling, physical layer and access control to traffic patterns is required. In this work, we design and analyze a data collection protocol based on information theoretic principles. In the suggested protocol, the sink collects messages from up to K sensors simultaneously, out of a large population of sensors, without knowing in advance which sensors will transmit, and without requiring any synchronization, coordination or management overhead. In other words, neither the sink nor the other sensors need to know who are the actively transmitting sensors, and this data is decoded directly from the channel output. We provide a simple codebook construction with very simple encoding and decoding procedures. We further design a secure version of the protocol, in which an eavesdropper observing only partial information sent on the channel cannot gain significant information on the messages transmitted or even which are the sources that sent these messages.

In this work, we study the optimal configuration of the physical layer in wireless networks by means of Semi-Markov Decision Process (SMDP) modeling. In particular, assume the physical layer is characterized by a set of potential operating points, with each point corresponding to a rate and reliability pair; for example, these pairs might be obtained through a now-standard diversity-multiplexing tradeoff characterization. Given the current network state (e.g., buffer occupancies), a Decision Maker (DM) needs to dynamically decide which operating point to use. The SMDP problem formulation allows us to choose from these points. A solution to the SMDP problem is an optimal selection of operating points, which is expressed by a decision rule as a function of the number of packets in the source's finite queue, the channel state, and the size of the packet to be transmitted. We derive a general solution to the SMDP which covers various model configurations, packet size distributions and channel dynamics. For the specific case of exponential transmission times, we analytically prove the optimal policy has a threshold structure. Numerical results validate this finding, as well as depict muti-threshold policies for time varying channels such as the Gilbert-Elliott channel.

Covert communication conceals transmission of messages from Alice to Bob out of a watchful adversary, Willie, which tries to determine if a transmission took place or not. While covert communication in a basic, vanilla settings where all variables are known to Willie results in the well known square-root law, when a jammer is present and assists Alice by creating uncertainty in Willie's decoder, this transmission may have a positive rate.In this work, we analyze the case where the jammer is equipped with multiple antennas and obtain the optimal transmission strategy of the jammer in order to maximize his assistance to Alice, in terms of maximizing a ratio between Willie's and Bob's noise variance. We show that the optimal strategy of the jammer is to perform beamforming towards a single direction with all his available power. This direction though, is not trivial, since it reflects an optimal tradeoff point between minimizing the interference at Bob and maximizing the interference at Willie.

The principal mission of multi-source multicast (MSM) is to disseminate all messages from all sources in a network to all destinations. MSM is utilized in numerous applications. In many of them, securing the messages disseminated is critical. A common secure model is to consider a network where there is an eavesdropper which is able to observe a subset of the network links, and seeks a code which keeps the eavesdropper ignorant regarding all the messages. While this is solved when all messages are located at a single source, secure MSM (SMSM) is an open problem, and the rates required are hard to characterize in general. In this paper, we consider individual security, which promises that the eavesdropper has zero mutual information with each message individually, or, more generally, with sub sets of messages. We completely characterize the rate region for SMSM under individual security, and show that such a security level is achievable at the full capacity of the network, that is, the cut-set bound is the matching converse, similar to non-secure MSM. Moreover, we show that the field size is similar to non-secure MSM and does not have to be larger due to the security constraint.

Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests can influence the makeup of future tests. This scenario has been studied from an information theoretic point of view. Aldridge 2012 showed that in the regime of a few defectives, adaptivity does not help much, as the number of tests required for identification of the set of defectives is essentially the same as for non-adaptive GT. Secure GT considers a scenario where there is an eavesdropper who may observe a fraction δ of the outcomes, and should not be able to infer the status of the items. In the non-adaptive scenario, the number of tests required is 1/(1-δ) times the number of tests without the secrecy constraint. In this paper, we consider Secure Adaptive GT. Specifically, when an adaptive algorithm has access to a private feedback link of rate Rf, we prove that the number of tests required for both correct reconstruction at the legitimate user, with high probability, and negligible mutual information at the eavesdropper is 1/min{1,1-δ+Rf} times the number of tests required with no secrecy constraint. Thus, unlike non-secure GT, where an adaptive algorithm has only a mild impact, under a security constraint it can significantly boost performance. A key insight is that not only the adaptive link should disregard test results and send keys, these keys should be enhanced through a “secret sharing” scheme before usage.

Distributed scheduling is an attractive approach for the Multiple-Access Channel (MAC). However, when a subset of the users access the channel simultaneously, distributed rate coordination is necessary, and is a major challenge, since the channel capacity of each user highly depends on the channels of other active users. That is, given a detection technique, e.g., Zero-Forcing (ZF), the rate at which a user can transmit depends on the channels other transmitting users have, a knowledge which is usually unavailable in distributed schemes. Fixing a rate and accepting some outage probability when this rate is too high is common practice in these cases. In this paper, we analyze the outage probability of a distributed, asymptotically optimal threshold-based scheduling algorithm under ZF. We rigorously evaluate the distribution of the relevant projections, and give upper and lower bounds on the outage probability as a function of the algorithm parameters.

Consider a Compute and Forward (CF) relay network with L users and a single relay. The relay tries to decode a linear function of the transmitted signals. For such a network, letting all L users transmit simultaneously, especially when L is large, causes a significant degradation in the rate in which the relay is able to decode. In fact, the rate goes to zero very fast with L. Therefore, in each transmission phase only a fixed number of users should transmit, i.e., users should be scheduled. In this work, we examine the problem of scheduling for CF and lay the foundations for identifying the optimal schedule which, to date, lacks a clear understanding. Specifically, we start with insights why when the number of users is large, good scheduling opportunities can be found. Then, we provide an asymptotically optimal, polynomial time scheduling algorithm and analyze it's performance. We conclude that scheduling under CF provides a gain in the system sum-rate, up to the optimal scaling law of O(log log L).

Consider the problem of a multiple access channel with a large number of users. In such a system, mostly due to practical constraints (e.g., decoding complexity), not all users can be scheduled together, and usually only one user may transmit at any given time. Assuming a distributed, opportunistic scheduling algorithm, we analyze the system's properties, such as delay, QoS, and capacity scaling laws. Specifically, we start with analyzing the performance while assuming the users are not necessarily fully backlogged, focusing on the queuing problem and, especially, on the strong dependence between the queues. We first extend a known queuing model by Ephremides and Zhu, to give new results on the convergence of the probability of collision to its average value (as the number of users grows), and hence for the ensuing system performance metrics, such as throughput and delay. This model, however, is limited in the number of users one can analyze. We thus suggest a new model, which is much simpler yet can accurately describe the system behavior when the number of users is large. We then proceed to the analysis of this system under the assumption of time dependent channels. Specifically, we assume each user experiences a different channel state sequence, expressing different channel fluctuations (specifically, the Gilbert-Elliott model). The system performance under this setting is analyzed, along with the channel capacity scaling laws.

Consider the problem of a multiple-input multiple-output multiple-access channel at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. However, since solutions which collect channel state information from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we analyze a distributed user selection algorithm, which selects a group of users to transmit without coordinating between users and without all users sending CSI to the base station. This threshold-based algorithm is analyzed for both zero-forcing and minimum mean square error receivers, and its expected sum rate in the limit of large number of users is investigated. It is shown that for large number of users, it achieves the same scaling laws as the optimal centralized scheme.

Network traffic classification is fundamental to network management and its performance. However, traditional approaches for traffic classification, which were designed to work on a dedicated hardware at very high line rates, may not function well in a virtual software-based environment. In this paper, we devise a novel fingerprinting technique that can be utilized as a software-based solution which enables machine-learning-based classification of ongoing flows. The suggested scheme is very simple to implement and requires minimal resources, yet attains very high accuracy. Specifically, for TCP flows, we suggest a fingerprint that is based on zero-length packets, hence enables a highly efficient sampling strategy which can be adopted with a single content-addressable memory rule. The suggested fingerprinting scheme is robust to network conditions such as congestion, fragmentation, delay, retransmissions, duplications, and losses and to varying processing capabilities. Hence, its performance is essentially independent of placement and migration issues, and thus yields an attractive solution for virtualized software-based environments. We suggest an analogous fingerprinting scheme for user datagram protocol traffic, which benefits from the same advantages as the TCP one and attains very high accuracy as well. Results show that our scheme correctly classified about 97% of the flows on the dataset tested, even on encrypted data.

The principal mission of Multi-Source Multicast (MSM) is to disseminate all messages from all sources in a network to all destinations. MSM is utilized in numerous applications. In many of them, securing the messages disseminated is critical. A common secure model is to consider a network where there is an eavesdropper which is able to observe a subset of the network links, and seek a code which keeps the eavesdropper ignorant regarding all the messages. While this is solved when all messages are located at a single source, Secure MSM (SMSM) is an open problem, and the rates required are hard to characterize in general. In this paper, we consider Individual Security, which promises that the eavesdropper has zero mutual information with each message individually. We completely characterize the rate region for SMSM under individual security, and show that such a security level is achievable at the full capacity of the network, that is, the cut-set bound is the matching converse, similar to non-secure MSM. Moreover, we show that the field size is similar to non-secure MSM and does not have to be larger due to the security constraint.

Compute and Forward (CF) is a promising relaying scheme which, instead of decoding single messages or forwarding/amplifying information at the relay, decodes linear combinations of the simultaneously transmitted messages. The current literature includes several coding schemes and results on the degrees of freedom in CF, yet for systems with a fixed number of transmitters and receivers. It is unclear, however, how CF behaves at the limit of a large number of transmitters. In this paper, we investigate the performance of CF in that regime. Specifically, we show that as the number of transmitters grows, CF becomes degenerated, in the sense that a relay prefers to decode only one (strongest) user instead of any other linear combination of the transmitted codewords, treating the other users as noise. Moreover, the sum-rate tends to zero as well. This makes scheduling necessary in order to maintain the superior abilities CF provides. Indeed, under scheduling, we show that non-trivial linear combinations are chosen, and the sum-rate does not decay, even without state information at the transmitters and without interference alignment.

The principal mission of Group Testing (GT) is to identify a small subset of “defective” items from a large population, by grouping items into as little as possible test pools. The test outcome of a pool is positive if it contains at least one defective item, and is negative otherwise. GT algorithms are utilized in numerous applications, and in most of them the privacy of the tested subjects, namely, whether they are defective or not, is critical. In this paper, we consider a scenario where there is an eavesdropper (Eve) which is able to observe a subset of the GT outcomes (pools). We propose a new non-adaptive Secure Group Testing (SGT) algorithm based on information theoretic principles, which keeps the eavesdropper ignorant regarding the items' status. Specifically, when the fraction of tests observed by Eve is 0 ≤ δ <; 1, we prove that the number of tests required for both correct reconstruction at the legitimate user (with high probability) and negligible mutual information at Eve's side is 1/1-δ times the number of tests required with no secrecy constraint.

In this paper, we study the secrecy rate and outage probability in Multiple-Input-Single-Output (MISO) Gaussian wiretap channels at the limit of a large number of legitimate users and eavesdroppers. In particular, we analyze the asymptotic achievable secrecy rates and outage, when only statistical knowledge on the wiretap channels is available to the transmitter. The analysis provides exact expressions for the reduction in the secrecy rate as the number of eavesdroppers grows, compared to the boost in the secrecy rate as the number of legitimate users grows.

Consider a transmission scheme with a single transmitter and multiple receivers over a faulty broadcast channel. For each receiver, the transmitter has a unique infinite stream of packets, and its goal is to deliver them at the highest throughput possible. While such multiple-unicast models are unsolved in general, several network coding-based schemes were suggested. In such schemes, the transmitter can either send an uncoded packet, or a coded packet which is the function of a few packets. The packets sent can be received by the designated receiver (with some probability) or heard and stored by other receivers. Two functional modes are considered; the first presumes that the storage time is unlimited, while in the second one it is limited by the given time to expire (TTE) parameter. We model the transmission process as an infinite-horizon Markov decision process (MDP). Since the large state space renders exact solutions computationally impractical, we introduce policy-restricted and induced MDPs with significantly reduced state space, and prove that with proper reward function they have equal optimal value function (hence equal optimal throughput). We then derive a reinforcement learning algorithm, which learns the optimal policy for the induced MDP. This optimal strategy of the induced MDP, once applied to the policy of the restricted one, significantly improves over uncoded schemes. Next, we enhance the algorithm by means of analysis of the structural properties of the resulting cost functional. We demonstrate that our method scales well in the number of users, and automatically adapts to the packet loss rates, unknown in advance. In addition, the performance is compared to the recent bound by Wang, which assumes much stronger coding (e.g., intrasession and buffering of coded packets), yet is shown to be comparable

Downlink Multi-User MIMO (MU-MIMO) enables the simultaneous spatial sharing of the channel by multiple users to achieve a capacity gain over Single-Input Single-Output (SISO) systems. Unfortunately, the overhead required to enable multi-user MIMO is much higher than the overhead required for single-stream systems. Namely, for K users, collection of channel state information requires K transmission exchanges (i.e., O(K)) between the AP and users. Likewise, the MU-MIMO acknowledgement process also requires the same amount of exchanges, thus reducing the performance gains attained via simultaneous downlink transmission. In this paper, we design, implement, and experimentally evaluate Concurrent Uplink Control Messages (CUiC) to scale the MU-MIMO control information exchange process and improve the efficiency of 802.11ac-based MU-MIMO networks. Our key technique is the design of new channel sounding and acknowledgement mechanisms that enable multiple users to transmit their reverse-direction control messages (i.e., beamforming reports and acknowledgments) concurrently to the AP, in O(1) transmission slots. We implement CUiC and perform an extensive set of experiments and demonstrate throughput gains of more than 100% compared to 802.11ac.

The mobility and portability of next-generation cellular devices must address limited accessibility to power; hence, power-saving techniques of mobile terminals are crucial. In this paper, we provide a comprehensive measurement study of power-save mechanisms implemented in current IEEE 802.16 (WiMAX) deployed networks. Particularly, we examine the power consumption of the various transmission and reception modes and compare them with the power consumption during Idle and Sleep modes. We show, both experimentally and analytically, that although theoretically, power consumption can be dramatically reduced by employing an efficient algorithm that alternates between power-save and active modes, lack of cross-layer coordination prevents efficient power-save implementation. We suggest and implement a simple proactive buffering solution that delays the sporadic traffic generated by the upper layers such as keep-alive messages, when the device is in Idle mode and show that such an enhancement can dramatically reduce the device's power consumption. In this paper, we discuss a strategy, termed Intra Frame Power-Save (IFPS), which, although not standardized by 802.16, is implemented by some leading vendors. IFPS does not require any cross-layer coordination, yet can dramatically reduce power consumption even while the device is in operational mode. We suggest ways for further reducing power consumption while performing the schedule by the base station utilizing the IFPS mechanism.

Recent advances in the standardization of 4G cellular networks introduce the notion of multi-coverage, where multiple base stations may collaboratively satisfy the demands of mobile users. We provide a theoretical model for studying such multi-coverage environments, in highly heterogeneous settings, where users demands and profits may vary, as can base stations' capacities and the rates with which they can service the users. Whereas previous works provided solutions that were only applicable to scenarios where rates are uniform throughout the network, or allowed a mobile user to be serviced by at most one base station, we present several algorithms for the multi-coverage problem in the presence of non-uniform rates, and analyze their performance. We complete our study by a simulation study that further validates our results and provides further insight into algorithm design, depending on the users' characteristics.

In this work, we consider the problem of a Multiple-Input Single-Output (MISO) Multiple-Access Channel with a large number of users K. In practical scenarios, only a small sub-set of the users can be scheduled simultaneously. However, since solutions which collect Channel State Information (CSI) from all users and schedule the best subset to transmit do not scale with K, distributed scheduling algorithms are advantageous. We analyze a distributed scheduling algorithm, which selects a group of users to transmit without coordinating between the users and without all users sending CSI to the base station. The expected capacity under Minimum Mean Squared Error (MMSE) decoding is given, with a special emphasis on large K. It is shown that the algorithm achieves the same scaling laws as the optimal centralized scheme.

In this paper, we present the design, implementation, and evaluation of the novel downlink Multi-User MIMO sounding protocol called MUTE. Our protocol decouples the sounding set selection used to collect Channel State Information (CSI), from the transmission set selection in order to minimize or even eliminate the overhead associated with sounding, while maximizing user selection performance. To this end, MUTE exploits channel statistics to all the different users to predict whether a particular user's channel will remain sufficiently stable, thereby allowing the access point to preclude channel sounding before a MU-MIMO transmission. We show that in indoor WLANs, MUTE can reduce sounding overhead by close to 73% under certain conditions while minimizing rate performance losses due to inaccurate channel estimation.

In this paper, we design novel distributed scheduling algorithms for multiuser multiple-input multiple-output systems and evaluate the resulting system capacity analytically. In particular, we consider algorithms which do not require sending channel state information to a central processing unit, nor do they require communication between the users themselves, yet, the resulting capacity closely approximates that of a centrally controlled system, which is able to schedule the strongest user in each time-slot. In other words, multiuser diversity is achieved in a distributed fashion. Our analysis is based on a novel application of the point-process approximation. This technique, besides tackling previously suggested models successfully, allows an analytical examination of new models, such as nonhomogeneous cases (nonidentically distributed users) or various quality of service considerations. This results in asymptotically exact expressions for the capacity of the system under these schemes, solving analytically problems which to date had been open. Possible applications include, but are not limited to, modern 4G networks, such as 3GPP LTE, or random access protocols.

In this paper, we design, implement, and evaluate 802.11ec (Encoded Control), an 802.11-based protocol without control messages: Instead, 802.11ec employs correlatable symbol sequences that, together with the timing the codes are transmitted, encode all control information and change the fundamental design properties of the MAC. The use of correlatable symbol sequences provides two key advantages: 1) efficiency, as it permits a near order of magnitude reduction of the control time; 2) robustness, because codes are short and easily detectable even at low signal-to-interference-plus-noise ratio (SINR) and even while a neighbor is transmitting data. We implement 802.11ec on a field programmable gate array (FPGA)-based software defined radio. We perform a large number of experiments and show that, compared to 802.11 (with and without RTS/CTS), 802.11ec achieves a vast efficiency gain in conveying control information and resolves key throughput and fairness problems in the presence of hidden terminals, asymmetric topologies, and general multihop topologies.

Consider the problem of joint uplink scheduling and power allocation. Being inherent in almost any wireless system, this resource allocation problem has received extensive attention. Yet, most common techniques either adopt classical power control, in which mobile stations are received with the same Signal-to-Interference-plus-Noise Ratio, or use centralized schemes, in which base stations coordinate their allocations. In this work, we suggest a novel scheduling approach in which each base station, besides allocating the time and frequency according to given constraints, also manages its uplink power budget such that the aggregate interference, "Noise Rise", caused by its subscribers at the neighboring cells is bounded. Our suggested scheme is distributed, requiring neither coordination nor message exchange between the base stations. We rigorously define the allocation problem under noise rise constraints. Inspired by the fact that under the noise-rise constraints, interference experienced by base stations is bounded and its variance is expected to be low, we suggested an approximation in which each base station assumes fixed interference. Under this approximation we formalize the joint scheduling and power control under the noise rise constraints as an optimization problem and characterize the optimal solution. For the special case of homogeneous deployment we give the optimal solution and derive an efficient iterative algorithm to achieve it. We then discuss a relaxed problem, where the noise rise is constrained separately for each sub-channel or resource unit. While sub-optimal, this view renders the scheduling and power allocation problems separate, yielding an even simpler and more efficient solution, while the essence of the scheme is kept. Via extensive simulations, we show that the suggested approach increases overall performance dramatically, with the same level of fairness and power consumption.

Consider the problem of a multiple-antenna Multiple-Access Channel at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. Since solutions which collect Channel State Information (CSI) from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we suggest distributed user selection algorithms which select a group of users to transmit without coordinating between all users and without all users sending CSI to the base station. These threshold-based algorithms are analyzed, and their expected capacity in the limit of large number of users is investigated. It is shown that for large number of users a distributed algorithm can achieve the same scaling laws as the optimal centralized scheme.

The use of asynchronous duty cycling at the MAC layer affords substantial energy savings in wireless networks. This technique is widely used in sensor networks and other types of wireless networks such as ad hoc networks. With asynchronous duty cycling, each node switches alternately between sleeping and active states; each node waking up asynchronously reduces network contention and wireless collisions caused by nodes waking up simultaneously, but also can have undesirable effects on higher layer protocols. In this paper, we study the problem of on-demand route discovery in asynchronous duty-cycling wireless networks and present four optimizations for such route discovery: Delayed Selection, Duty-Cycled Selection, Reply Updating, and Adaptive Backoff. Through detailed ns-2 simulations, we show that, without these optimizations, the routes discovered in asynchronous duty-cycling networks can be over 50% longer than the theoretical shortest routes and can have an ETX 90% larger than the ETX of the optimal routes. With only simple changes made at the MAC or network layers, our optimizations enabled nodes to substantially improve discovered routes, finding routes that were only 0.2% longer than the theoretical shortest routes or routes with an ETX only 9% larger than the ETX of the theoretical optimal-ETX routes, while also reducing route discovery latency and node energy consumption.

In this paper, we design, implement and evaluate 802.11ec (Encoded Control), an 802.11-based protocol without control messages: instead, 802.11ec employs correlatable symbol sequences, which together with the timing the codes are transmitted, encode all control information and change the fundamental design properties of the MAC. The use of correlatable symbol sequences provides two key advantages: (i) efficiency, as it permits a near order of magnitude reduction of the control time; (ii) robustness, because codes are short and easily detectable even at low SINR and even while a neighbor is transmitting data. We implement 802.11ec on an FPGA-based software defined radio. We perform a large number of experiments and show that, compared to 802.11 (with and without RTS/CTS), 802.11ec achieves a vast efficiency gain in conveying control information and resolves key throughput and fairness problems in the presence of hidden terminals, asymmetric topologies, and general multi-hop topologies.

In 802.11 managed wireless networks, the manager can address underserved links by rate-limiting the conflicting nodes. In order to determine to what extent each conflicting node is responsible for the poor performance, the manager needs to understand the coordination among conflicting nodes' transmissions. In this paper, we present a management framework called Management, Inference, and Diagnostics using Activity Share (MIDAS). We introduce the concept of Activity Share, which characterizes the coordination among any set of network nodes in terms of the time they spend transmitting simultaneously. Unfortunately, the Activity Share cannot be locally measured by the nodes. Thus, MIDAS comprises an inference tool that, based on a combined physical, protocol, and statistical approach, infers the Activity Share by using a small set of passively collected, time-aggregate local channel measurements reported by the nodes. MIDAS uses the estimated Activity Share as the input of a simple model that predicts how limiting the transmission rate of any conflicting node would benefit the throughput of the underserved link. The model is based on the current network conditions, thus representing the first throughput model using online measurements. We implemented our tool on real hardware and deployed it on an indoor testbed. Our extensive validation combines testbed experiments and simulations. The results show that MIDAS infers the Activity Share with a mean relative error as low as 4% in testbed experiments.

This paper presents PW-MAC (Predictive-Wakeup MAC), a new energy-efficient MAC protocol based on asynchronous duty cycling. In PW-MAC, nodes each wake up to receive at randomized, asynchronous times. PW-MAC minimizes sensor node energy consumption by enabling senders to predict receiver wakeup times; to enable accurate predictions, PW-MAC introduces an on-demand prediction error correction mechanism that effectively addresses timing challenges such as unpredictable hardware and operating system delays and clock drift. PW-MAC also introduces an efficient prediction-based retransmission mechanism to achieve high energy efficiency even when wireless collisions occur and packets must be retransmitted. We evaluate PW-MAC on a testbed of MICAz motes and compare it to X-MAC, WiseMAC, and RI-MAC, three previous energy-efficient MAC protocols, under multiple concurrent multihop traffic flows and under hidden-terminal scenarios and scenarios in which nodes have wakeup schedule conflicts. In all experiments, PW-MAC significantly outperformed these other protocols. For example, evaluated on scenarios with 15 concurrent transceivers in the network, the average sender duty cycle for X-MAC, WiseMAC, and RI-MAC were all over 66%, while PW-MAC's average sender duty cycle was only 11%; the delivery latency for PW-MAC in these scenarios was less than 5% that for WiseMAC and X-MAC. In all experiments, PW-MAC maintained a delivery ratio of 100%.

Medium access control (MAC) protocols for wireless sensor networks face many challenges, including energy-efficient operation and robust support for varying traffic loads, in spite of effects such as wireless interference or even possible wireless jamming attacks. This paper presents the design and evaluation of the EM-MAC (Efficient Multichannel MAC) protocol, which addresses these challenges through the introduction of novel mechanisms for adaptive receiver-initiated multichannel rendezvous and predictive wake-up scheduling. EM-MAC substantially enhances wireless channel utilization and transmission efficiency while resisting wireless interference and jamming by enabling every node to dynamically optimize the selection of wireless channels it utilizes based on the channel conditions it senses, without use of any reserved control channel. EM-MAC achieves high energy efficiency by enabling a sender to predict the receiver's wake-up channel and wake-up time Implemented in TinyOS on MICAz motes, EM-MAC substantially outperformed other MAC protocols studied. EM-MAC maintained the lowest sender and receiver duty cycles, the lowest packet delivery latency, and 100% packet delivery ratio across all experiments. Our evaluation includes single-hop and multihop flows, as well as experiments with heavy ZigBee interference, constant ZigBee jamming, and Wi-Fi interference.

IEEE 802.11-based mesh networks can yield a throughput distribution among nodes that is spatially biased, with traffic originating from nodes that directly communicate with the gateway obtaining higher throughput than all other upstream traffic. In particular, if single-hop nodes fully utilize the gateway's resources, all other nodes communicating with the same gateway will attain very little (if any) throughput. In this paper, we show that it is sufficient to rate limit the single-hop nodes in order to give transmission opportunities to all other nodes. Based on this observation, we develop a new rate limiting scheme for 802.11 mesh networks, which counters the spatial bias effect and does not require, in principle, any control overhead. Our rate control mechanism is based on three key techniques. First, we exploit the system's inherent priority nature and control the throughput of the spatially disadvantaged nodes by only controlling the transmission rate of the spatially advantaged nodes. Namely, the single-hop nodes collectively behave as a proxy controller for multi-hop nodes in order to achieve the desired bandwidth distribution. Second, we devise a rate limiting scheme that enforces a utilization threshold for advantaged single-hop traffic and guarantees a small portion of the gateway resources for the disadvantaged multi-hop traffic. We infer demand for multi-hop flow bandwidth whenever gateway resource usage exceeds this threshold, and subsequently reduce the rates of the spatially advantaged single-hop nodes. Third, since the more bandwidth the spatially disadvantaged nodes attain, the easier they can \emph{signal} their demands, we allow the bandwidth unavailable for the advantaged nodes to be elastic, i.e., the more the disadvantaged flows use the gateway resources, the higher the utilization threshold is. We develop an analytical model to study a system characterized by such priority, dynamic utilization thresholds, and control by proxy. Moreover, we use simulations to evaluate the proposed elastic rate limiting technique.

In 802.11 managed wireless networks, the manager can address under-served links by rate-limiting the conflicting nodes. In order to determine to what extent each conflicting node is responsible for the poor performance, the manager needs to understand the coordination among conflicting nodes' transmissions. In this paper, we present a management framework called MIDAS (Management, Inference, and Diagnostics using Activity Share). We introduce the concept of Activity Share which characterizes the coordination among any set of network nodes in terms of the time they spend transmitting simultaneously. Unfortunately, the Activity Share cannot be locally measured by the nodes. Thus, MIDAS comprises an inference tool which, based on a combined physical, protocol, and statistical approach, infers the Activity Share by using a small set of passively collected, time-aggregate local channel measurements reported by the nodes. MIDAS uses the estimated Activity Share as the input of a simple model that predicts how limiting the transmission rate of any conflicting node would benefit the throughput of the under-served link. The model is based on the current network conditions, thus representing the first throughput model using online measurements. We implemented our tool on real hardware and deployed it on an indoor testbed. Our extensive validation combines testbed experiments and simulations. The results show that MIDAS infers the Activity Share with an average normalized relative error below 12% in all testbed experiments.

The use of asynchronous duty-cycling in wireless sensor network MAC protocols is common, since it can greatly reduce energy consumption and requires no clock synchronization. However, existing systems using asynchronous duty-cycling do not efficiently support broadcast-based communication that may be used, for example, in route discovery or in network-wide queries or information dissemination. In this paper, we present the design and evaluation of ADB (Asynchronous Duty-cycle Broadcasting), a new protocol for efficient multihop broadcast in wireless sensor networks using asynchronous duty-cycling. ADB differs from traditional multihop broadcast protocols that operate above the MAC layer, in that it is integrated with the MAC layer to exploit information only available at this layer. Rather than treating the data transmission from a node to all of its neighbors as the basic unit of progress for the multihop broadcast, ADB dynamically optimizes the broadcast at the level of transmission to each individual neighbor of a node, as the neighbors asynchronously wakeup. We evaluate ADB both through ns-2 simulations and through measurements in a testbed of MICAz motes using TinyOS, and compare its performance to multihop broadcast based on X-MAC and on RI-MAC. In both evaluations, ADB substantially reduced energy consumption, network load, and delivery latency compared to other protocols, while achieving over 99% delivery ratio.

This paper addresses channel assignment and random medium access design for single-radio multi-channel mesh networks. Two prior approaches include: (i) designing MAC protocols that dynamically select channels based on local information and (ii) partitioning the mesh into subnetworks with different channels and using 802.11 as the medium access protocol. Both of these approaches suffer from limited throughput improvement; the first approach due to wrong or incomplete channel state information that inherently arises in a multi-hop wireless environment, while the second approach due to high interference within each subnetwork. In this paper, we first introduce D1C-CA, Distance-1 Constrained Channel Assignment. D1C-CA statically assigns channels to a set of links as a function of physical connectivity, contention, and the unique gateway functionality of mesh networks, i.e, all Internet (non-local) traffic has a gateway node as its source or destination. To design D1C-CA, we model the channel assignment problem as a new form of graph edge coloring in which edges at distance one are constrained. We prove that the problem is NP-complete and design an efficient heuristic solution for mesh networks. Second, we design an asynchronous control-channel-based MAC protocol that solves multi-channel coordination problems and employs the proposed channel assignment algorithm. Finally, we investigate the performance of our approach through extensive simulations and show considerable performance improvements compared to alternate schemes.

In this paper, we perform an extensive measurement study on a multi-tier mesh network serving 4,000 users. Such dense mesh deployments have high levels of interaction across heterogeneous wireless links. We find that this heterogeneous backhaul consisting of data-carrying (forwarding) linksandnon- data-carrying (non-forwarding) links creates two key effects on performance. First, we show that low-rate management and control packets can produce a disproportionally large degradation in data throughput. We define a metric for this effect called Wireless Overhead Multiplier and use it to quantify the impact of MAC and PHY mechanisms on the the throughput degradation. Surprisingly, we show that these multiplicative effects are primarily driven by the non-forwarding links where, in the worst case, data packets lose physical layer capture to the overhead, yielding disproportionate throughput degradation. Finally, we show that when data flows contend in this worst-case scenario, the loss-based autorate policy is unnecessarily triggered, causing throughput imbalance and poor network utilization.

Significant progress has been made in understanding the behavior of TCP and congestion-controlled traffic over multi- hop wireless networks. Despite these advances, however, no prior work identified severe throughput imbalances in the basic scenario of mesh networks, in which one-hop flows contend with two-hop flows for gateway access. In this paper, we demonstrate via real network measurements, test-bed experiments, and an analytical model that starvation exists in such a scenario, i.e., the one-hop flow receives most of the bandwidth while the two- hop flow starves. Our analytical model yields a solution consisting of a simple contention window policy that can be implemented via mechanisms in IEEE 802.11e. Despite its simplicity, we demonstrate through analysis, experiments, and simulations, that the policy has a powerful effect on network-wide behavior, shifting the network's queuing points, mitigating problematic MAC behavior, and ensuring that TCP flows obtain a fair share of the gateway bandwidth, irrespective of their spatial locations.

Duty cycling is a widely used mechanism in wireless sensor networks (WSNs) to reduce energy consumption due to idle listening, but this mechanism also introduces additional latency in packet delivery. Several schemes have been proposed to mitigate this latency, but they are mainly optimized for light traffic loads. A WSN, however, could often experience bursty and high traffic loads, such as due to broadcast or convergecast traffic. In this paper, we present a new MAC protocol, called Demand Wakeup MAC (DW-MAC), that introduces a new low-overhead scheduling algorithm that allows nodes to wake up on demand during the Sleep period of an operational cycle and ensures that data transmissions do not collide at their intended receivers. This demand wakeup adaptively increases effective channel capacity during an operational cycle as traffic load increases, allowing DW-MAC to achieve low delivery latency under a wide range of traffic loads including both unicast and broadcast traffic. We compare DW-MAC with S-MAC (with and without adaptive listening) and with RMAC using ns-2 and show that DW-MAC outperforms these protocols, with increasing benefits as traffic load increases. For example, under high unicast traffic load, DW-MAC reduces delivery latency by 70% compared to S-MAC and RMAC, and uses only 50% of the energy consumed with S-MAC with adaptive listening. Under broadcast traffic, DW-MAC reduces latency by more than 50% on average while maintaining higher energy efficiency.

The problem of idle listening is one of the most significant sources of energy consumption in wireless sensor nodes, and many techniques have been proposed based on duty cycling to reduce this cost. In this paper, we present a new asynchronous duty cycle MAC protocol, called Receiver-Initiated MAC (RI-MAC), that uses receiver-initiated data transmission in order to efficiently and effectively operate over a wide range of traffic loads. RI-MAC attempts to minimize the time a sender and its intended receiver occupy the wireless medium to find a rendezvous time for exchanging data, while still decoupling the sender and receiver's duty cycle schedules. We show the performance of RI-MAC through detailed ns-2 simulation and through measurements of an implementation in TinyOS in a testbed of MICAz motes. Compared to the prior asynchronous duty cycling approach of X-MAC, RI-MAC achieves higher throughput, packet delivery ratio, and power efficiency under a wide range of traffic loads. Especially when there are contending flows, such as bursty traffic or transmissions from hidden nodes, RI-MAC significantly improves throughput and packet delivery ratio. Even under light traffic load for which X-MAC is optimized, RI-MAC achieves the same high performance in terms of packet delivery ratio and latency while maintaining comparable power efficiency.

In this paper, we develop and analyze a low-complexity cooperative protocol that significantly increases the average throughput of multi-hop upstream transmissions for wireless tree networks. We consider a system in which transmissions are assigned to nodes in a collision free, spatial time division fashion. This protocol exploits the broadcast nature of wireless networks where the communication channel is shared between multiple adjacent nodes within interference range. For any upstream end-to-end flow in the tree, each intermediate node receives information from both one-hop and two-hop neighbors and transmits only sufficient information such that the next upstream one-hop neighbor will be able to decode the packet. This approach can be viewed as the generalization of the classical three node relay channel for end-to-end flows in which each intermediate node becomes successively source, relay and destination. We derive the achievable rate and propose an optimal schedule that realizes this rate for any regular tree network. We show that our protocol dramatically outperforms the conventional scheme where intermediate nodes simply forward the packets hop by hop. At high signal-to-noise ratio, it yields approximatively 80% throughput gain.

In this paper, a low-complexity cooperative protocol that significantly increases the average throughput of multihop upstream transmissions for wireless tree networks is developed and analyzed. A system in which transmissions are assigned to nodes in a collision free, spatial time division fashion is considered. The suggested protocol exploits the broadcast nature of wireless networks where the communication channel is shared between multiple adjacent nodes within interference range. For any upstream end-to-end flow in the tree, each intermediate node receives information from both one-hop and two-hop neighbors and transmits only sufficient information such that the next upstream one-hop neighbor will be able to decode the packet. This approach can be viewed as the generalization of the classical three node relay channel for end-to-end flows in which each intermediate node becomes successively source, relay and destination. The achievable rate for any regular tree network is derived and an optimal schedule that realizes this rate in most cases is proposed. Our protocol is shown to dramatically outperform the conventional scheme where intermediate nodes simply forward the packets hop by hop. At high signal-to-noise ratio (SNR), it yields approximately 66% throughput gain for practical scenarios.

The emergence of network convergence emphasizes the need to support distributed synchronous servers such as TDMoIP (pseudo-wire) and 3G cellular gateways over a packet switched infrastructure. Conse-quently, we formalize the problem of network wide clock frequency synchronization and introduce novel and efficient algorithms to synchronize the frequency among all the clocks in the network with respect to a single frequency. The common thread of our solutions is that they take a network-wide view that accounts for all the clocks in the network and measurements taken over all links to estimate the frequency difference of each clock with respect to the reference clock. The various presented algorithms introduce different trade-offs between the accuracy and the computation complexity. While all our schemes are global, they employ simple pair-wise measurements between neighboring nodes. Consequently, all the algorithms presented in the paper, are simple, easy to implement and require a modest amount of measurement and control traffic. I.

We present a novel approach for the estimation of one-way delays between network nodes without any time synchronization in the network. It is based on conducting multiple and simple one-way measurements among pairs of nodes, and estimating the one-way delays by optimizing the value of a global objective function that is affected by the overall network topology and not just by individual measurements. We examine two objective functions. The first intuitive choice is the least square error (LSE). Using a novel concept of delay-induced link probabilities, we develop a second objective function that is based on the maximum-entropy (ME) principle. Extensive numerical experiments show that both functions considerably outperform the common method of halving the round-trip delays. They also show that ME outperforms the commonly used LSE.

Time synchronization is critical in distributed environments. A variety of network protocols, middleware and business applications rely on proper time synchronization across the computational infrastructure and depend on the clock accuracy. The ''network time protocol" (NTP) is the current widely accepted standard for synchronizing clocks over the Internet. NTP uses a hierarchical scheme in order to synchronize the clocks in the network. In this paper we present a novel non-hierarchical peer-to-peer approach for tune synchronization termed CTP - classless time protocol. This approach exploits convex optimization theory in order to evaluate the impact of each clock offset on the overall objective function. We define the clock offset problem as an optimization problem and derive its optimal solution. Based on the solution we develop a distributed protocol that can be implemented over a communication network and prove its convergence to the optimal clock offsets. For compatibility, the CTP may use the exact format and number of messages used by NTP. We also present methodology and numerical results for evaluating and comparing the accuracy of time synchronization schemes. We show that the CTP substantially outperforms hierarchical schemes such as NTP in the sense of clock accuracy with respect to a universal clock, without increasing complexity.

In this paper we present a novel approach for the estimation of one-way delays from cyclic-path delay measurements that does not require any kind of synchronization among the nodes of the network. Furthermore, this approach takes into account the asymmetric nature of the network, and the fact that traffic flows are not necessarily the same in both directions. Our approach is based on cyclic-path delay measurements, each of which is extracted using a single (source) clock and therefore is accurate. The basic idea of the approach is to express the cyclic-path delays in terms of one-way delay variables. If there were enough independent cyclic-path delay measurements, then one could solve explicitly for the one-way delays. We show that the maximal number of independent measurements that can be taken is smaller hence a procedure for estimating the one-way delay is proposed.

The probability distribution of the number of lost packets within a block of consecutive packet arrivals into a finite buffer is an important quantity in various networking problems. In a previous paper, Cidon, Khamisy and Sidi (1993) introduced a recursive scheme to derive this distribution. In this paper, we derive explicit expressions for this distribution using various versions of the powerful ballot theorem. The expressions are derived for a single source M/M/1/K queue.