Mobile applications rely on persistent connections for push notifications, but network middleboxes silently drop idle connections to reclaim resources. To keep these connections alive, clients transmit periodic heartbeats, which in turn trigger frequent radio wake-ups and drain battery. To address the problem, we present Strand, a server-driven zero-wakeup keep-alive architecture that crafts server-side heartbeats with a precisely calibrated Time-To-Live. These packets intentionally “strand” (expire) at the final middlebox, successfully refreshing the mapping state without traversing the energy-expensive last-hop wireless link, keeping the mobile radio completely asleep. Furthermore, to minimize probing overhead, Strand employs a cost-aware weighted Dynamic Programming (DP) algorithm that incorporates timeout distribution priors to find search boundaries and overcome the severe temporal latency penalties of the traditional exponential approach. Real-world experiments show that Strand reduces active push energy overhead by ∼ 40%. Additionally, our weighted DP-based probing algorithm accelerates timeout convergence by ∼ 28% for TCP and ∼ 26% for UDP over state-of-the-art baselines.

Result reproduction of computer networking research is challenging as the scarcity of open-source implementations and the complexity of heterogeneous system architectures. Even though Large Language Models have demonstrated potential in code generation, existing code generation frameworks often fail to address the long-context constraints and intricate logical dependencies, which are vital in reproducing network systems from academic papers. Thus, we introduce RepLLM, an end-to-end multi-agent framework designed to automate code reproduction from paper content. RepLLM features a collaborative architecture comprising four specialized agents—Content Parsing, Architecture Design, Code Generation, and Audit & Repair, which are coordinated through Shared Memory mechanism to ensure global context consistency. With the enhancement of Structured Chain-of-Thought LLM reasoning and a sandbox-isolated static-dynamic debugging methodology, our framework effectively resolves semantic discrepancies and runtime errors, thereby improving reliable reproductions. Extensive evaluations on representative papers in top conferences demonstrate that RepLLM outperforms state-of-the-art system-level LLM frameworks in generating compile-ready and logically correct systems. Our results show that, with the aid of RepLLM, we can reproduce 95% of the original benchmarks within approximately two hours while reducing token consumption by up to 10% compared with state-of-the-art baselines.

Cooperative broadcast is an efficient approach to improve Wi-Fi broadcast performance in crowded scenarios with densely deployed access points (APs). However, existing concurrent transmission MAC protocols cannot perfectly synchronize APs for the receiving user, and the superimposed channels at users vary over time due to multi-path effects with different carrier frequency offsets (CFOs) from the APs. Traditional channel estimation methods, which treat the superimposed channels as a whole and use a portion of the superimposed channels to derive the rest, are unsuitable. To solve the problem, we propose a fine-grained channel estimation approach that first estimates channel taps and CFOs of each AP, and then reconstructs the superimposed channels. We first study a benchmark channel estimation algorithm that utilizes a widely adopted compressed sensing (CS) technique. However, through analysis and simulations, we show that the CS-based algorithm suffers from high correlation problems in the constructed sensing matrix and the non-sparse channel problem in practice, leading to an estimation error floor at high SNRs. To solve these problems, we present a two-stage channel estimation algorithm. It first estimates the CFOs by identifying the most likely CFO combination matching the received signals, and then estimates the time-domain channel taps. Simulation and experimental results show that the two-stage channel estimation algorithm achieves much lower bit error rate (BER) and packet error rate (PER) than the traditional IEEE 802.11 approach, and the two-stage algorithm outperforms the CS-based algorithm, especially at high SNRs. The network-layer simulation results further demonstrate that, empowered by the proposed two-stage channel estimation algorithm, the cooperative broadcast scheme improves throughput by at least 1.4× (up to 46.8×) compared with the unicast-based broadcast schemes, and by approximately 0.6× to 0.8× compared with the simple uncooperative broadcast scheme.

Uncoordinated random-access protocols are attractive for underwater acoustic (UWA) networks due to their simplicity and low overhead, especially for data collection applications in scuba diving. However, the performance is limited by severe collisions and the challenging UWA channel, including rich multipath and time-varying channel (caused by Doppler effects and user movements). Existing UWA physical-layer waveforms struggle to resolve collisions while maintaining high data rates. This paper presents ZCMod, a Zadoff-Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to mitigate interference and encodes multiple bits via cyclic shifts for high data rates. To address UWA-specific challenges, ZCMod introduces two key designs: 1) shape-based demodulation, which tracks channel response shifts to combat multipath effects; 2) auxiliary modulation, where each symbol is modulated with two ZC sequences—one for channel estimation and the other for data transmission—to handle fast time-varying channels. Experiments and simulations demonstrate that a) ZCMod achieves more robust BER performance and eliminates error floors compared to state-of-the-art (SOTA) methods in slight time-varying channels; and b) ZCMod maintains stable throughput in fast time-varying channels, while SOTA approaches suffer significant degradation.

Orthogonal Time Frequency Space (OTFS) modulation has demonstrated advantages in addressing the doubly selective underwater acoustic (UWA) channels. In this paper, we focus on channel estimation in OTFS-based underwater acoustic communications (UAC), where large Doppler effects in wideband systems increase estimation challenge. We first present an OTFS-UAC system based on the discrete Zak transform, incorporating fractional effects, and the scaling impact induced by the Doppler shifts in wideband systems. To address the superimposing of fractional effects and Doppler scaling, we analyze these characteristics in the time-domain and then map them into the delay-Doppler (DD) domain. Specifically, we formulate channel estimation as a compressed sensing problem and design a refined grid-based sensing matrix through the timedomain effective channel matrix. Utilizing the sparsity of the UWA channel impulse response, we propose a Sparse Bayesian Learning (SBL)-based algorithm that avoids DD domain channel spreading. Simulation and experimental results demonstrate that the proposed algorithm outperforms the comparison algorithms by at least 3dB signal-to-noise ratios (SNR) gain under bit error rate (BER) of 10−2on simulation channels, and by at least 5dB SNR gain under BER of 10−1on experiment channels.

Existing data-plane verifiers face severe performance challenges in verifying hyper-scale underlay data center networks (DCNs) – centralized verifiers often fail to fully exploit the parallelism offered by the modern multi-core CPUs, while distributed verifiers suffer from high overhead due to task distribution and inter-node communication. To overcome these limitations, this paper introduces HeTu, a high-performance centralized parallel data-plane verifier specifically for verifying hyper-scale underlay DCNs. HeTu achieves ultra-fast verification through three key designs: (1) a fully parallel verification framework with small graph construction overhead, (2) a new binary decision diagram management strategy that enables full parallelism by using separated storage and selectively indexing and caching network-level predicates to reduce redundant operations, and (3) an optimized forwarding graph model that aggregates parallel tasks to eliminate redundant computation. Extensive evaluations on synthetic FatTree and large-scale production datasets show that HeTu outperforms state-of-the-art algorithms in runtime by 100× to 6000×, demonstrating its superior scalability and efficiency in data-plane verification of hyper-scale DCNs.

Runtime traffic analysis on programmable data-plane requires substantial human effort, and the high speed and complexity of dataplane often make human capacity the efficiency bottleneck. While existing work has proposed LLM-based approaches, they typically rely on offline network logs, failing to address the human capacity limitations in real-time environments. This paper explores the potential of leveraging evolving LLMs to mitigate these human-centric challenges in real physical dataplane. It outlines a novel framework called NetSophon, which features an LLM-based brain for efficient decision-making and an effective arm to manipulate and perceive the physical programmable dataplane. Through interactions among the brain, arm, and dataplane, NetSophon acts as a "super-copilot" for human operators, facilitating real-time dataplane traffic analysis at scale. A case study demonstrates NetSophon’s potential to assist human operators in interacting with dataplane.

The fundamental challenge of configuration verification in an interdomain network is privacy because each autonomous system (AS) treats its network configuration files as private information and is not willing to share them with others. In this paper, we present IVeri, a scalable privacypreserving interdomain configuration verification system based on secure multi-party computation. IVeri supports privacypreserving verification and scalable verification via the following designs: (1) a verification algorithm that meets secure multi-party computation security requirements, (2) a data aggregator that reduces communication overhead while preserving privacy, (3) an algorithm that accelerates the simulation process based on routing algebra, and (4) an incremental verification algorithm that handles minor configuration changes. Extensive experiments with open-source datasets demonstrate that IVeri's optimization techniques significantly improve scalability, enabling the verification of large networks with 1000 ASes in under 3 hours, which outperforms state-of-the-art solutions.

Uncoordinated random-access protocols are well-suited for underwater acoustic (UWA) networks due to their simplicity and low overhead. However, their performance is hindered by severe collisions and the challenging characteristics of UWA channels such as rich multipath and Doppler effect. Existing UWA physical layer waveforms struggle to resolve collisions while maintaining high data rates. This paper introduces ZCMod, a high-rate waveform allowing uncoordinated concurrent random access in UWA networks. ZCMod employs a Zadoff–Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to minimize inter-user interference and encodes multiple bits through cyclic shifts of the sequences to improve data rates. ZCMod further addresses the unique challenges of UWA channels via two new designs: 1) a shape-based demodulation approach that estimates the data-induced shift of channel response shape between the preamble and data symbols to handle rich multipath, and 2) an auxiliary modulation approach that modulates each data symbol with two ZC sequences, one for extracting current channel response shape and the other for data modulation, to handle the fast time-varying channel. Experimental results in a lake and a swimming pool and extensive simulation results show that a) ZCMod achieves around 100% higher throughput compared with the state-of-the-art (SOTA) approaches in quasi-static channels, and b) ZCMod maintains comparable throughput in fast time-varying channels as in quasi-static conditions, where the SOTA approaches experience significant degradation.

This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-free protocol, the BS blindly allocates a fixed length of preamble to devices for identity detection as it lacks the prior information on the number of active devices K. However, in practice, K varies dynamically over time, resulting in degraded identity detection performance especially when K is large. Consequently, the current grant-free protocol fails to ensure stable identity detection performance. To address this issue, we propose a two-stage communication protocol which consists of estimation of K in Phase I and detection of identities of active devices in Phase II. The preamble length for identity detection in Phase II is dynamically allocated based on the estimated K in Phase I through a table lookup manner such that the identity detection performance could always be better than a predefined threshold. In addition, we design an algorithm for estimating K in Phase I, and exploit the estimated K to reduce the computational complexity of the identity detector in Phase II. Numerical results demonstrate the effectiveness of the proposed two-stage communication protocol and algorithms.

Linear chirp-based underwater acoustic communication has been widely used due to its reliability and long-range transmission capability. However, unlike the counterpart chirp technology in wireless - LoRa, its throughput is severely limited by the number of modulated chirps in a symbol. The fundamental challenge lies in the underwater multi-path channel, where the delayed signal may cause inter-symbol and intra-symbol interfere. In this paper, we present UWLoRa+, a system that realizes the same chirp modulation as LoRa with higher data rate, and address the multi-path challenge via the following new designs: a) we replace the linear chirp used by LoRa with the non-linear chirp to reduce the signal interference range and the collision probability; b) we design an algorithm that first demodulates each path and then combines the demodulation results of detected paths; and c) we replace the Hamming codes used by LoRa with the non-binary LDPC codes to mitigate the impact of the inevitable collision. Experiment results show that the new designs improve the bit error rate (BER) by 3 times, and the packet error rate (PER) significantly, compared with the LoRa's naive design. Compared with an state-of-the-art system for decoding underwater LoRa chirp signal, UWLoRa+ improves the throughput by up to 50 times.

Satisfiability Modulo Theories (SMT) based network configuration verification tools are powerful tools in preventing network configuration errors. However, their fundamental limitation is efficiency, because they rely on generic SMT solvers to solve SMT problems, which are in general NP-complete. In this paper, we show that by leveraging network domain knowledge, we can substantially accelerate SMT-based network configuration verification. Our key insights are: given a network configuration verification formula, network domain knowledge can (1) guide the search of solutions to the formula by avoiding unnecessary search spaces; and (2) help simplify the formula, reducing the problem scale. We leverage these insights to design a new SMT- based network configuration verification tool called NetSMT. Extensive evaluation using real-world topologies and synthetic network configurations shows that NetSMT achieves orders of magnitude improvements compared to state-of-the-art methods.

Uncoordinated random-access protocols are especially suitable for underwater acoustic networks with long propagation delays due to their simplicity. However, their performance is limited by severe collisions caused by uncoordinated access, and the current modulations cannot handle the collisions under the multipath environment. In this paper, we propose a new modulation and a new demodulation algorithm to resolve collisions. In particular, we adopt a Zadoff-Chu (ZC) sequence with cyclic shifts as the modulation, and assign users with different ZC sequences to minimize inter-user interference. To combat the multipath challenge, we leverage the insight that the multipath interference pattern is almost constant within the same packet and the modulated data only shifts the pattern, and develop a pattern-based demodulation algorithm. Trace-driven simulation results show that our new approach allows at least five users, and outperforms the existing approach by at least 8dB. In the future, we intend to develop a real-time system in a realistic environment.

Cooperative broadcast is an efficient approach to improve Wi-Fi broadcast performance in a crowded scenario with densely deployed access points (APs). However, the current concurrent transmission MAC protocols cannot synchronize multi-APs’ signals perfectly for all users. As a result, the superimposed signal from APs is time-varying at the users due to the multiple time-domain channels and carrier frequency offsets (CFOs) from multiple APs. The traditional channel estimation approach that estimates the superimposed channel as a whole is ill-suited for the superimposed signal. In this paper, we propose a fine-grained channel estimation approach to first estimate these channel parameters for each AP, and then reconstruct the superimposed channel. Specifically, we present a two-stage channel estimation algorithm that first estimates the CFOs by discretizing the CFO range and matching the most possible CFOs, and then computes the time-domain channels. Experiment and simulation results show the new channel estimation approach achieves much lower bit error rate (BER) and packet error rate (PER) than the traditional IEEE 802.11 approach. In addition, we propose a distributed mechanism to choose the master AP that initializes multi-APs’ simultaneous transmission, which the current concurrent transmission MAC protocols lack. Network-layer simulation results show that the proposed cooperative broadcast scheme improves the throughput by 64% to 82% compared with the traditional uncooperative broadcast scheme.

Link Adaptation (LA) that dynamically adjusts transmission parameters to accommodate time-varying channels is a critical technology in the Long-Term Evolution/New Radio system. Previous deep reinforcement learning (DRL)-based LA techniques directly select the Modulation and Coding Schemes (MCS) for each transmission. However, the frequent inference results in a high computational resource cost, leading to significant decision delays and potentially compromising the overall performance. To address these challenges, we present a new algorithm named TD3-OLLA, building upon separating the frequent selection of MCS from the time-consuming DRL model inference process. In particular, we introduce a two-level control framework that makes the real-time MCS selection using the traditional Outer Loop Link Adaptation (OLLA) algorithm and employs the DRL algorithm to tune OLLA's parameters. Our algorithm adopts the advanced Twin Delayed Deep Deterministic Policy Gradient (TD3) model and methods like classified experience replay to enhance the performance against rapidly changing link conditions. The simulation results demonstrate that TD3-OLLA achieves higher throughput than state-of-the-art LA techniques with an ultra-low overhead—the computation time is reduced by 80% compared to other DRL-based algorithms. Furthermore, it exhibits a high tolerance for model decision delays, making it well-suited for practical communication systems with strict latency requirements.

This paper presents the first orthogonal frequency-division multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for wireless federated edge learning, where multiple edge devices transmit model data simultaneously using non-orthogonal OFDM subcarriers, and the edge server aggregates data directly from the superimposed signal. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog transmission for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation for phase-asynchronous multi-user OFDM systems. To realize a digital AirComp system, we develop a medium access control (MAC) protocol that allows simultaneous transmissions from different users using non-orthogonal OFDM subcarriers, and put forth joint channel decoding and aggregation decoders tailored for convolutional and LDPC codes. To verify the proposed system design, we build a digital AirComp prototype on the USRP software-defined radio platform, and demonstrate a real-time LDPC-coded AirComp system with up to four users. Trace-driven simulation results on test accuracy versus SNR show that: 1) analog AirComp is sensitive to phase asynchrony in practical multi-user OFDM systems, and the test accuracy performance fails to improve even at high SNRs; 2) our digital AirComp system outperforms two analog AirComp systems at all SNRs, and approaches the optimal performance when SNR$\geq$6 dB for two-user LDPC-coded AirComp, demonstrating the advantage of digital AirComp in phase-asynchronous multi-user OFDM systems.

This paper presents a Long Range (LoRa) data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes’ data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

In this paper, we show that by capturing the causal relationship among the computation of routers, one can transform the distributed program composed of routing processes into a sequential program, which allows the use of various sequential program analysis theories and tools for diagnosing and repairing routing configuration errors. This insight sheds light on future research on automatic network configuration diagnosis and repair. To demonstrate its feasibility and generality, we give the preliminary design of two methods for routing configuration error diagnosis: (1) data flow analysis using minimal unsatisfiable core and error invariants; and (2) control flow analysis using selective symbolic execution. Using real-world topologies and synthetic configurations, we show that both methods can effectively find errors in routing configurations while incurring reasonable overhead.

This paper presents Deep Reinforcement Learning (DRL)-based channel allocation algorithms for Wi-Fi networks with channel bonding capability. In particular, the proposed DRL algorithms allocate the primary channel and the maximal bonding bandwidth for each access point (AP). Existing DRL-based channel allocation algorithms assume a pre-known static interference model between APs, which cannot be accurately obtained in the hidden terminal scenario and the hidden channel scenario where APs have different sensing capabilities depending on the used channels. In contrast, our proposed DRL algorithm leverages the observed throughput as a reward to learn the interference relationship automatically, and implement centralized and distributed algorithms based on Proximal Policy Optimization (PPO) to learn and optimize channel allocation policies for improved performance. Simulation results show that the proposed methods outperform traditional methods in terms of network throughput in scenarios with hidden terminals and channels, and also perform well in scenarios with dynamic traffic loads. The proposed algorithms are more suitable for practical applications since no prior system knowledge is required.

This paper presents the first broadband digital over-the-air computation (AirComp) system for phase asynchronous OFDM-based federated edge learning systems. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog modulation for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation in the asynchronous multi-user OFDM systems. To realize a digital AirComp system, we propose a non-orthogonal multiple access protocol that allows simultaneous transmissions from multiple edge devices, and present a full-state joint channel decoding and aggregation (Jt-CDA) decoder. To reduce the computation complexity, we further present a reduced-complexity Jt-CDA decoder, and its arithmetic sum bit error rate performance is similar to that of the full-state joint decoder for most signal-to-noise ratio (SNR) regimes. Simulation results on test accuracy of CIFAR10 dataset versus SNR show that: 1) analog AirComp systems are sensitive to phase asynchrony under practical setup, and the test accuracy performance exhibits an error floor even at high SNR regime; 2) our digital AirComp system outperforms an analog AirComp system by at least 1.5 times when SNR≥9dB, demonstrating the advantage of digital AirComp in asynchronous multi-user OFDM systems.

This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the asynchronous superimposed signal. Different from traditional MPR approaches that are designed for the uncoordinated collision decoding scenario, our MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses the new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol; 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data; and 3) a soft-decision packet decoding algorithm that keeps the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms a state-of-the-art MPR decoder by 5.4× in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms a state-of-the-art MPR scheme by at least 2.1× for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

This paper presents the first network configuration verifier that provides fast all-pair reachability analysis of incremental configuration changes for network overlay data center networks (DCNs). Network overlay DCNs leverage distributed routing protocol on edge leaf switches to disseminate overlay routes and establish overlay tunnels. In addition, network overlay DCNs use access control lists, microsegmentation policy, policy-based routing and firewall policy to control east-west and north-south traffic. Although some incremental verification approaches have been proposed, they either do not support certain forwarding features of the network, or are not efficient. Our configuration verifier addresses these issues through the following components: 1) a port predicate based forwarding model that is general to support all features; 2) fine-grained association technique to index possibly affected reachable pairs by changed interfaces in the original network; and 3) required waypoint path computation that finds all reachable pairs related to changed interfaces in the new network. Based on these components, our verifier presents two incremental verification algorithms that are specially designed for different service update cases. Experiment results show that our incremental verification algorithms are accurate and fast. For all-pair reachability, our verifier performs change-impact analysis within 15s for networks with 200 leafs (4000 subnets and 16 million pairs), outperforming existing approaches by up to 10x.

This paper presents the first network configuration verifier that provides fast all-pair reachability analysis of incremental configuration changes for network overlay data center networks (DCNs). Network overlay DCNs leverage distributed control (i.e., BGP EVPN) on switches to establish VXLAN tunnels, distribute overlay routes and limit traffic access (e.g., microsegmentation). Although some incremental verification techniques have been proposed, they are either not complete, or do not support certain features of the network. Our configuration verifier addresses these issues through the following components: 1) a port-predicate forwarding model that is general to support all features; 2) fine-grained indexing technique to lookup possibly affected reachable pairs by changed interfaces; and 3) required waypoint path computation that finds all reachable pairs related to changed interfaces. Experiment results show that our algorithm is complete and fast. For the studied service updates, our verifier performs all-pair reachability change impact analysis within 25s for networks with 100 leafs (2000 endpoints and 4 mill. pairs), outperforming existing approaches by up to 8x.

This paper presents the first reliable physical-layer network coding (PNC) system that supports real TCP/IP applications for the two-way relay network (TWRN). Theoretically, PNC could boost the throughput of TWRN by a factor of 2 compared with traditional scheduling (TS) in the high signal-to-noise (SNR) regime. Although there have been many theoretical studies on PNC performance, there have been relatively few experimental and implementation efforts. Our earlier PNC prototype, built in 2012, was an offline system that processed signals offline. For a system that supports real applications, signals must be processed online in real-time. Our real-time reliable PNC prototype, referred to as RPNC, solves a number of key challenges to enable the support of real TCP/IP applications. The enabling components include: 1) a time-slotted system that achieves μs-level synchronization for the PNC system; 2) reduction of PNC signal processing complexity to meet real-time constraints; 3) an ARQ design tailored for PNC to ensure reliable packet delivery; and 4) an interface to the application layer. We took on the challenge to implement all of the above with general-purpose processors in PC through an SDR platform rather than ASIC or FPGA. With all of these components, we have successfully demonstrated image exchange with TCP and two-party video conferencing with UDP over RPNC. Experimental results show that the achieved throughput approaches the PHY-layer data rate at high SNR, demonstrating the high efficiency of the RPNC system.

Physical-layer network coding (PNC) can potentially boost the throughput of a two-way relay network by 100% compared with conventional packet forwarding schemes. However, the complexity of PNC channel decoders can be considerably higher than the complexity of channel decoders for point-to-point communication systems. Although many PNC channel decoders proposed to date have good decoding performance, they may not be feasibly implemented in practical systems due to their high computation complexities. This paper presents a reduced-complexity decoder (RCD), a PNC decoder with adjustable decoding complexity that is amenable to real-time implementation. We experimentally evaluate the performance-complexity trade-off of RCD. Our experimental results show that RCD can achieve substantial throughput gain over state-of-the-art decoders proposed for real-time PNC systems.

Recent studies show that Wi-Fi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or Wi-Fi nodes. However, massive deployment of ZigBee nodes and uncooperative Wi-Fi users call for innovative cross-technology coexistence without intervening legacy systems. In this work, we investigate the Wi-Fi and ZigBee coexistence when ZigBee is the interested signal. Typically, the duration of transmitting a ZigBee data packet is longer than that of a Wi-Fi packet. Mitigating short duration Wi-Fi interference (calledflash) in long duration ZigBee data (calledsmog) is challenging. To address these challenges, we propose ZIMO: a sink-based MIMO design for harmony coexistence of ZigBee and Wi-Fi networks with the goal of protecting the ZigBee data packets from being interfered by high-power cross-technology signals. The key insight is to properly exploit opportunities resulted from differences between Wi-Fi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of Wi-Fi and ZigBee will enhance other coexistence technologies such as TIMO[1]. We implement a prototype in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve ZigBee network throughput up to 1.9$\times$, with 1.5$\times$in media, and 1.1$\times$to 1.9$\times$for Wi-Fi network as byproduct in ZigBee signal recovery.

With the proliferation of smart wireless devices in our homes, the cross-technology interference increasingly becomes an important issue. This paper presents a novel smart relay system, called SpaceHub, which leverages an multi-antenna relay node to mitigate cross-technology interference for all communicating devices which may only have single antenna. In SpaceHub, the relay node overhears wireless communications in the air, separates the collided signals, and forwards the separated (cleaned) signals to their intended receivers without a prior knowledge of the wireless signal structures. The core component of SpaceHub is a blind signal separator that constructs spatial filters using the angle-of-arrival information of collided signals. We have implemented SpaceHub on a software radio platform and our evaluation shows SpaceHub signal separator can suppress the interference up to 23dB, and is robust against the power or relative locations of interfering signals.

Slotted ALOHA can benefit from physical-layer network coding (PNC) by decoding one or multiple linear combinations of the packets simultaneously transmitted in a timeslot, forming a system of linear equations. Different systems of linear equations are recovered in different timeslots. A message decoder then recovers the original packets of all the users by jointly solving multiple systems of linear equations obtained over different timeslots. We propose the batched BP decoding algorithm that combines belief propagation (BP) and local Gaussian elimination. Compared with pure Gaussian elimination decoding, our algorithm reduces the decoding complexity from cubic to linear function of the number of users. Compared with the ordinary BP decoding algorithm for low-density generator-matrix codes, our algorithm has better performance and the same order of computational complexity. We analyze the performance of the batched BP decoding algorithm by generalizing the tree-based approach and provide an approach to optimize the system performance.

This paper presents a first real-time network-coded multiple access (NCMA) system that jointly exploits physical (PHY)-layer network coding (PNC) and multiuser decoding (MUD) to boost the throughput of a wireless local area network (WLAN). NCMA is a new design paradigm for multipacket reception wireless networks, in which the access point can receive and decode several packets simultaneously transmitted by multiple users. Conventionally, multipacket reception is realized using MUD only, whereas the key idea of NCMA is to use PNC together with MUD to realize multipacket reception. Although the feasibility of NCMA has previously been studied by the authors, our previous NCMA prototype was a version with offline signal processing. In addition, our previous investigation left open a number of theoretical and implementation issues, the resolution of which is critical to the adoption of NCMA in real practice. The current investigation makes the following state-of-the-art contributions toward NCMA: 1) we demonstrate a first NCMA system with integrated real-time PHY and MAC-layer decoding; 2) we construct a new unified framework for MAC-layer decoding that yields higher throughput with faster decoding-the faster decoding is one of the key enablers of our real-time implementation; and 3) we design new PHY-layer decoding techniques that overcome the poor performance of the first-generation NCMA prototype at low SNR. Experimental results show that, compared with the previous NCMA prototype, our new NCMA prototype improves real-time throughput by more than 100% at medium-high SNR (≥ 8 dB).

Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to 1.9x, with median throughput gain of 1.2x.

We propose a low-complexity digital fountain approach for network-coded multiple access (NCMA), where each source node encodes its input packets using a fountain code. In NCMA, both physical-layer network coding and multiuser decoding are employed in the physical layer of the sink node, so that the output of the physical layer is the coupling of the fountain codes employed at the source nodes. We demonstrate that a belief propagation (BP) decoding algorithm can effectively decode the coupled fountain codes to recover the input packets of all source nodes. Our approach significantly reduces the decoding complexity compared with the previous NCMA schemes based on Reed-Solomon codes and random linear codes, and hence has the potential to increase throughput and decrease delay in computation-limited NCMA systems.

This paper proposes and experimentally demonstrates a first wireless local area network (WLAN) system that jointly exploits physical-layer network coding (PNC) and multiuser decoding (MUD) to boost system throughput. We refer to this multiple access mode as network-coded multiple access (NCMA). Prior studies on PNC mostly focused on relay networks. NCMA is the first realized multiple access scheme that establishes the usefulness of PNC in a non-relay setting. NCMA allows multiple nodes to transmit simultaneously to the access point (AP) to boost throughput. In the non-relay setting, when two nodes A and B transmit to the AP simultaneously, the AP aims to obtain both packet A and packet B rather than their network-coded packet. An interesting question is whether network coding, specifically PNC which extracts packet A ⊕ B, can still be useful in such a setting. We provide an affirmative answer to this question with a novel two-layer decoding approach amenable to real-time implementation. Our USRP prototype indicates that NCMA can boost throughput by 100 percent in the medium-high SNR regime (≥10 dB). We believe further throughput enhancement is possible by allowing more than two users to transmit together.

Recent studies show that WiFi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncooperative WiFi users call for innovative cross-technology coexistence without intervening legacy systems. In this work we investigate the WiFi and ZigBee coexistence when ZigBee is the interested signal.Mitigating short duration WiFi interference (called flash) in long duration ZigBee data (called smog) is challenging, especially when we cannot modify the WiFi APs and the massively deployed sensor nodes. To address these challenges, we propose ZIMO, a sink-based MIMO design for harmony coexistence of ZigBee and WiFi networks with the goal of protecting the ZigBee data packets.The key insight of ZIMO is to properly exploit opportunities resulted from differences between WiFi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of WiFi and ZigBee will enhance other coexistence technologies such as TIMO [1]. We implement a prototype for ZIMO in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve up to 1.9x throughput for ZigBee network, with median gain of 1.5x, and 1.1x to 1.9x for WiFi network as byproduct in ZigBee signal recovery.

We study the time duration of neighbor discovery in peer-to-peer wireless networks with multi-channel multi-packet reception capability. Radios with this capability, at any time slot, can either transmit messages in one channel or receive messages from all channels. This capability is provided by emerging CDMA/OFDMA techniques. Neighbor discovery in this scenario is different from other multi-packet reception scenarios like MIMO since collision can still happen in single channel. To discover neighbors with such capability in single-hop networks, we prove that the expected running time of all randomized algorithms is Θ(n/k) where n is the number of nodes and k is the number of channels. We provide a Las Vegas algorithm that finds all neighbors with variable running time. We prove that its running time is Θ(n/k ln n) with high probability. We also give a Monte Carlo algorithm that terminates in Θ(n/k) time. We show that it finds all neighbors with high probability. Both algorithms are validated by simulation.

We propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N·k) for a network with N channels and k secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus non-stochastic), it results in a total regret uniformly upper bounded by Θ(√TN logN), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

The ability of discovering neighboring nodes, namely neighbor discovery, is essential for the self-organization of wireless ad hoc networks. In this paper, we propose a history-aware adaptive back off algorithm for neighbor discovery assuming collision detection and feedback mechanisms. Given successful discovery feedback, undiscovered nodes can adjust their contention window. With collision feedback and historical information, only transmission nodes enter the re-contention process, and decrease their contention window to accelerate neighbor discovery process after collision. Then, we give theoretical analysis of our algorithm on the discovery time and energy consumption, and derive the optimal size of contention windows by two rounds of optimization. Finally, we validate our theoretical analysis by simulations, and show the performance improvement over existing algorithms.

Neighbor discovery is an essential step for the self-organization of wireless sensor networks. Many algorithms have been proposed for efficient neighbor discovery. However, most of those algorithms need nodes to keep active during the process of neighbor discovery, which might be difficult for low-duty-cycle wireless sensor networks in many real deployments. In this paper, we investigate the problem of neighbor discovery in low-duty-cycle wireless sensor networks. We give an ALOHA-like algorithm and analyze the expected time to discover all n - 1 neighbors for each node. By reducing the analysis to the classical K Coupon Collector's Problem, we show that the upper bound is ne(log2n + (3 log2n - 1) log2log2n + c) with high probability, for some constant c, where e is the base of natural logarithm. Furthermore, not knowing number of neighbors leads to no more than a factor of two slowdown in the algorithm performance. Then, we validate our theoretical results by extensive simulations, and explore the performance of different algorithms in duty-cycle and non-duty-cycle networks. Finally, we apply our approach to analyze the scenario of unreliable links in low-duty-cycle wireless sensor networks.

Cyber-Physical Systems require the integration of various heterogeneous networks. To evaluate proposed algorithms in network research, real-world test beds are an indispensable complement to simulations. In this paper, we present a flexible heterogeneous mesh networking platform (FHMESH) which interconnects various heterogeneous networks, i.e. wireless sensor network, wireless mesh network, FM radio network and the Internet. FHMESH builds agile and universal gateways using software defined radio technology, and the gateways form mesh backbone to interconnect those heterogeneous networks. Then, we present the platform architecture and the specific implementation details for FHMESH, and carry out some preliminary experiments. Even though the testing results of our system are not so perfect, we believe that the integration of various heterogeneous networks makes FHMESH work as a prototype for heterogeneous network research, and the flexible and universal gateway can facilitate the cross-layer research.