Recent advances in LLM-based multi-agent systems have demonstrated remarkable capabilities in complex decision-making scenarios such as financial trading and software engineering. However, evaluating each individual agent’s effectiveness and online optimization of underperforming agents remain open challenges. To address these issues, we present HiveMind, a self-adaptive framework designed to optimize LLM multi-agent collaboration through contribution analysis. At its core, HiveMind introduces Contribution-Guided Online Prompt Optimization (CG-OPO), which autonomously refines agent prompts based on their quantified contributions. We first propose the Shapley value as a grounded metric to quantify each agent's contribution, thereby identifying underperforming agents in a principled manner for automated prompt refinement. To overcome the computational complexity of the classical Shapley value, we present DAG-Shapley, a novel and efficient attribution algorithm for Directed Acyclic Graph (DAG)-structured multi-agent workflows that leverages the inherent DAG structure of the agent workflow to axiomatically prune non-viable coalitions. By hierarchically reusing intermediate outputs of agents in the DAG, our method further reduces redundant computations, and achieving substantial cost savings without compromising the theoretical guarantees of Shapley values. Evaluated in a multi-agent stock-trading scenario, HiveMind achieves superior performance compared to static baselines. Notably, DAG-Shapley reduces LLM calls by over 80 percent while maintaining attribution accuracy comparable to full Shapley values, establishing a new standard for efficient credit assignment and enabling scalable, real-world optimization of multi-agent collaboration.

The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data. However, in the context of decentralized data access within Web3.0, it is imperative to cope with efficiency concerns caused by the replication of redundant data, as well as security vulnerabilities caused by data inconsistency. To address these challenges, we develop a Trustworthy Decentralized Cooperative Caching (TDC-Cache) framework for Web3.0 to ensure efficient caching and enhance system resilience against adversarial threats. This framework features a two-layer architecture, wherein the Decentralized Oracle Network (DON) layer serves as a trusted intermediary platform for decentralized caching, bridging the contents from decentralized storage and the content requests from users. In light of the complexity of Web3.0 network topologies and data flows, we propose a Deep Reinforcement Learning-Based Decentralized Caching (DRL-DC) for TDC-Cache to dynamically optimize caching strategies of distributed oracles. Furthermore, we develop a Proof of Cooperative Learning (PoCL) consensus to maintain the consistency of decentralized caching decisions within DON. Experimental results show that, compared with existing approaches, the proposed framework reduces average access latency by 20%, increases the cache hit rate by at most 18%, and improves the average success consensus rate by 10%. Overall, this paper serves as a first foray into the investigation of decentralized caching framework and strategy for Web3.0.

Node importance estimation, a classical problem in network analysis, underpins various web applications. Previous methods either exploit intrinsic topological characteristics, e.g., graph centrality, or leverage additional information, e.g., data heterogeneity, for node feature enhancement. However, these methods follow the supervised learning setting, overlooking the fact that ground-truth node-importance data are usually partially labeled in practice. In this work, we propose the first semi-supervised node importance estimation framework, i.e., EASING, to improve learning quality for unlabeled data in heterogeneous graphs. Different from previous approaches, EASING explicitly captures uncertainty to reflect the confidence of model predictions. To jointly estimate the importance values and uncertainties, EASING incorporates DJE, a deep encoder-decoder neural architecture. DJE introduces distribution modeling for graph nodes, where the distribution representations derive both importance and uncertainty estimates. Additionally, DJE facilitates effective pseudo-label generation for the unlabeled data to enrich the training samples. Based on labeled and pseudo-labeled data, EASING develops effective semi-supervised heteroscedastic learning with the varying node uncertainty regularization. Extensive experiments on three real-world datasets highlight the superior performance of EASING compared to competing methods. Codes are available via https://github.com/yankai-chen/EASING.

A decentralized identity system that can provide users with selfsovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0.The account system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity.However, a disadvantage of such completely anonymous identity system is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain.In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities.This work proposes zkBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems.zkBID links souls (blockchain accounts) to humans (users' personhood credentials) in a one-to-one manner to truly reflect the social relationships and interactions between humans on the blockchain.zkBID conceals the one-to-one relationships between blockchain accounts and users' personhood credentials for privacy protection using zero-knowledge proofs and linkable ring signatures.Thus, with zkBID, the users' blockchain accounts are credibly anonymous.Importantly, zkBID is fully decentralized: all user-related data are generated by users and verified by smart contracts on the blockchain.We implemented zkBID and built a blockchain test network for evaluation purposes.Our tests demonstrate the effectiveness of zkBID and suggest proper ways to configure zkBID system parameters.

Data privacy protection and secure sharing are the main issues faced by smart healthcare IoT systems. In medical uses, patient health information is frequently kept in the cloud, which limits the user’s ability to entirely control their data. Additionally, standard encryption keys do not sufficiently mitigate the risks posed by malicious entities like compromised cloud service providers. To address these issues, blockchain technology, combined with Internet of Medical Things (IoMT) can securely safeguard patient medical records through a peer-to-peer, secure, and collective ledger. Therefore, we propose a novel IoT-driven architecture that leverages blockchain technology to protect patient medical files from tampering and unauthorized access. This architecture integrates patient medical files with blockchain and is enhanced by a combination of Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNN). Utilizing blockchain for the transmission of encrypted data significantly strengthens data security and minimizes the risk of data breaches. The process of generating encryption and decryption keys through a coupled CNN and BiLSTM ensures the robustness and uniqueness of these keys. Additionally, the selection of the best key is performed using the Gradient Descent Optimization Algorithm (GDOA), which demonstrates the effectiveness and efficiency of the encryption and decryption process. We also compare the implementation of our model with existing technologies, assessing its performance based on various metrics, including restoration efficiency, response time, record time, key generation time, encryption time, decryption time, turnaround time, and overall running time. Our proposed method is confirmed to be more effective than current techniques in terms of these performance metrics.

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness.

The widespread adoption of Network Address Translation (NAT) technology has led to a significant number of network end nodes being located in private networks behind NAT devices, impeding direct communication between these nodes. To solve this problem, a technique known as "hole punching" has been devised for NAT traversal to facilitate peer-to-peer communication among end nodes located in distinct private networks. However, as the increasing demands for speed and security in networks, TCP-based hole punching schemes gradually show performance drawbacks. Therefore, we present a QUIC-based hole punching scheme for NAT traversal. Through a comparative analysis of the hole punching time between QUIC-based and TCP-based protocols, we find that the QUIC-based scheme effectively reduces the hole punching time, exhibiting a pronounced advantage in weak network environments. Furthermore, in scenarios where the hole punched connection is disrupted due to factors such as network transitions or NAT timeouts, this paper evaluates two schemes for restoring the connection: QUIC connection migration and re-punching. Our results show that QUIC connection migration for connection restoration saves 2 RTTs compared to QUIC re-punching, and 3 RTTs compared to TCP re-punching, effectively reducing the computational resources consumption for re-punching.

We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier-sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals' transmission opportunities over the course of learning. Considering short-packet machine-type communications, extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.

A critical security issue in the blockchain-aided cooperative spectrum sensing (B-CSS) network is that, blockchain cannot guarantee the reliability of off-chain data source, even though the data has been recorded on the chain. Furthermore, the performance optimization of the B-CSS networks is constrained by an underlying tradeoff between throughput and security. Driven by these issues, we first develop a novel B-CSS framework with a decentralized reputation management (DRM) mechanism, wherein nodes not only collaborate to detect the availability of target spectrum off the chain, but also act as the blockchain nodes to maintain global decisions on the chain. In the off-chain phase, the DRM mechanism can enhance the trustworthiness of CSS by evaluating each node's reputation according to its contribution to the global detection. Furthermore, in light of the on-chain throughput-and-security tradeoff, verifiable reputation can be utilized as the consensus stake to adjust the difficulty level of block generation. Then, given the on-chain reputation consensus, we maximize the average throughput of the proposed framework by jointly optimizing the block size, sensing time, and block generation time. Simulation results demonstrate the optimized performance of the proposed framework. Moreover, compared with the baseline schemes, our proposal is more robust to the threat of malicious attacks such as data-tampering attack and collusion attack.

This work presents Reputation-Based Streamlet (RBStreamlet), an improved version of the Streamlet consensus algorithm, designed to address limitations such as high bandwidth load and high communication complexity during the Propose and Vote phases. RBStreamlet introduces a reputation mechanism for leader election and fork selection, along with erasure coding and Merkle trees to reduce data transmission volume in the Propose phase. Furthermore, RBStreamlet leverages threshold signature technology to decrease the message complexity in the Vote phase. We implement the proposed RBStreamlet algorithm in a prototype blockchain system and build a network emulator to evaluate its performance under conditions resembling a practical wide-area network. Through extensive experiments, RBStreamlet demonstrates superior performance, scalability, and resilience to Byzantine attacks compared to the original Streamlet, especially in bandwidth-limited scenarios. These findings suggest its potential for practical applications in blockchain systems.

Nowadays, deep learning (DL) techniques have found extensive applications in the Internet of Things (IoT) community, such as autonomous driving and medical diagnosis. Despite these successful implementations, elevated data collection expenses, data confidentiality concerns, and privacy issues all contribute to the complexity and costliness of data acquisition in DL-based image processing. Image modification methods based on diffusion models can subtly modify images to generate new images with similar content to the original image but with unique differences, providing a powerful tool for data augmentation. Nonetheless, diffusion models exhibit constraints in cross-modal image modification, such as sensitivity to prompt, model complexity, and user-friendliness. To address these issues, this paper proposes a rapid attention-based multimodal modification model (RAMMM) to facilitate straightforward and efficient text-guided image modification. Our proposed RAMMM primarily enhances the text processing and image generation procedures via an attention mechanism, thus improving the ability in capturing semantic and contextual information within the text. Consequently, RAMMM excels in generating high-quality sample images aligned with the description provided for the modified text. In addition, by utilizing text-guided image modification, RAMMM is capable of generating batches of image samples systematically to augment the dataset size. Evaluation results demonstrate that RAMMM can improve the performance and generalization capabilities of diffusion models by enhancing both the quality and quantity of the dataset. When employed for data augmentation on the CIFAR10 dataset, RAMMM achieves a 1.5% increase in target recognition accuracy.

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.

As the underlying consensus protocol of Bitcoin and Ethereum blockchains, Proof-of-Work (PoW) features a cryptographic mathematical puzzle whose solution is easy to verify but extremely hard to solve. Under PoW, miners maintain the security of blockchain by devoting computing powers to solve the puzzle; the miner who has solved the puzzle successfully generates a block, along with a reward (e.g., a set of cryptocurrency). The average waiting time to generate a block is inversely proportional to the computing power of the miner. To reduce the average block generation time, a group of individual miners can form a centralized mining pool to aggregate their computing power to solve the puzzle together and share the reward contained in the block. However, if the aggregated computing power of the pool forms a substantial portion of the total computing power in the network, the pooled mining undermines the core spirit of blockchain, i.e., the decentralization, and harms its security. To discourage the pooled mining, we develop a new consensus protocol called Proof-of-Age (PoA) that builds upon the native PoW protocol. The core idea of PoA lies in using Age-of-Work (AoW) to measure the effective mining periods that the miners have devoted to maintaining the security of blockchain. Unlike in the native PoW protocol, in our PoA protocol, miners benefit from its effective mining periods even if they have not successfully mined a block. We first employ a continuous time Markov chain (CTMC) to model the block generation process of the PoA based blockchain. Based on this CTMC model, we then analyze the block generation rates of the mining pool and solo miners respectively. Our analytical results verify that under PoA, the block generation rates of miners in the mining pool are reduced compared to that of solo miners, thereby disincentivizing the pooled mining. Finally, we simulate the mining process in the PoA blockchain to demonstrate the consistency of the analytical results.

This paper presents the implementation and experimental evaluation of a short-packet physical-layer network coding (PNC) system. Implementation of short-packet PNC systems is challenging. First, short packets may have only a few pilot symbols for synchronization and channel estimation purposes. Increasing the number of pilots increases the overhead; decreasing the number of pilots, on the other hand, degrades the packet error rate performance. Second, many short-packet systems are meant for applications with very stringent delay requirements. Employing advanced but complex PNC channel decoding may result in unacceptable delay due to the processing delay. This work presents a low-complexity and low-overhead physical-layer design of OFDM-based short-packet PNC systems, implemented over the software-defined radio platform. Our design makes use of only a small number of pilots (without separate OFDM preamble symbols) to address issues such as slot synchronization, packet detection, carrier frequency offsets, and mismatched channel state information. Our design employs reduced-complexity XOR channel decoding based code-aided parameter estimation (that includes synchronization and channel estimation) to compensate for the limitations imposed by having a small number of pilots. This is the first demonstration that provides a practical framework for applying PNC to short-packet communications.

The existing medium access control (MAC) protocol of Wi-Fi networks (i.e., carrier-sense multiple access with collision avoidance (CSMA/CA)) suffers from poor performance in dense deployments due to the increasing number of collisions and long average backoff time in such scenarios. To tackle this issue, we propose an intelligent wireless MAC protocol based on deep learning (DL), referred to as DL-MAC, which significantly improves the spectrum efficiency of Wi-Fi networks. The goal of DL-MAC is to enable not only intelligent channel access but also intelligent rate adaptation. To achieve this goal, we design a deep neural network (DNN) that takes the historical received signal strength indications (RSSIs) as inputs and outputs joint channel access and rate adaptation decision. Notably, the proposed DLMAC takes the constraints of practical applications into account and the DL-MAC is evaluated using the experimental wireless data sampled from the actual environments on the 2. 4GHz frequency band. The experimental results show that our DLMAC can achieve around 86% performance of the global optimal MAC, and about twice the performance of the traditional Wi-Fi MAC in the environments of our lab and the Shenzhen Baoan International Airport departure hall.

This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global$\alpha$-fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly received, so that the agent can optimize its strategy based on the received reward. In our wireless application where the channels are noisy, the feedback/reward (i.e., the ACK packet) may be lost due to channel noise and interference. Without correct feedback, the agent (i.e., the network user) may fail to find a good solution. Moreover, in the distributed protocol, each agent makes decisions on its own. It is a challenge to guarantee that the multiple agents will make coherent decisions and work together to achieve the same objective, particularly in the face of imperfect feedback channels. To tackle the challenge, we put forth (i) a feedback recovery mechanism to recover missing feedback information, and (ii) a two-stage action selection mechanism to aid coherent decision making to reduce transmission collisions among the agents. Extensive simulation results demonstrate the effectiveness of these two mechanisms. Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted.

This paper investigates the application of physical-layer network coding (PNC) to Industrial Internet of Things (IIoT) in which a controller and a robot are out of each other’s transmission range, and they exchange messages with the assistance of a relay. We particularly focus on a scenario where 1) the controller has more information to transmit than the robot; 2) the channel of the controller is stronger than that of the robot, and both users have nearly the same transmit power. To reduce the communication latency, we put forth an asymmetric PNC transmission scheme in which the controller transmits more information than the robot by exploiting its stronger channel gain in the uplink of PNC. However, the current channel-coded PNC requires the two users to transmit the same amount of source information in order to preserve the linearity of the two users’ channel codes at the relay for successful decoding. Therefore, a challenge in the asymmetric PNC transmission scheme is how to construct a channel decoder at the relay, considering that a superimposed symbol at the relay contains different amounts of source information from the controller and robot. To fill this gap, we propose a lattice-based encoding and decoding scheme in which the robot and controller encode and modulate their information in lattices with different lattice construction levels. The network-coded messages are decoded level-by-level in the lattice. Our design is versatile on that the controller and the robot can freely choose their modulation orders based on their channel power, and the design is applicable for arbitrary channel codes, not just for one particular channel code. The simulation results demonstrate the effectiveness of the proposed channel-coded asymmetric PNC transmission scheme.

This study applies reinforcement learning (RL) from the AI machine learning field to derive an optimal Bitcoin-like blockchain mining strategy. A salient feature of the RL learning framework is that an optimal (or near-optimal) strategy can be obtained without knowing the details of the blockchain network model. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining. In particular, the mining problem can be formulated as a Markov Decision Process (MDP) which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model-based solution. In this study, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy. Since the mining MDP problem has a nonlinear objective function (rather than linear functions of standard MDP problems), we design a new multidimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multidimensional RL mining algorithm can still achieve optimal performance over time-varying blockchain networks.

This paper investigates a cache-aided cloud radio access network (C-RAN), comprising a central unit, K base stations (BSs) each with NTantennas, and M users each with NRantennas, where each BS and user have local caches to store some popular contents from the central unit. For this cache-aided network, we propose the linear network coded (NC) wireless caching that consists of linear wireless network coding assisted cache placement phase and signal-space alignment (SSA) enabled content delivery phase. In the cache placement phase, we design a joint NC caching function at the BSs to store linear combinations of messages from the central unit, as a form of linear wireless network coding. In the content delivery phase, we design the SSA pattern based on the NC caching to guide the precoding designs at BSs. Then, each user can reliably decode its requested messages by receiver shaping and reverse NC operation. The primary contribution of this work is to achieve the coding gain induced by the integration of linear wireless network coding and SSA, which has been not exploited in the field of wireless coded caching. In particular, to deal with high temporal variability of user requests, we show that the proposed cache placement is invariant to different user requests in the worst-case caching, without any shared caching messages at different BSs. Furthermore, we verify that the proposed scheme is also compatible with the insufficient caching scenario at the BSs. In addition, we analyze the achievable sum degrees of freedom (DoF) for the proposed caching network. Both analytical and numerical results verify that the proposed caching scheme achieves a higher sum DoF than the existing related works.

This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep-reinforcement learning multiple access (CS-DLMA). The goal of CS-DLMA is to enable efficient and equitable spectrum sharing among a group of co-located heterogeneous wireless networks. Existing CSMA protocols, such as the medium access control (MAC) protocol of WiFi, are designed for a homogeneous network in which all nodes adopt the same protocol. Such protocols suffer from severe performance degradation in a heterogeneous environment where there are nodes adopting other MAC protocols. CS-DLMA aims to circumvent this problem by making use of DRL. In particular, this paper adopts α-fairness as the general objective of CS-DLMA. With α-fairness, CS-DLMA can achieve a range of different objectives (e.g., maximizing sum throughput, achieving proportional fairness, or achieving max-min fairness) when coexisting with other MACs by changing the value of α. A salient feature of CS-DLMA is that it can achieve these objectives without knowing the coexisting MACs through a learning process based on DRL. The underpinning DRL technique in CS-DLMA is deep Q-network (DQN). However, the conventional DQN algorithms are not suitable for CS-DLMA due to their uniform time-step assumption. In CSMA protocols, time steps are non-uniform in that the time duration required for carrier sensing is smaller than the duration of data transmission. This paper introduces a non-uniform time-step formulation of DQN to address this issue. Our simulation results show that CS-DLMA can achieve the general α-fairness objective when coexisting with TDMA, ALOHA, and WiFi protocols by adjusting its own transmission strategy. Interestingly, we also find that CS-DLMA is more Pareto efficient than other CSMA protocols, e.g., p-persistent CSMA, when coexisting with WiFi. Although this paper focuses on the use of our non-uniform time-step DQN formulation in wireless networking, we believe this new DQN formulation can also find use in other domains.

We design and implement Publication Chain (PubChain), a decentralized open-access publication platform built on decentralized and distributed technologies of blockchain and IPFS peer-to-peer file sharing systems. The existing publication platforms have some severe drawbacks. First, instead of promoting widespread knowledge sharing, access to publications on the platforms owned by publishers is often on a fee basis. This drawback of pay wall prevents researchers from "standing on the shoulders of giants". Moreover, the peer review process on most all existing publication platforms (including both openaccess and publisher platforms) is prone to be ineffective, since there is no proper incentive to reviewers for performing high-qualified reviews. PubChain is an alternative platform to the existing publication venues aiming to address their drawbacks. No central third-party owns the contents (i.e., papers and reviews) of PubChain. Exploiting blockchain technology, we devise an elaborate incentive scheme on PubChain to incentivize key stakeholders (i.e., authors, readers and reviewers) to participate publication activities on PubChain in a substantive manner by earning credits and rewards through self-motivated interactions. We have performed simulations to investigate the robustness of our proposed incentive scheme against fraudulent publications and reviews. We also have implemented a prototype of PubChain to demonstrate its key concepts.

This paper explores the application of physical-layer network coding (PNC) for short-packet transmissions. PNC can potentially reduce the communication delay in relay-assisted wireless networks and can thus be instrumental in realizing short-packet communication systems with stringent delay requirements. In this work, first, we first derive an achievability bound for channel-coded short-packet PNC systems. Based on the random-coding error-exponent, the bound serves as a benchmark for short-packet PNC operating with traditional preamble-aided channel estimation and XOR channel decoding. Second, we design a blind channel estimation algorithm and a code-aided channel estimation algorithm for short-packet PNC systems. Both outperform the traditional preamble-aided channel estimation for PNC systems operating with mismatched channel-state-information. As a case study, we compare the three algorithms for packets of 128 symbols over a two-way relay channel. The results show that the blind algorithm outperforms the code-aided algorithm and preamble-aided algorithm by almost 0.2 and 1.5 dB respectively. Furthermore, the blind algorithm achieves the target packet error rate of 10-4within 0.5 dB of the random coding bound of an imaginary system in which perfect channel-state-information is available at the relay at no cost (i.e., channel estimation is not required in the imaginary system). The bound and the algorithms give us a fundamental framework for applying PNC to short-packet transmissions.

This paper derives the maximum achievable sum-rate and presents the optimal encoding/decoding framework for rate-diverse wireless network coding (RD-WNC) over broadband channels consisting of multiple parallel subchannels. RD-WNC applies to a communication scenario in which a base station wants to deliver two different messages with different rates to two users. The base station combines the two separate messages into one network-coded message and broadcasts the network-coded message to both users. Each user then extracts its desired message from the network-coded message by subtracting from it the other message, which we assume to be side information available to the user. Deriving the maximum achievable sum-rate for RD-WNC is challenging when the channel consists of multiple parallel subchannels with different channel coefficients (e.g., the subcarrier channels of OFDM systems), since apart from the rate allocation between the two users, optimal power allocation among multiple subchannels needs to be identified. The first contribution of this paper is a new “mountain-leveling” power allocation algorithm to achieve the maximum sum-rate. With the resulting power allocation, we can then achieve the corresponding optimal sum-rate by having a separate encoding/decoding mechanism for each subchannels, but doing so is cumbersome and complex when the number of subchannels is large. The second contribution of this paper is a practical encoding/decoding framework using only one encoder-decoder mechanism for all subchannels without sacrificing sum-rate optimality. We provide numerical results to corroborate our theoretical findings and to demonstrate the benefits of our encoding/decoding framework.

Sequences play an important role in many applications and systems. Discovering sequences with desired properties has long been an interesting intellectual pursuit. This article puts forth a new paradigm, AlphaSeq, to discover desired sequences algorithmically using deep reinforcement learning (DRL) techniques. AlphaSeq treats the sequence discovery problem as an episodic symbol-filling game, in which a player fills symbols in the vacant positions of a sequence set sequentially during an episode of the game. Each episode ends with a completely filled sequence set, upon which a reward is given based on the desirability of the sequence set. AlphaSeq models the game as a Markov decision process (MDP) and adapts the DRL framework of AlphaGo to solve the MDP. Sequences discovered improve progressively as AlphaSeq, starting as a novice, and learns to become an expert game player through many episodes of game playing. Compared with traditional sequence construction by mathematical tools, AlphaSeq is particularly suitable for problems with complex objectives intractable to mathematical analysis. We demonstrate the searching capabilities of AlphaSeq in two applications: 1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multi-carrier code-division multiple access (CDMA) systems and 2) AlphaSeq discovers new sequences that triple the signal-to-interference ratio-benchmarked against the well-known Legendre sequence-of a mismatched filter (MMF) estimator in pulse compression radar systems.

We propose a deep-learning approach for the joint MIMO detection and channel decoding problem. Conventional MIMO receivers adopt a model-based approach for MIMO detection and channel decoding in linear or iterative manners. However, due to the complex MIMO signal model, the optimal solution to the joint MIMO detection and channel decoding problem (i.e., the maximum likelihood decoding of the transmitted codewords from the received MIMO signals) is computationally infeasible. As a practical measure, the current model-based MIMO receivers all use suboptimal MIMO decoding methods with affordable computational complexities. This work applies the latest advances in deep learning for the design of MIMO receivers. In particular, we leverage deep neural networks (DNN) with supervised training to solve the joint MIMO detection and channel decoding problem. We show that DNN can be trained to give much better decoding performance than conventional MIMO receivers do. Our simulations show that a DNN implementation consisting of seven hidden layers can outperform conventional model-based linear or iterative receivers. This performance improvement points to a new direction for future MIMO receiver design.

This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple Access (DLMA). Specifically, we consider the scenario of a number of networks operating different MAC protocols trying to access the time slots of a common wireless medium. A key challenge in our problem formulation is that we assume our DLMA network does not know the operating principles of the MACs of the other networks-i.e., DLMA does not know how the other MACs make decisions on when to transmit and when not to. The goal of DLMA is to be able to learn an optimal channel access strategy to achieve a certain pre-specified global objective. Possible objectives include maximizing the sum throughput and maximizing α-fairness among all networks. The underpinning learning process of DLMA is based on DRL. With proper definitions of the state space, action space, and rewards in DRL, we show that DLMA can easily maximize the sum throughput by judiciously selecting certain time slots to transmit. Maximizing general α-fairness, however, is beyond the means of the conventional reinforcement learning (RL) framework. We put forth a new multi-dimensional RL framework that enables DLMA to maximize general α-fairness. Our extensive simulation results show that DLMA can maximize sum throughput or achieve proportional fairness (two special classes of α-fairness) when coexisting with TDMA and ALOHA MAC protocols without knowing they are TDMA or ALOHA. Importantly, we show the merit of incorporating the use of neural networks into the RL framework (i.e., why DRL and not just traditional RL): specifically, the use of DRL allows DLMA (i) to learn the optimal strategy with much faster speed and (ii) to be more robust in that it can still learn a near-optimal strategy even when the parameters in the RL framework are not optimally set.

This paper proposes a Gaussian mixture message passing (GMMP) scheme to implement the blind known-interference cancellation (BKIC). Being aware of interference data as a priori information, the BKIC aims at canceling the interference without estimating the interference channel. Since the target signals are represented by continuous real-valued variables, the previous BKIC scheme is constructed as a real-valued belief propagation (RBP) for implementing message passing on the factor graph that represents the corresponding signal model. To implement the RBP-BKIC, the real-valued variables are actually quantized into vectors of discrete values. As such, the quantized RBP-BKIC has some drawbacks: 1) its performance is determined by the quantization step size and 2) it can only be applied to real signaling with 1-D PAM modulations. To overcome these drawbacks, we propose a GMMP scheme for the BKIC. First, we reveal that all messages passing over the factor graph of BKIC systems can be exactly represented by the mixtures of weighted Gaussian probability density functions. Superior to the quantized RBP-BKIC, we further show that the proposed GMMP scheme is an exact and efficient solution to the BKIC. In particular, it can approach performances of point-to-point communication systems with complex QAM modulations at the cost of affordable computational complexities. Moreover, we put forth a message passing framework that combines the GMMP-BKIC and the channel decoding into an iterative message passing scheme.

This paper investigates the use of deep reinforcement learning (DRL) in the design of a "universal" MAC protocol referred to as Deep-reinforcement Learning Multiple Access (DLMA). The design framework is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". While the scope of DARPA SC2 is broad and involves the redesign of PHY, MAC, and Network layers, this paper's focus is narrower and only involves the MAC design. In particular, we consider the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The DRL agents of DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the rewards - in accordance with the DRL algorithmic framework - a DRL agent can learn the optimal MAC strategy for harmonious co-existence with TDMA and ALOHA nodes. In particular, the use of neural networks in DRL (as opposed to traditional reinforcement learning) allows for fast convergence to optimal solutions and robustness against perturbation in hyper- parameter settings, two essential properties for practical deployment of DLMA in real wireless networks.

Applying physical-layer network coding (PNC) to vehicular ad-hoc networks (VANETs) can theoretically boost the network throughput by 100%, thus partially addressing the intermittent node connectivity and short contact time issues caused by high speed vehicle motions. However, the application of OFDM modulated PNC in VANETs faces detrimental effects caused by carrier frequency offsets (CFOs) and time-frequency-selective channels. CFOs may destroy the orthogonality of OFDM subcarriers, resulting in inter-carrier interference (ICI). The CFOs of two transmitters may also be different, and cannot be removed by CFO tracking and equalization at the receiver as in conventional single-user communication even if the CFOs are known. In addition, time-frequency- selective channels due to delay and Doppler spreads are difficult to estimate and non-accurate channel estimations will increase the detection bit error rate (BER). To address the two challenges, this paper proposes an ICI-aware approach that jointly exploits pilot and data for channel estimation and data detection. Specifically, our approach jointly uses the belief propagation (BP) algorithm to mitigate the CFO/ICI effect for data detection, and the expectation maximization (EM) algorithm to accurately estimate the channels. A linear interpolation method and an ICI compensation method are simulated as benchmarks. Simulation results indicate that our approach improves the BER performance compared to the two benchmarks (more than 2 dB SNR gain in most cases), especially in the high SNR regime.

This paper presents the Distributed Cooperative Access Points (DCAP) system that can simultaneously serve multiple clients using cooperative beamforming to increase the capacity of WiFi-type wireless networks. The distributed APs are connected by Ethernet and driven by independent low-cost local oscillators. To facilitate cooperative beamforming, we address three major challenges: the phase synchronization, the channel state information (CSI) measurement, and the user selection. Specifically, we develop 1) a cooperative tracking scheme to track signal phase drifts at symbol level without adding extra hardware complexity; 2) an incremental CSI estimation mechanism that removes the per-frame CSI measurement overhead of previous approaches; and 3) a simple random user selection algorithm that scales the network capacity linearly and delivers over 70 percent performance compared to the optimal but complex greedy algorithm. We implement DCAP on the Sora software radio platform and evaluate it in a wireless network with nine nodes. Experimental results show that the cooperative beamforming is feasible in practice, and our cooperative phase tracking can ensure strict phase alignment (≤ 0.03 radian) among APs during the entire beamforming period (1.2 ms). Otherwise, without tracking, phases may drift by 0.3 radian over merely 600 μs, causing that the symbol SNR decreases as large as 20 dB.

In this paper we propose a new analog beamforming structure by switching on or off each of multiple transmit antennas according to channel state information. The proposed analogue beamforming can significantly reduce the high cost, high power and bulky analogue phase-shifters which are employed in analog massive MIMO systems. On one hand, the high performance low cost commercial switch devices make our architecture easy to implement, saving both system cost and space. On the other hand, our on-off analog beamforming (OABF) can achieve good performance with low complexity algorithms. Specifically, we first propose two SNR-maximization algorithms, which determines the on-off state of each switch under per-antenna power constraint. After that, we theoretically prove our on-off analog beamforming scheme can achieve full system diversity gain and array gain with polynomial complexity. The simple structure of OABF makes the massive MIMO much easier to implement, at a cost of small constant rate loss.

A noncoherent multiuser transmission scheme is proposed for massive multiple-input multiple-output (M-MIMO) systems without explicit channel estimation. In particular, each user uses differential PSK modulation and the receiver employs differential detection. First, we propose a simple user selection scheme to optimize the distributions of power space profile (PSP) of individual users which alleviates the overlap of PSPs. Then, each output stream of the weighing filter is fed into a noncoherent successive interference cancellation (N-SIC) processor, and finally into a soft-input soft-output (SISO) multiple-symbol differential detector(MSDD). Employing the autocorrelation receiver (AcR) and the belief propagation(BP) message passing algorithm, the proposed SISO-MSDD framework can be easily integrated with advanced channel coding. The proposed scheme bears the potential to solve the high channel estimation overhead for conventional coherent M-MIMO systems. Simulation results show that the BER performance can be significantly improved within a few iterations of the proposed scheme.

This paper proposes an encoding/decoding framework for achieving the optimal channel capacities of the two-user broadcast channel where each user (receiver) has the message targeted for the other user (receiver) as side information. Since the link qualities of the channels from the base station to the two users are different, their respective single-user non-broadcast channel capacities are also different. A goal is to simultaneously achieve/approach the single-user non-broadcast channel capacities of the two users with a single broadcast transmission by applying network coding. This is referred to as the rate-diverse wireless network coding problem. For this problem, this paper presents a capacity-achieving framework based on linear-structured nested lattice codes. The significance of the proposed framework, besides its theoretical optimality, is that it suggests a general design principle for linear rate-diverse wireless network coding going beyond the use of lattice codes. We refer to this design principle as the principle of virtual single-user channels. Guided by this design principle, we propose two implementations of our encoding/decoding framework using practical linear codes amenable to decoding with affordable complexities: the first implementation is based on Low Density Lattice Codes (LDLC) and the second implementation is based on Bit-interleaved Coded Modulation (BICM). These two implementations demonstrate the validity and performance advantage of our framework.

This paper studies protograph low-density paritycheck coded cooperation (CC) schemes for two-hop multi-relay systems with L relays over Nakagami-m quasi-static fading (QSF) channels. We propose two CC schemes, namely schemes I and II, with different maximum code rates to satisfy different transmission requirements. We further design a family of distributed rate-compatible root-protograph (RCRP) codes to achieve full diversity in CC-based multi-relay QSF channels. In particular, our RCRP codes with L + 1 sub-codewords can realize full diversity in scheme I, and our RCRP codes with two sub-codewords can achieve full diversity in scheme II with a maximum-ratio combiner. In addition, we estimate the asymptotic word error rate and bit error rate of our RCRP codes using a generalized protograph extrinsic information transfer algorithm, which is able to characterize the error performance of finite-length codewords accurately. Analysis and simulation show that our RCRP codes can achieve outage-limit-approaching performance in both multi-relay CC architectures. This makes the RCRP coding framework extremely attractive for multirelay cooperative communication applications with slow-varying fading.

This paper proposes a belief propagation (BP) message passing algorithm-based joint multiple symbol differential detection (MSDD) and channel decoding scheme for noncoherent differential ultra-wideband impulse radio (UWB-IR) systems. MSDD is an effective means to improving the performance of noncoherent differential UWB-IR systems. To optimize the overall detection and decoding performance, this paper proposes a novel soft-in soft-out (SISO) MSDD scheme for noncoherent differential UWB-IR. We first propose a new sampling mechanism for the noncoherent auto-correlation receiver to sample the received UWB-IR signal. The proposed sampling mechanism can exploit the dependences (imposed by the differential modulation) among data symbols throughout the whole packet. The signal probabilistic model has a hidden Markov chain structure. We use a factor graph to represent this hidden Markov chain. Then, we apply BP message passing algorithm on the factor graph to develop an SISO MSDD scheme, which is easy to integrate with SISO channel decoding to form a joint MSDD and channel decoding scheme. Performance results of bit error rate simulations and EXIT chart analyses indicate the performance advantages of our scheme over the previous MSDD scheme.

This paper addresses the problem of joint channel estimation and channel decoding in physical-layer network coding (PNC) systems. In PNC, multiple users transmit to a relay simultaneously. PNC channel decoding is different from conventional multi-user channel decoding: specifically, the PNC relay aims to decode a network-coded message rather than the individual messages of the users. Although prior work has shown that PNC can significantly improve the throughput of a relay network, the improvement is predicated on the availability of accurate channel estimates. Channel estimation in PNC, however, can be particularly challenging because of 1) the overlapped signals of multiple users; 2) the correlations among data symbols induced by channel coding; and 3) time-varying channels. We combine the expectation-maximization (EM) algorithm and belief propagation (BP) algorithm on a unified factor-graph framework to tackle these challenges. In this framework, channel estimation is performed by an EM subgraph, and channel decoding is performed by a BP subgraph that models a virtual encoder matched to the target of PNC channel decoding. Iterative message passing between these two subgraphs allow the optimal solutions for both to be approached progressively. We present extensive simulation results demonstrating the superiority of our PNC receivers over other PNC receivers.

This paper applies the expectation-maximization (EM) algorithm to address the problem of joint channel estimation and channel decoding in Physical-layer Network Coding (PNC) systems. The use of PNC can significantly improve the throughput of a relay network. The throughput advantage, however, is predicated on the availability of accurate channel estimates. For channel-coded PNC systems, a major challenge is that the maximum a posteriori probability (MAP) channel estimation is nontrivial due to 1) the overlapping of signals from multiple users received at the relay; and 2) the correlations among data symbols introduced by channel coding. In this paper, we show that an EM algorithm implemented on a factor graph framework is well suited to tackle this problem. Through iterative message passing, the channel estimation component and the channel decoding component in the factor graph interact to improve each other's results progressively. Simulation results indicate that just one EM iteration of our algorithm can significantly improve the channel estimation accuracy as well as the BER performance of channel-coded PNC systems.

This paper presents the first implementation of a two-way relay network based on the principle of physical-layer network coding. To date, only a simplified version of physical-layer network coding (PNC), called analog network coding (ANC), has been successfully implemented. The advantage of ANC is that it is simple to implement; the disadvantage, on the other hand, is that the relay amplifies the noise along with the signal before forwarding the signal. PNC systems in which the relay performs XOR or other denoising PNC mappings of the received signal have the potential for significantly better performance. However, their implementation also poses many challenges. For example, the relay must be able to deal with symbol and carrier-phase asynchronies of the simultaneous signals received from the two end nodes, and the relay must perform channel estimation before decoding. We investigate a PNC implementation in the frequency domain, referred to as FPNC, to tackle these challenges. FPNC is based on OFDM. In FPNC, XOR mapping is performed on the OFDM samples in each subcarrier rather than on the samples in the time domain. We implement FPNC on the universal soft radio peripheral (USRP) platform. Our implementation requires only moderate modifications of the packet preamble design of 802.11a/g OFDM PHY. With the help of the cyclic prefix (CP) in OFDM, symbol asynchrony and the multi-path fading effects can be dealt with simultaneously in a similar fashion. Our experimental results show that symbol-synchronous and symbol-asynchronous FPNC have essentially the same BER performance, for both channel-coded and unchannel-coded FPNC.

A physical-layer network coding (PNC) aided two-way relay scheme is proposed for Transmitted-Reference (TR) UWB networks. In particular, a novel noncoherent UWB-PNC detector is investigated for the TR UWB networks. Inheriting the simplicity of the TR-UWB receiver, the proposed PNC detector is based on the autocorrelation receiver (AcR) with simple structure, which effectively suppresses the multi-user interference and harvests the multipath energy. Equipped with the proposed TR UWB-PNC detector, the relay node first detects the bitwise XORed symbol directly from the overlapped information bearing waveforms transmitted from the source nodes, then broadcasts the estimate of the XORed symbol to achieve efficient two-way relay. Simulation results show that, compared with the non-relay, one-way relay and two-way relay with time division multiple access (TDMA) and network coded broadcasting (NCBC), the proposed PNC aided two-way scheme significantly improves both the energy and spectral efficiencies of the TR-UWB networks.

An ultra-high data rate Time Reversal (TR) Multiple-Input Multiple-Output (MIMO) Ultra-Wideband (UWB) communication system with space-time pre-equalizer is proposed. When the symbol duration is set to approach the duration of UWB monocycles, the data rate is close to the limit due to the extremely large bandwidth, resulting in the severe intersymbol interference (ISI). The zero-forcing (ZF)criterion based space-time pre-equalizer presented in this letter eliminates both ISI and multi-stream interference (MSI) caused by spatial multiplexing at the sampling time. With less demand for degree of freedom (the number of antenna) than other existing schemes, the proposed space-time pre-equalizer enables the data rate of TR-MIMO-UWB system to reach the order of Gbps without losing bit error rate (BER) performance.

In this paper, we investigate noncoherent Multiple-Input Multiple-Output (MIMO) ultra-wideband (UWB) systems where the signal is encoded by Differential Space-Time Block Code (DSTBC). Considering the specific signal format of DSTBC-UWB system and employing the property of DSTBC, a noncoherent multiple symbol detection (MSD) scheme is developed by generalized likelihood ratio testing (GLRT) approach. Although the proposed MSD scheme can enhance the performance of DSTBC-UWB system, the complexity of the exhaustive search based MSD exponentially increases with observation window size. To decrease the computational complexity, the original MSD metric is transformed into another equivalent form which can be implemented by sphere decoding (SD) for DSTBC-UWB system. Moreover, a suboptimal Decision-Feedback (DF) based MSD with lower complexity than SD based MSD is proposed to further reduce the computational complexity.

By introducing the leakage concept, the maximum signal-to-leakage-and-noise ratio (SLNR) scheme has been served as a candidate precoding scheme in the advanced long term evolution (LTE-Advanced) communication system. However, the original scheme allocates every user the same transmit power and takes the matched filter to decode the receive signals, which results in the limited bit error rate (BER) performance. With the antenna correlation at BS and the channel estimation error for every user, we design a modified matrix by minimizing the system Mean Square Error (MMSE) after maximizing the SLNR under the total transmit power constraint. At each user, a linear decoder is calculated based on the MMSE criteria in the presence of imperfect channel state information (ICSI). Due to the dynamic power allocation during the parallel data streams and the linear MMSE (LMMSE) receiver, the proposed scheme can mitigate the residual interference induced by ICSI and improve the system BER performance efficiently.

A multi-layer orthogonal block coded transmission scheme is proposed in this paper to enhance the performance of the noncoherent Ultra-Wideband impulse radio (UWB-IR) system. The design employs a novel Multiple Orthogonal Block Coded Modulation (MOBCM), which transmits multiple information bearing orthogonal codewords with an ingenious layered structure. At the receiver side, a correspondent noncoherent multiple codeword detection technique is developed to jointly detect multiple codewords and exploit the high energy efficiency inherent in the MOBCM. Solid performance gain is achieved and our design reduces the general performance gap between noncoherent and coherent receiver for UWB-IR system.

In this paper, we proposed an effective and unified transmit power allocation (TPA) scheme for the Singular Value Decomposition (SVD)-Assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) and uplink (UL) transmissions in the presence of imperfect channel state information (ICSI). The existing power allocation policies for the SVD-Assisted MU-MIMO system, such as equal power allocation (EPA) in the DL and maximum signal-to-noise ratio (MSNR) policy in UL have been developed under the perfect CSI case. However, the EPA scheme doesn't exploit the CSI and ignores the Bit Error Rate (BER) difference among all singular values meanwhile the MSNR method neglects the noise enhancement caused by the decoding operation at Base Station (BS). Aiming at solving these problems, the proposed TPA scheme takes the smallest singular value and the noise enhancement into account and derives an unified expression for DL and UL. Under the ICSI, the proposed scheme can exploit the power allocation operation to mitigate the residual interference induced by ICSI and improve the system BER performance efficiently.

In this paper, we propose a novel transmit preprocessing scheme using Channel Selection (CS) to jointly design PreRake and Precoding for multiple-input multiple-output Ultra-Wideband (MIMO-UWB) communications. The PreRake technique is implemented to exploit the advantage of multipath diversity and can achieve spatial multiplexing based on our proposed CS. The combination of CS and PreRake transforms the UWB frequency selective fading channel into an equivalent flat fading channel and an equivalent channel matrix (ECM) is calculated. CS can construct the ECM as a well-conditioned matrix so that higher data transmission rate can be provided by spatial multiplexing. Then, Precoding method can be introduced to enhance system performance via diagonalizing the ECM. The simple but effective channel inversion (CI) precoding is employed in the proposed transmit preprocessing scheme which we denote as CS-CI-PreRake. Simulations results show the proposed scheme achieves a solid performance.

Channel allocation in cognitive radio networks completely determines the realizability and efficiency of cognitive radio since it is the final step before the cognitive subscribers can use the spectrum holes. In this paper, we consider channel allocation in cognitive radio networks as a resource allocation problem under the circumstance that the allocation of transmission rate, link and transmission power for secondary users are restricted. And considering the impact of primary user activity on available channels originally, we formulate such resource allocation problem as a binary integer optimal programming, and then we design two algorithms to solve this problem and compare their performances.Finally, numerical results show that the proposed channel allocation model and strategies are quite feasible.

In this paper, two novel space-time coded modulation and corresponding noncoherent detection schemes for Ultra-Wideband (UWB) impulse radio system are proposed. An additional orthogonality is introduced to original Orthogonal Space-Time Block Code (OSTBC) for performance enhancement in the proposed two schemes, which is termed as Dual Orthogonal Space-Time Coded Modulation (DOSTCM). The specially designed signal structures from the DOSTCM exploit the advantage of multiantenna system and enable simple but effective noncoherent detection. Simulation results show our schemes achieve outstanding bit error rate (BER) performance.