This paper investigates distributed link selection (LS) for a multi-hop buffer-aided relay network consisting of one source, one destination, and multiple relays, where each node has access only to local instantaneous channel state information (CSI) and the buffer status of its adjacent nodes. In particular, by improving the alternate-transmission strategy, we propose a novel flexible distributed LS scheme that flexibly selects odd- and even-numbered links in each time slot. Notably, acquisition of local buffer-state information is integrated into the distributed LS agreement process, so it incurs no extra signaling overhead. We also derive the average throughput and packet delay of the proposed scheme by constructing a two-layer Markov model and enumerating all feasible buffer-state transitions. In addition, a simplified expression and an asymptotic analysis are provided to give further insight into the average throughput. Theoretical analysis and simulations show that the proposed flexible scheme substantially outperforms a baseline alternate scheme and closely approaches the performance of a related centralized LS scheme.

Linear feedback coding scheme, such as the elegant Schalkwijk-Kailath (SK) scheme, receives much attention in the literature since its decoding error probability decreases as a second-order exponential in the coding blocklength. In recent years, a linear feedback scheme has been proposed for the Gaussian relay channel (GRC) with destination-source feedback, which combines the SK scheme and the amplify-and-forward (AF) relay strategy. Since there exists three possible feedback links in the GRC, then one question beckons: is there any rate gain if there exist multiple feedback links in the GRC, and can any other relay strategy outperform the AF strategy? In this paper, we answer this question by investigating four feedback models of the GRC, namely, the GRC with destination-source and destination-relay feedback, the GRC with destination-relay and relay-source feedback, the GRC with destination-source and relay-source feedback, and the GRC with all feedback links, respectively. We propose SK-type schemes for these feedback models, and numerical examples show that when the coding blocklength is not long, the rates of our proposed schemes almost approach their asymptotic values, and these rates may be larger than those of existing schemes in the literature. The study of this paper shows that different number/location of feedback links may bring additional rate gain in finite blocklength regime.

This paper presents a general framework for probabilistic relay selection (RS) in asymmetric buffer-aided cooperative relaying systems, which caters to scenarios with both perfect and imperfect channel state information (CSI) during the RS process. The framework extends and generalizes many existing buffer-aided RS schemes. In particular, we introduce an auxiliary stochastic process which assigns varying selection probabilities to different links, considering the dynamic wireless channel and buffer states. Subsequently, we leverage the obtained outage probability and average packet delay (APD) to formulate outage optimization problems while adhering to APD. To address the intricate high-dimensional optimization problems, we employ a deep learning (DL) approach, which involves designing probability mass functions for the auxiliary stochastic process and developing an effective loss function to update the neural network. Simulation results unequivocally demonstrate the superior performance of the proposed DL-based probabilistic RS scheme compared to benchmark schemes, particularly in scenarios involving imperfect CSI.

In this paper, we propose stacking codes and decoding algorithms to correct burst deletion/insertion and random substitution errors in a non-binary segment-Lmaxburst-Ddeletion and maxburst-Sinsertion (NB-SBDI(L,D,S)) channel without knowing the codeword boundaries. Specifically, in the NB-SBDI(L,D,S) channel, at most a single non-binary burst (a block of consecutive bits/symbols) of deletions or insertions of length up toDorS, respectively, exists in a block ofLconsecutive non-binary symbols. The proposed stacking-codes are constructed by stacking multiple Reed-Muller(RM)/modified BCH/modified binary representation of Reed-Solomon (BRRS) codes, respectively, and no marker codes are involved. We show that, in the NB-SBDI(L,D,S) channel, the proposed approach can not only correct both burst deletions/insertions and random substitution errors, but also achieve error-free decoding for stacking-RM/modified stacking-BCH/modified stacking-BRRS codes for burst deletions/insertions as long as no substitution errors exist. Also, the modified stacking-BRRS code is more rate efficient than the existing Song code [1], the stacking-RM/modified stacking-BCH codes are more rate efficient than the Song code if max(D,S) and code length are small.

Linear feedback coding scheme, such as the elegant Schalkwijk-Kailath (SK) scheme, receives much attention in the literature since its decoding error probability decreases as a second-order exponential in the coding blocklength. In recent years, a linear feedback scheme has been proposed for the Gaussian relay channel (GRC) with destination-source feedback, which combines the SK scheme and the amplify-and-forward (AF) relay strategy. Since there exists three possible feedback links in the GRC, then one question beckons: is there any rate gain if there exist two feedback links in the GRC, and can any other relay strategy outperform the AF strategy? In this paper, we answer this question by investigating two feedback models of the GRC, namely, the GRC with destination-source and destination-relay feedback, and the GRC with destination-relay and relay-source feedback. We propose two SK-type schemes which are respectively based on the decode-and-forward (DF) relay strategy and the compress-and-forward (CF) relay strategy. Numerical examples show that the achievable rates of our proposed schemes may be larger than that of the AF relay strategy based SK-type scheme in the literature.

In this paper, the sum-capacity of the symmetric finite-state Gaussian multiple access channel (FS-GMAC) with delayed feedback is determined, where the channel state is perfectly obtained by the receiver, and the receiver sends the state and channel output back to the two transmitters through two noiseless feedback channel after same time delay. We first propose a linear-feedback coding scheme for this model, which is based on multiplexing coding and a variant of Schalkwijk- Kailath scheme, and derive the corresponding achievable sum-rate. Then, by establishing an upper bound on the sum-capacity of the same model, we further show that the proposed scheme is capacity-achieving.

Reliably reconstructing a function of independent Gaussian sources over a white Gaussian multiple-access channel (GMAC) is reconsidered by exploring channel feedback. First, an efficient linear feedback scheme is proposed to compute arbitrary linear functions of sources over GMAC in the presence of noiseless feedback, which is based on a variation of the well-known Schalkwijk-Kailath type scheme. Next, we extend our proposed scheme to the noisy feedback case (the feedback channel is a white Gaussian channel) by introducing modulo lattice function and dither signal into the original scheme. Finally, the results of this paper are further explained by numerical examples, and it is shown that our proposed schemes outperform the existing ones in some cases.

The notable success of large language models (LLMs) has sparked an upsurge in building language agents to complete various complex tasks. We present AMOR, an agent framework based on open-source LLMs, which reasons with external knowledge bases and adapts to specific domains through human supervision to the reasoning process. AMOR builds reasoning logic over a finite state machine (FSM) that solves problems through autonomous executions and transitions over disentangled modules. This allows humans to provide direct feedback to the individual modules, and thus naturally forms process supervision. Based on this reasoning and feedback framework, we develop AMOR through two-stage fine-tuning: warm-up and adaptation. The former fine-tunes the LLM with examples automatically constructed from various public datasets, enabling AMOR to generalize across different knowledge environments, while the latter tailors AMOR to specific domains using process feedback. Extensive experiments across multiple domains demonstrate the advantage of AMOR to strong baselines, thanks to its FSM-based reasoning and process feedback mechanism. The code and data are publicly available at https://github.com/JianGuanTHU/AMOR.

This paper investigates the cooperative jamming based key generation scheme in a cooperative wireless network, where Alice and Bob aim to generate a secret key (SK) that is secret from Eve and a private key (PK) that is secret from both the relay and Eve, with the assistance of a relay. The correlation between legitimate channels and eavesdropping channels is considered. The SK and PK rates are evaluated based on minimum mean square error (MMSE) and zero forcing (ZF) estimation methods, respectively. The analytical expressions are further simplified with asymptotic forms in the high signal-to-noise ratio (SNR) regime. We demonstrate that the MMSE method is optimal to estimate channels, and the ZF method is asymptotically optimal in the high SNR regime. Moreover, we prove that the double-hop key generation strategy cannot achieve a higher rate than the single-hop strategy. Finally, numerical results validate the analysis and demonstrate that the cooperative jamming based key generation scheme significantly outperforms the traditional pairwise key generation scheme.

This paper investigates the problem of secure transmission over a two-user discrete memoryless multiple-access wiretap channel with partial rate-limited feedback (MAC-WT-PLF). The receiver can causally and securely transmit feedback to one of the transmitters at a limited rate. Three achievable rate regions and one outer bound on the secrecy capacity are presented based on three proposed coding schemes and the Sato-type bounding approach. The proposed coding schemes show that the feedback can play multiple roles, i.e., encrypting part of messages, enlarging the size of the dummy message, and increasing the correlation between the channel inputs, to enhance the secrecy performance. Of particular interest is identifying the novel role of enlarging the size of the dummy message at one of the transmitters, which enables both transmitters to benefit from the feedback significantly. In addition, the proposed achievable rate regions and outer bound are computed for the Gaussian MAC-WT-PLF, and comparative numerical results are provided under different eavesdropping cases.

This paper investigates the performance of key generation between two nodes assisted by a relay in the presence of correlated eavesdropping channels. A cooperative jamming scheme is utilized to impose superimposed channel measurements on the relay and eavesdropper. Both lower and upper bounds on key capacities for both secret key (SK) and private key (PK) generation are evaluated, where the lower bounds are derived by using minimum mean square error and zero forcing methods for channel estimation, and the upper bounds are derived by formulating several enhanced discrete memoryless source (DMS) models. The analytical expressions are further simplified in the high signal-to-noise ratio (SNR) regime. We discover that one of the two legitimate channels should specialize in playing a role of jamming the relay or eavesdropper. We also demonstrate that the derived lower and upper bounds are tight when the eavesdropping channels are lowly or highly correlated. When the eavesdropping channels are uncorrelated, the SK and PK capacities can be determined since the corresponding upper and lower bounds are equal. Moreover, at high SNRs, a constant gap exists between the SK/PK upper and lower bounds as the correlation coefficient becomes one.

In this paper, we investigate the fairness of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) for eliminating the interference of the sensing signal before decoding the signals of communication users. We formulate the problem of maximizing the fairness between communication users and the sensing target by jointly designing the transmit beamforming vectors of the base station (BS) and the coefficient matrices of the STAR-RIS. For tackling the challenging optimization problem, a low-complexity algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP) techniques is proposed for obtaining the transmit beamforming vectors and the STAR-RIS coefficient matrices. For the ISAC system with a single user, we further derive the closed-form expression of the BS transmit beamforming vector for reducing the complexity of the algorithm. Then, the non-convex optimization problem of the STAR-RIS coefficient matrices can be solved efficiently by transforming it into a convex problem. Simulation results show that the fairness of the proposed STAR-RIS-NOMA assisted ISAC system outperforms the conventional RIS-NOMA assisted ISAC system and the conventional RIS and orthogonal multiple access (RIS-OMA) assisted ISAC system.

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search.

Buffer-aided relay networks provide more reliability and coverage in future wireless communications. Therefore, this paper investigates a buffer-aided cooperative relaying system with$K$relays and a direct link from the source to the destination, providing a general scenario different from other existing state-of-the-art techniques. In particular, we propose a novel link selection scheme, which adaptively coordinates the selection priorities of the direct and cooperative relay link according to the instantaneous buffer state. The performance of the proposed link selection scheme is analyzed, in terms of outage probability, average packet delay (APD) and diversity order by providing closed-form expressions. For asymptotic analysis, a theoretical framework is presented by dividing all buffer states into different sets, which verifies that the minimum buffer size is just two for achieving the full diversity order of$2K+1$. We also provide the relationship between the asymptotic APD and diversity order by adjusting predefined target queue lengths, which shows that the diversity order ranges from$K+1$to$2K+1$as the asymptotic APD ranges from 0 to$K$time slots per packet. Both theoretical and simulation results demonstrate that direct transmission significantly improves the outage and delay performance simultaneously.

In this paper, we study the simultaneous wireless information and power transfer (SWIPT) cooperative system, where one source forwards information to one destination with the assistance of multiple relays. Each relay is equipped with a finite data butter and a finite energy butter storing the harvested energy by radio-frequency (RF). An optimization problem is formulated for throughput maximization of the SWIPT cooperative system, taking into consideration the strict delay constraint, dynamic channel conditions, time-varying discrete data butter states and time-varying continuous energy butter states. A discrete-time Markov decision process (MDP) is adopted to model the relay selection process referring to data butter states and energy butter states. Two deep Q-network (DQN)based methods named invalid action penalty (IAP) and invalid action mask (IAM) are proposed. The simulation results show that the proposed IAM method can achieve better convergence and throughput performance than the IAP method.

This paper investigates joint transmit beampattern and phase shifts optimization techniques for a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) radar in the presence of an eavesdropping target. We propose an optimization technique to maximize the signal-to-interference plus noise ratio (SINR) at the MIMO radar. However, the problem is non-convex due to the non-concavity of the secrecy rate function. To tackle this issue, we apply the block coordinate descent (BCD) algorithm to update the transmit power and the phase shifts of the RIS alternately. Specifically, we utilize the majorization-minimization (MM) algorithm to optimize the phase shifts for a given transmit power and utilize the first-order Taylor expansion to reformulate the problem as a convex problem to optimize the transmit power for a given set of phase shifts. Two transmit beamforming vectors are designed to detect the target and convey information safely to the legitimate receiver. Simulation results show that the RIS-assisted MIMO radar can significantly enhance the SINR compared to an ordinary MIMO radar.

The high spectrum efficiency of nonorthogonal multiple access (NOMA) is attractive to solve the massive number of connections in the Internet of Things (IoT). This article investigates a buffer-aided cooperative NOMA (C-NOMA) system in the IoT, where the intended users are equipped with buffers for cooperation. The direct transmission from the access point to the users and the buffer-aided cooperative transmission between the intended users are coordinated. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the performance of the proposed scheme, in terms of the system outage probability and average delay, is theoretically derived with closed-form expressions. Furthermore, the full diversity order of three is demonstrated to be achieved for each user pair if the buffer size is not less than three, which is larger than conventional nonbuffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system in the IoT.

This paper investigates asynchronous reinforcement learning algorithms for joint buffer-aided relay selection and power allocation in the non-orthogonal-multiple-access (NOMA) relay network. With the hybrid NOMA/OMA transmission, we investigate joint relay selection and power allocation to maximize the throughput with the delay constraint. To solve this complicated high-dimensional optimization problem, we propose two asynchronous reinforcement learning-based schemes: the asynchronous deep Q-Learning network (ADQN)-based scheme and the asynchronous advantage actor-critic (A3C)-based scheme, respectively. The A3C-based scheme achieves better performance and robustness when the action space is large, while the ADQN-based scheme converges faster with a small action space. Moreover, a-prior information is exploited to improve the convergence of the proposed schemes. The simulation results show that the proposed asynchronous learning-based schemes can learn from the environment and achieve good convergence.

In this paper, we investigate a Deep Reinforcement Learning based delay-constrained relay selection for secure buffer aided Cognitive Relay Networks (CRNs). We model the relay selection problem in secure butter-aided CRNs as a Markov Decision Process (MDP) problem, and introduce Deep Q-Learning to solve this MDP problem. In the proposed scheme, delay constraint is considered when the packets arriving at the receiver in CRNs. Moreover, we consider the security of data transmissions in butter-aided CRNs with an eavesdropper which can intercept the signals from the source and relays. Furthermore, we introduce ε-greedy strategy to balance the exploitation and exploration. The result shows compared with Max-Ratio scheme, the proposed scheme enhances the throughput with both delay and security constrained significantly in secure CRNs.

A novel cooperative non-orthogonal multiple access (NOMA) strategy is proposed, where a full-duplex cell-center user acts as a friendly relay to help a cell-edge user. An advanced energy harvesting technology based on simultaneous wireless information and power transfer (SWIPT) is used at the cell-center user to harvest energy used at the cooperative stage. We propose a joint design to optimize the power splitting (PS) ratio and the beamforming vectors. The proposed design aims to maximize energy efficiency (EE) of the system while guaranteeing the minimum required target rate of the cell-edge user and the successful decoding rate at the cell-center user. To make the problem tractable, we use Dinkelbachs method to address the fractional functions and the semidefinite relaxation (SDR) technique to deal with the rank constraint. Then, an iterative algorithm based on successive convex approximation (SCA) is proposed to finally solve the reformulated problem. In order to rich the application of the proposed power allocation algorithm, we extend the proposed strategy to an imperfect channel state information (CSI) mode. S-Procedure is introduced to approximate the channel uncertainties. Numerical results reveal that our proposed iterative algorithms have a good convergence rate. In addition, the proposed strategy offers a significant increase in energy efficiency compared to the existing strategies, especially at the low power region.

In this paper, we consider the design of a new secure transmission scheme for a four-node relay-eavesdropper channel, where user cooperation is used to facilitate secure communications. The key idea of the proposed achievable scheme is to apply the compression relaying concept of noisy network coding (NNC) to the secrecy communication scenario. But different from the original non-secrecy NNC, the compression rate at the relay is adaptively chosen according to the eavesdropping channel. Particularly, helping interference information is injected into the compression codebook at the relay by simply enlarging the compression rate, which is to effectively suppress the eavesdropping channel. Closed-form expressions of the secrecy rates achieved by the proposed NNC-based secure scheme are characterized for both the discrete memoryless and Gaussian relay-eavesdropper channels, where the optimal compression rate and the optimal transmit power at the relay are also determined. The proposed secure scheme can be viewed as a general framework, which naturally combines the NNC compression relaying scheme with the interference-assisted scheme. Analytical and numerical results demonstrate that the proposed secure scheme offers constant performance gains over typical existing cooperative secure schemes.

This paper considers a downlink single-cell non-orthogonal multiple access (NOMA) network, where the base station, which has multiple antennas, broadcasts the mixed multicast and unicast messages to multiple users with a single antenna. We propose two types of power allocation schemes for NOMA networks with multicast-unicast transmission to investigate the impact of performance, namely, cognitive radio inspired NOMA with dynamic quality of service (QoS) at multicast users (CR-NOMA-D-M), and CR-NOMA constrains the dynamic QoS at the unicast user (CR-NOMA-D-U). Based on the proposed schemes, we drive the exact/closed-form expressions for the secrecy outage probability, outage probability, as well as the approximate outage probability at high signal-to-noise ratio to study the diversity gain. Compared with existing works, the derived analytical results show that both proposed schemes cannot only significantly improve the outage performance for multicast users, but also remarkably enhance the diversity gain and the secrecy outage probability for the unicast user. Finally, the theoretical results are validated by the numerical results.

In this paper, the optimal power allocation scheme is investigated for sum rate maximization of non- orthogonal multiple access (NOMA) with adaptive rates and α-fairness. Compared to the existing fairness NOMA models, α-fairness can only utilize a single scalar to achieve different user fairness levels. Specifically, the power allocation problem is first formulated to maximize the instantaneous sum rate with α-fairness, where user rates are adapted according to the instantaneous channel state information (CSI). Then, a simple alternate algorithm is proposed to solve the Karush-Kuhn Tucker (KKT) conditions of the formulated power allocation problem. Analytical results demonstrate that the proposed algorithm converges and yields the optimal solution. Numerical results reveal that, at the same fairness level, NOMA significantly outperforms the conventional orthogonal multiple access (MA).

This paper investigates the optimal power allocation scheme for sum throughput maximization of non-orthogonal multiple access (NOMA) system with α-fairness. In contrast to the existing fairness NOMA models, α-fairness can only utilize a single scalar to achieve different user fairness levels. Two different channel state information at the transmitter (CSIT) assumptions are considered, namely, statistical and perfect CSIT. For statistical CSIT, fixed target data rates are predefined, and the power allocation problem is solved for sum throughput maximization with α-fairness, through characterizing several properties of the optimal power allocation solution. For perfect CSIT, the optimal power allocation is determined to maximize the instantaneous sum rate with α-fairness, where user rates are adapted according to the instantaneous channel state information (CSI). In particular, a simple alternate optimization algorithm is proposed, which is demonstrated to yield the optimal solution. Numerical results reveal that, at the same fairness level, NOMA significantly outperforms the conventional orthogonal multiple access for both the scenarios with statistical and perfect CSIT.

This paper investigates group secret key generation problems for different types of wireless networks, by exploiting physical layer characteristics of wireless channels. A new group key generation strategy with low complexity is proposed, which combines the well-established point-to-point pairwise key generation technique, the multisegment scheme, and the one-time pad. In particular, this group key generation process is studied for three types of communication networks: 1) the three-node network; 2) the multinode ring network; and 3) the multinode mesh network. Three group key generation algorithms are developed for these communication networks, respectively. The analysis shows that the first two algorithms yield optimal group key rates, whereas the third algorithm achieves the optimal multiplexing gain. Next, for the first two types of networks, we address the time allocation problem in the channel estimation step to maximize the group key rates. This non-convex max-min time allocation problem is first reformulated into a series of geometric programming, and then, a single-condensation-method-based iterative algorithm is proposed. Numerical results are also provided to validate the performance of the proposed key generation algorithms and the time allocation algorithm.

This paper studies the problem of simultaneously generating a secret key (SK) and a private key (PK) between Alice and Bob, in a cooperative pairwise-independent network (PIN) with two relays. In the PIN, the pairwise source observed by every pair of terminals is independent of those sources observed by any other pairs. The SK needs to be protected from Eve, while the PK needs to be protected not only from Eve but also from the two relays. Two cooperative SK-PK generation algorithms are proposed: both of them first generate common randomness, based on the well-established pairwise key generation technique and the application of the one-time pad; but then, the two algorithms utilize the XOR operation and a specific random-binning-based SK-PK codebook to generate the expected keys, respectively. The achievable SK-PK rate regions of both the two proposed algorithms are analyzed. Of particular interest is the second algorithm with random-bing based codebook, whose achievable key rate region is demonstrated to be exactly the same as the derived outer bound, a crucial step for establishing the key capacity of this PIN model. Finally, the two proposed SK-PK generation algorithms are extended to a cooperative wireless network, where the correlated source observations are obtained from estimating wireless channels during a training phase.

We study the problem of private key generation in a cooperative pairwise independent network (PIN), with M + 2 terminals (Alice, Bob, and M relays), M ≥ 2. In the PIN, the correlated source observed by every pair of terminals is independent of the sources observed by any other pairs of terminals. Moreover, all terminals can communicate with each other over a public channel, which is also observed by Eve, noiselessly. The objective is to generate a private key between Alice and Bob with the help of the M relays; such a private key needs to be protected not only from Eve but also from all relays. A single-letter expression for the private key capacity of this PIN model is obtained, where the achievability part is established by proposing a random binning (RB)-based key generation algorithm, and the converse part is established by deriving upper bounds of M enhanced source models. Next, we consider a cooperative wireless network and use the estimates of fading channels to generate private keys. It has been shown that the proposed RB key generation algorithm can achieve a multiplexing gain of M - 1, which is an improvement compared with the existing XOR algorithm, whose achievable multiplexing gain is IM/2J.

In this paper, the outage performance of downlink non-orthogonal multiple access (NOMA) is investigated for the case where each user feeds back only one bit of its channel state information (CSI) to the base station. Conventionally, opportunistic one-bit feedback has been used in fading broadcast channels to select only one user for transmission. In contrast, the considered NOMA scheme adopts superposition coding to serve all users simultaneously in order to improve user fairness. A closed-form expression for the common outage probability (COP) is derived, along with the optimal diversity gains under two types of power constraints. Particularly, it is demonstrated that the diversity gain under a long-term power constraint is twice as large as that under a short-term power constraint. Furthermore, we study dynamic power allocation optimization for minimizing the COP, based on one-bit CSI feedback. This problem is challenging, since the objective function is non-convex; however, under the short-term power constraint, we demonstrate that the original problem can be transformed into a set of convex problems. Under the long-term power constraint, an asymptotically optimal solution is obtained for high signal-to-noise ratio.

This paper studies the private key generation of a cooperative pairwise-independent network (PIN) with M + 2 terminals, where Alice and Bob wish to generate a private key under the help of the M relays, M ≥ 2; such a private key needs to be protected not only from Eve but also from individual relays simultaneously. In this PIN, the correlated source observed by every pair of terminals is independent of those sources observed by any other pair of terminals. All the terminals can communicate with each other over a public channel which is also observed by Eve noiselessly. The private key capacity of this PIN model is established, whose lower bound is obtained by proposing a novel random binning (RB) based key generation algorithm, and the upper bound is obtained based on the construction of M enhanced source models. The two bounds are shown to be exactly the same. Then, we consider a cooperative wireless network and use the estimates of fading channels to generate private keys. Compared to the existing XOR-based algorithm whose achievable multiplexing gain is ⌊M/2⌋, the proposed RB-based algorithm is demonstrated to achieve the optimal multiplexing gain M - 1.

This paper considers a general framework of the wiretap channel with helping interference and state information (WT-HI-SI), where a transmitter-receiver pair wishes to keep the message secret from a passive eavesdropper in the presence of an interferer and a random state. The interferer is to help the legitimate transceivers to enhance their security level, and the state information is available at the transmitter, but not at the eavesdropper. For the discrete memoryless WT-HI-SI, an achievable scheme is proposed by combining the noise forward scheme and the double binning coding scheme. Some previously proposed schemes can be viewed as special cases of the proposed scheme. Then, the achievable scheme is applied to two special channels, the Gaussian WT-HI-SI and the Gaussian WT-HI, respectively. For the Gaussian WT-HI-SI, there exists an external random state noncausally available to the transmitter in advance. But for the Gaussian WT-HI, there does not exist any external random state. In this case, we propose a novel achievable scheme that requires the transmitter to artificially generate the random state whose power can be adjusted adaptively according to dynamic channel conditions. Both the analytic and numerical results are provided to demonstrate that the use of the state information can generally improve the secrecy performance. A more important contribution of this paper is that even for the scenario where no external state information is available to the transmitter, the proposed scheme with the artificial state can still achieve a strictly larger secrecy rate in comparison with existing interference assisted schemes.

This paper study information-theoretic security for a four-node relay-eavesdropper channel. We propose a new achievable scheme whose key idea is to combine noisy network coding for relay channels and the interference assisted strategy for wiretap channel with a helping interferer. The corresponding achievable secrecy rate is characterized for both discrete memoryless and Gaussian channels. The previous interference assisted schemes such as noise-forwarding and cooperative jamming are shown to be special cases of the proposed scheme. Moreover, in some very strong eavesdropping case where these interference assisted schemes can only achieve zero secrecy rate, the proposed secrecy scheme can still achieve a positive secrecy rate.

This paper studies the impact of partial encoder cooperation on the secrecy of the multiple access channel (MAC) with an external eavesdropper. In particular, two encoders, connected by two communication links with finite capacities, wish to send secret messages to the common intended decoder in the presence of a passive eavesdropper. The inner and outer bounds on the secrecy capacity are derived for the discrete memoryless channel. The derived inner bound rate region is achievable by combining Willems's coding for the MAC with partially cooperating encoders and Wyner's random binning for the wiretap channel. Then, both the inner and outer bounds are extended to the Gaussian case and the corresponding rate regions are established. Several simple achievable transmission schemes are proposed for the Gaussian channel and the numerical results show that the partial encoder cooperation can increase the achievable rate regions.

Patents are critical for a company to protect its core technologies. Effective patent mining in massive patent databases can provide companies with valuable insights to develop strategies for IP management and marketing. In this paper, we study a novel patent mining problem of automatically discovering core patents (i.e., patents with high novelty and influence in a domain). We address the unique patent vocabulary usage problem, which is not considered in traditional word-based statistical methods, and propose a topic-based temporal mining approach to quantify a patent's novelty and influence. Comprehensive experimental results on real-world patent portfolios show the effectiveness of our method.

A novel cooperative transmission protocol is proposed for multiple access scenarios, where multiple users communicate with a common destination with the assistant of multiple half-duplex relays. The proposed alternative relaying decode-and-forward (ARDF) protocol can achieve the full diversity gain by applying superposition coding at each transmitter, which also utilizes the linear zero-forcing detection at each relay to combat a serious inter-relay interference. Different to the previous uplink protocols, the proposed protocol can exploit the cooperation involving both the relays and sources, which guarantees the proposed scheme to approach the optimal multiple-input singleoutput upper bound even with general inter-relay interference. Analytical and numerical results have been provided to demonstrate the performance of the proposed protocol.

Patenting is one of the most important ways to protect company's core business concepts and proprietary technologies. Analyzing large volume of patent data can uncover the potential competitive or collaborative relations among companies in certain areas, which can provide valuable information to develop strategies for intellectual property (IP), R&D, and marketing. In this paper, we present a novel topic-driven patent analysis and mining system. Instead of merely searching over patent content, we focus on studying the heterogeneous patent network derived from the patent database, which is represented by several types of objects (companies, inventors, and technical content) jointly evolving over time. We design and implement a general topic-driven framework for analyzing and mining the heterogeneous patent network. Specifically, we propose a dynamic probabilistic model to characterize the topical evolution of these objects within the patent network. Based on this modeling framework, we derive several patent analytics tools that can be directly used for IP and R&D strategy planning, including a heterogeneous network co-ranking method, a topic-level competitor evolution analysis algorithm, and a method to summarize the search results. We evaluate the proposed methods on a real-world patent database. The experimental results show that the proposed techniques clearly outperform the corresponding baseline methods.

Though news readers can easily access a large number of news articles from the Internet, they can be overwhelmed by the quantity of information available, making it hard to get a concise, global picture of a news topic. In this paper we propose a novel method to address this problem. Given a set of articles for a given news topic, the proposed method models theme variation through time and identifies the breakpoints, which are time points when decisive changes occur. For each breakpoint, a brief summary is automatically constructed based on articles associated with the particular time point. Summaries are then ordered chronologically to form a timeline overview of the news topic. In this fashion, readers can easily track various news topics efficiently. We have conducted experiments on 15 popular topics in 2010. Empirical experiments show the effectiveness of our approach and its advantages over other approaches.

While various cooperative protocols have been developed for the simple scenario with one source-destination pair, most of them still suffer a significant loss compared with the optimal multiple-input single-output (MISO) upper bound. The diversity-multiplexing tradeoff will be used as the criterion for performance evaluation. In this paper, we propose two new half-duplex decode-forward cooperative transmission protocols, whose performance can approach the optimal MISO bound, and achieve a better diversity-multiplexing tradeoff when compared with existing cooperative protocols, particularly for large multiplexing gains. Firstly, a simple protocol of cooperative transmission is devised by combining opportunistic strategies with non-orthogonal transmission. When the number of relays is large, the proposed opportunistic decode-forward cooperative protocol can approach the optimal MISO upper bound. Due to the inter-relay interference constraint, each relay can only be used once, which limits the achievable diversity gain. Such an observation motivates our second transmission protocol which can further push the performance of cooperative transmission close to the optimal upper bound. Secondly, a relaying protocol is proposed for a four-node network where two multiple-antenna relays alternately forward messages to the destination when they can successfully cancel the inter-relay interference using the zero forcing method. Monte-Carlo simulation has also been provided to demonstrate the performance of both protocols and comparable ones.

To guarantee the quality of component composition is an acute and important problem in component-based software development. This paper proposes a multidimension composition (MDC) model which views component composition from four aspects of time, space, data and state. This model is implemented as two data structure: ISP(internal structural property) hierarchical tree and BP (behavioral property) directed-graph respectively. MDC model solves the limit of traditional hierarchical component model and extends the flexibility of composition granularity.