We consider the secure coded caching problem proposed by Ravindrakumaret. alwhere no user can obtain information about files other than the one requested. We first propose three new schemes for 1) the general case with arbitraryNfiles andKusers; 2) cache sizeM= 1,N= 2 files and arbitraryKusers; and 3) worst-case delivery rateR= 1, arbitraryNfiles andKusers, respectively. Then we derive some new converse results for 1) the general case with arbitraryNfiles andKusers; 2) cache sizeM= 1 with arbitraryNfiles andKusers; 3) worst-case delivery rateR= 1 with arbitraryNfiles andKusers; and 4) cache sizeM∈ [1,K/K-1] withN= 2 files and arbitraryKusers. As a result, we obtain 1) the two exact end-points for the optimal memory-rate tradeoff curve for arbitrary number of users and files; 2) a segment of the optimal memory-rate tradeoff curve, whereM∈ [1,K/K-1], for the case ofN= 2 files and arbitrary number of users; and 3) a multiplicative-gap-10 result, i.e., we show that the proposed achievable schemes achieve a ratio less than 10 with respect to the cut-set bound.

We investigate the demand-private coded caching problem, in whichKusers, each equipped with a cache of sizeM, access a library ofNfiles under a privacy constraint. This constraint requires that no user obtain any information about the demands of others. We first present a new virtual-user-based achievable scheme for arbitrary numbers of users and files, which yields tighter order-optimal guarantees whenN≤KandM≤ 1. Next, we further focus on the caseN≤K. On the achievability side, for cache sizeM∈ [0,N/(K+1)(N−1)], we propose a novel demand-private scheme based on the idea that each user’s decoding process should depend only on their own demand. In terms of converse, we derive a new converse bound that is applicable forN≤Kand arbitraryM. Comparing the proposed achievability and converse, we find the optimal memory-rate tradeoff of the demand-private coded caching problem forM∈ [0,N/(K+1)(N−1)] whereN≤K≤ 2N−2, and the optimal memory-rate tradeoff forM∈ [0,1/K+1] whereK> 2N− 2. Moreover, for the case of 2 files and arbitrary number of users, by deriving another new converse bound, the optimal memory-rate tradeoff is characterized forM∈ [0,2/K] ∪ [2(K-1)/K+1,2]. Finally, we provide the optimal memory-rate tradeoff of the demand-private coded caching problem for 2 files and 3 users under arbitrary cache sizeM.

In this paper, we investigate the intelligent reflecting surface (IRS)/reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system based on sensing mutual information (MI). Specifically, the base station (BS) perceives the sensing target via the reflected sensing signal by the IRS, while communicating with the users simultaneously. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints for all communication users, the transmit power constraint at the BS, and the unit-modulus constraint on the IRS’s passive reflection. We solve this problem under two cases: one simplified case assuming a line-of-sight (LoS) channel between the BS and IRS and no clutter interference to sensing, and the other generalized case considering the Rician fading channel of the BS-IRS link and the presence of clutter interference to sensing. For the first case, we prove that the dedicated sensing beamformer is unnecessary for improving sensing MI and develop a low-complexity iterative algorithm to jointly optimize the BS and IRS active/passive beamformers. Then, for the second case, we propose an alternative iterative algorithm, which can also be applied to the first case, to solve the beamforming design problem under the general setup. Numerical results are provided to validate the performance of the proposed algorithms, as compared to various benchmark schemes.

The dynamic resource allocation between enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) traffic is a challenging problem. While eMBB strives for high data rates using slots as transmission time intervals, URLLC emphasizes reliability and low latency using mini-slots. Puncturing and superposition schemes are introduced by 3GPP. The existing works considering puncturing and/or superposition overlook the frequency-selective fading and consequently neglect the differences in channel state information of resource blocks (RBs) occupying different sub-carriers. Under the frequency-selective fading, it is crucial to perform RB-specific puncturing and/or superposition by determining which specific RBs will be punctured and/or superposed. In this paper, a deep reinforcement learning (DRL)-based dynamic resource slicing scheme on mini-slot-level timescale for eMBB and URLLC traffic under frequency-selective fading is proposed, considering both puncturing and superposition schemes. In particular, an optimization problem is designed to determine the transmission power allocation ratio of each RB in each mini-slot, where the objective is to maximize the comprehensive performance of eMBB users considering data rate satisfaction, fairness and data rate stability simultaneously under URLLC latency and reliability constraint. Simulation results demonstrate that the proposed DRL-based algorithm employing deep Q-network, with lower complexity, achieves near-optimal performance and outperforms benchmark algorithms.

The paper investigates MIMO integrated sensing and communication (ISAC) systems with extended radar clutter interference. Different from previous work considering a point target scatterer, we focus on a more general scenario with an extended target scatterer. We aim to maximize the sensing mutual information (MI) while satisfying constraints on communication quality of service (QoS) and base station (BS) transmit power. The existing algorithm exhibits high computational complexity in the general scenario proposed in this paper. Hence, we propose an efficient low-complexity algorithm that employs a parallel optimization strategy and closed-form solutions to significantly reduce the complexity of each iteration. Moreover, we derive a lower bound on sensing MI, which is a function of transmit beampattern, and provide a sufficient condition for its tightness. Numerical results demonstrate the low complexity and good performance of our proposed algorithm compared to the existing benchmarks and validate the relationship between the lower bound and sensing MI.

To address the issues of single point of failure and malicious attacks faced by existing federated learning-based spec-trum sensing models in cognitive radio, a cooperative spectrum sensing model based on blockchain and federated learning is proposed. A novel consensus mechanism based on Proof of Rapid Aggregation (PoRA) is first introduced, which effectively avoids the block generation delay caused by the mining process. Additionally, an adaptive weight aggregation algorithm is proposed using smart contract to improve accuracy. Experimental results demonstrate that the proposed model achieves efficient and accurate cooperative spectrum sensing while ensuring security.

This paper proposes a novel algorithm to verify entropic vectors with probability mass functions parametrized and generated by neural networks. Given a target vector, we minimize the normalized distance by training a neural network, which reveals the entropic nature of the target, with the underlying distribution obtained accordingly. Empirical results demonstrate improved normalized distances and convergence performances compared with prior works. We also conduct optimizations of Ingleton score and Ingleton violation index, where a new lower bound of Ingleton violation index is obtained. An inner bound of the almost entropic region with four random variables is constructed with the proposed method, presenting the current best inner bound measured by the volume ratio.

In this paper, we investigate reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems based on sensing mutual information (MI). With the deployment of the RIS, the base station (BS) can perceive the sensing target via the reflected sensing link created by the RIS and communicate with users simultaneously, and the channel between the BS and the RIS is considered as a light-of-sight (LoS) channel. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints of different users, power constraint of the BS, and the unit-modulus constraint of the RIS. Although the formulated nonconvex optimization problem with quartic objective function is difficult to solve, we propose an iterative algorithm based on rank-1 projection, majorization-minimization (MM), eigenvalue decomposition (EVD), and convex-concave procedure (CCP) methods. We also prove that the dedicated sensing beamformer can improve the sensing MI unless the BS-users link is blocked by some obstacles. Numerical results demonstrate that our proposed algorithm outperforms the other benchmarks on the performance of sensing MI and beampattern.

As an important component of self-healing technology, outage detection is of great significance for the smooth operation of subsequent fault diagnosis and outage compensation operations. In this paper, we propose an outage detection algorithm that combines a hybrid Generative Adversarial Network (GAN) with an overlap-sensitive Artificial Neural Network (ANN) to solve the data imbalance problem as well as the overlap problem between data classes in outage detection. The proposed algorithm first synthesizes the outage data and adjusts the data distribution by hybrid GAN outage data distribution features. Then, based on the distribution of the samples in the feature space, the K-nearest neighbor algorithm is used to calculate the degree of overlap between the classes of the samples, and the samples are assigned weights accordingly. Finally, the resulting weight set is combined with the calibrated dataset and weighted to train the artificial neural network classifier. Simulation results show that when compared with the existing outage detection algorithm, the proposed algorithm improves the outage detection performance significantly, and can accurately detect multiple classes of outage cells.

The coexistence of Enhanced Mobile Broad-band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services is a common scenario in 5G. While eMBB strives for high data rates using slots as transmission time intervals (TTIs), URLLC emphasizes reliability and low latency using mini-slots. Puncturing scheme is introduced by 3GPP, which means puncturing a set of Resource Blocks (RBs) from ongoing eMBB transmissions and reallocating them for URLLC traffic. In this paper, a DRL-based dynamic resource slicing scheme for eMBB and URLLC traffic considering puncturing is proposed, where the data rate, Quality of Service (QoS) satisfaction and rate stability for eMBB users are simultaneously optimized on mini-slot-level timescale, by employing an improved Deep Q-learning (DQN) algorithm. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms while ensuring the latency and reliability requirements of URLLC and successfully protects eMBB users under adverse conditions.

Reconfigurable intelligent surface (RIS) is a promising candidate technology for the upcoming sixth-generation (6G) communication system for its ability to manipulate the wireless communication environment by controlling the coefficients of reflection elements (REs). However, since the RIS usually consists of a large number of passive REs, the pilot overhead for channel estimation in the RIS-aided system is prohibitively high. In this article, the channel estimation problem for an RIS-aided multiuser multiple-input–single-output (MISO) communication system with clustered users is investigated. First, to describe the correlated feature for RIS–user channels, a beam-domain channel model is developed for RIS–user channels. Then, a pilot reuse strategy is put forward to reduce the pilot overhead and decompose the channel estimation problem into several subproblems. Finally, by leveraging the correlated nature of RIS–user channels, an eigenspace projection (EP) algorithm is proposed to solve each subproblem, respectively. Simulation results show that the proposed EP channel estimation scheme can achieve accurate channel estimation with lower pilot overhead than existing schemes.

We study the problem of distributed multi-user secret sharing (DMUSS), involving a main node, N storage nodes, and K users. Every user has access to the contents of a certain subset of storage nodes and wants to decode an independent secret message. With knowledge of K secret messages, the main node strategically places encoded shares in the storage nodes, ensuring two crucial conditions: (i) each user can recover its own secret message from the storage nodes that it has access to; (ii) each user is unable to acquire any information regarding the collection of$K-1$secret messages for all the other users. The rate of each user is defined as the size of its secret message normalized by the size of a storage node. We characterize the capacity region of the DMUSS problem, which is the closure of the set of all achievable rate tuples that satisfy the correctness and perfect secrecy conditions. The converse proof relies on a bound from the traditional single-secret sharing regime. In the achievability proof, we firstly design the linear decoding functions, based on the fact that each secret message needs to be recovered from a single set of storage nodes. It turns out that the perfect secrecy condition holds if K matrices, whose entries are extracted from the decoding functions, are full rank. We prove that the decoding functions can be constructed explicitly if the rate tuple satisfies the converse and the field size is not less than K. At last, the encoding functions are obtained by solving the system of linear decoding functions, where some shares are equal to the randomness and the other shares are linear combinations of the secret messages and the randomness.

In this paper, we study the problem of the 1-of-2 string oblivious transfer (OT) between Alice and Bob in the presence of a passive eavesdropper Eve. The eavesdropper Eve is not allowed to get any information about the private data of Alice or Bob. When Alice and Bob are honest-but-curious users, we propose a protocol that satisfies 1-private (neither Alice nor Bob colludes with Eve) OT requirements for the binary erasure symmetric broadcast channel, in which the channel provides dependent erasure patterns to Bob and Eve. We find that when the erasure probabilities of the channel are within a certain range, the derived lower and upper bounds on the wiretapped OT capacity meet. Our results generalize and improve upon the results on 1-private wiretapped OT capacity by Mishra et al. Finally, we propose a protocol for a larger class of wiretapped channels and derive a lower bound on the wiretapped OT capacity.

Both the oblivious transfer (OT) problem and the symmetric private information retrieval (SPIR) problem studies the scenario where a client retrieves information privately and securely from databases, i.e., the privacy of the client is protected from the databases, and the undesired information is protected from the client. The OT problem studies the case of one database plus additional noisy resources between the database and the client. The SPIR problem studies the case of multiple replicated and non-colluding databases. In this paper, we combine the two models and propose a new problem of oblivious transfer (OT) with two replicated databases and a binary erasure multiple access channel connecting the databases and the client. We first provide an upper bound on the OT capacity. We then propose a protocol which achieves the upper bound. Therefore, we obtain the capacity of OT for this model. In our achievability and converse proofs, we utilized the techniques from both traditional OT and PIR. Compared to schemes that utilizes only techniques from OT, we see a 100% increase in the achieved OT rate.

Device-to-device (D2D) coded caching is an effective way to reduce the peak-time delivery rate of both the server and the users. It consists of two phases, the placement phase and the delivery phase. Most prior works on D2D coded caching are based on the assumption that all users will request content at the beginning of the delivery phase. However, in practice, this often times is not true. Motivated by this consideration, this paper formulates a new problem called the request-robust D2D coded caching, where the identity of the users making file requests are known only at the beginning of the delivery phase. For this novel D2D coded caching problem, we propose an achievable scheme based on uncoded cache placement and exploiting common demands and one-shot delivery. We show that the proposed scheme outperforms known D2D coded caching schemes applied to the request-robust scenario.

Entropy region is a set consisting of the entropic vector corresponding to every discrete probability distribution. Both its outer bound consisting of Shannon-type inequalities and inner bound consisting of the rank vectors corresponding to every arrangement of vector subspaces are closed under polymatroid duality. In 2018, Kaced proved that the closure of the entropy region is not closed under polymatroid duality for five or more discrete random variables, and the case of four discrete random variables is left as an open problem. In this paper, we give a definite answer to this open problem by proving via counter example that the closure of the entropy region is not closed under polymatroid duality for four discrete random variables either.

Abstract The authors propose a secure intelligent reflecting surface (IRS) assisted transmission system in the presence of a ground eavesdropper, where the source in the ground transmits confidential information to unmanned aerial vehicle (UAV), IRS is deployed to promote the transmission rate of the source to UAV. Closed‐form expression for secure transmission non‐outage probability is derived, and secure transmission performance is analyzed. Simulation validates the correctness of the derivation. Compared with benchmarks, results show that the IRS can improve the secure transmission of ground to UAV.

Smart factories need to support the simultaneous communication of multiple industrial Internet-of-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multiple-input and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The log-function method and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is that increasing the number of devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively constant for centralized mMIMO.

We propose a new D2D centralized coded caching problem, named the 3-user D2D coded caching with two random requesters and one sender (2RR1S), where in the delivery phase, any two of the three users will make file requests, and the user that does not make file request is the designated sender. We find the optimal scheme, denoted as the 2RRIS scheme, for any number of files$N$. To examine the usefulness of the proposed model and scheme, we adapt the 2RR1S scheme to three scenarios. The first one is the 3-user D2D coded caching model proposed by Ji et al. By characterizing the optimal rate-memory tradeoff for the 3-user D2D coded caching when$N=2$, we show that the adapted 2RR1S scheme is in fact optimal when the cache size is medium. The second scenario is request-random D2D coded caching. Adapting the 2RR1S scheme to this scenario, we show the superiority of our adapted scheme for medium to large cache size. The third scenario is K-user D2D coded caching with$K-s$random requesters and$s$senders, for which an achievability result is obtained by generalizing the 2RR1S scheme.

We study the secure distributed matrix multiplication (SDMM) problem under arbitrary collusion pattern. In the one-sided SDMM problem, where only one matrix of the matrix multiplication needs to be kept secure, we propose an achievable scheme that attains the optimal normalized download cost. The optimal scheme distributes a different number of encoded copies to each server, and the servers that collude more with others are given fewer encoded copies. The converse result is proved using Shearer’s lemma. In the two-sided SDMM problem under arbitrary collusion pattern, where the user would want to keep both matrices of the matrix multiplication secure, we provide an achievable scheme whose key parameters, including the method with which the random matrices are appended, the number of random matrices appended, the number of encoded copies generated, the number of encoded copies distributed to each server, are given by the proposed algorithm. We also demonstrate, via numerical results, the performance of the proposed scheme in terms of normalized upload-download cost trade-off, and show that it is much better than the current known scheme devised for the homogeneous collusion pattern.

We study the symmetric private information retrieval (SPIR) problem under arbitrary collusion and eavesdropping patterns for replicated databases. We find its capacity, which is the same as the capacity of the original SPIR problem with the number of serversNreplaced by a numberF*. The numberF* is the optimal solution to a linear programming problem, and it is a function of the joint pattern, which is the union of the collusion and eavesdropping pattern. This is the first result that shows how two arbitrary patterns collectively affect the capacity of the SPIR problem. We draw the conclusion that for SPIR problems, the collusion and eavesdropping constraints are interchangeable in terms of capacity, i.e., the two patterns play the same role in the SPIR problem and the capacity remains unchanged if we exchange the colluding and eavesdropping patterns. As corollaries of our result, the capacity of the SPIR problem under arbitrary collusion patterns, and the capacity of the PIR problem where each colluding set is included in some eavesdropping set, are also found. Some extensions with restrictions to finite message lengths are provided, and in this case, upper and lower bounds on the capacity are given. The lower bound is described with a solution to an integer linear programming problem.

We study the private information retrieval (PIR) problem under arbitrary collusion patterns for replicated databases. We find a general characterization of the PIR capacity, which is the same as the capacity of the original PIR problem with the number of databases N replaced by a number S*. S*is the optimal solution to a linear programming problem that is a function of the incidence matrix of the collusion pattern. Hence, the essence of any collusion pattern can be distilled into one number S*. In the proposed achievable scheme, databases are non-uniformly queried according to the optimal solution of a linear programming problem based on the collusion pattern. It can be seen that the databases who collude more with others are queried less. In the converse proof, Shearer's lemma is applied, in place of Han's inequality, to a linear combination of inequalities, where each inequality corresponds to one colluding set in the collusion pattern. The weights of the linear combination come from the optimal solution of another linear programming problem based on the collusion pattern. Finally, by noting the interesting fact that the two seemingly different linear programming problems, one used in the achievability proof and the other used in the converse proof, are in fact dual problems, we characterize the capacity of the PIR problem under arbitrary collusion patterns.

We propose a legal full‐duplex unmanned aerial vehicle (UAV) surveillance system in the presence of the ground‐to‐ground suspicious link with antisurveillance technology. UAV performs passive surveillance and active jamming simultaneously, and the suspicious source with multiantenna employs artificial noise to avoid being monitored. In order to ensure effective surveilling, we adopt two beamforming schemes, namely, maximum ratio transmission (MRT)/receiving zero‐forcing (RZF) and transmitting zero‐forcing (TZF)/maximum ratio combing (MRC), for MIMO UAV. For the two beamforming schemes, we derive the surveilling nonoutage probability in a closed‐form expression and analyze the surveilling performance under different system environments. Monte Carlo (MC) simulation validates the correctness of the formula.

We study the private information retrieval (PIR) problem under arbitrary collusion patterns for replicated databases. We find its capacity, which is the same as the capacity of the original PIR problem with the number of databases N replaced by a number S*. The number S* is the optimal solution to a linear programming problem that is a function of the collusion pattern. Hence, the collusion pattern affects the capacity of the PIR problem only through the number S*.

In this work, we investigate the capacity of private information retrieval (PIR) from N replicated databases, where a subset of the databases are byzantine. We allow for multi-round queries and demonstrate that the identities of the byzantine databases can be determined with a small additional download cost. As a result, the capacity of the multi-round PIR with byzantine databases (BPIR) reaches that of the robust PIR problem when the number of byzantine databases is less than the number of trustworthy databases.

The centralized coded caching problem is studied for the two-user scenario, considering heterogeneous cache capacities at the users and private channels from the server to the users, in addition to a shared channel. Optimal caching and delivery strategies that minimize the worst-case delivery latency are presented for an arbitrary number of files. The converse proof follows from the sufficiency of file-index-symmetric caching and delivery codes, while the achievability is obtained through memory-sharing among a number of special memory-capacity pairs. The optimal scheme is shown to exploit the private link capacities by transmitting part of the corresponding user`s request in an uncoded fashion. When there are no private links, the results presented here improve upon the two known results in the literature, namely: 1) equal cache capacities and arbitrary number of files and 2) unequal cache capacities and two files. The results are then extended to the caching problem with heterogeneous distortion requirements.

Combining cache and heterogeneous cellular networks (HetNets) together is proposed to satisfy the increasing capacity requirements in 5G networks. In this paper, we consider the analysis and optimization of random caching in the K-tier multi-antenna multi-user HetNets. We derive the expressions of the cache hit probability and the potential throughput, using tools from stochastic geometry. Limiting to the fully loaded interference-limited scenario, we obtain the closed-form exact expressions and the upper bounds of the metrics. The linear asymptotic expression of the cache hit probability in the high signal to interference and noise ratio (SINR) threshold region is also obtained. Based on analytical results, we consider the potential throughput maximization problem via optimizing the caching distribution. For a general scenario, we obtain a local optimal solution and a simple approximate asymptotic optimal solution in the high SINR threshold region. For a special case where each BS serves the largest number of users per resource block, we numerically solve a non-convex problem and propose a sub-optimal caching strategy by approximation, where a closed-form result is obtained in each iteration. Numerical simulations show that our proposed caching schemes outperform existing caching schemes. Analysis and optimization results provide insightful design guidelines for the cache-enabled multi-antenna HetNets.

In this paper, we study outer bounds on the capacity region of the downlink multicell processing model with finite backhaul capacity for the simple case of two base stations and two mobile users. It is modeled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity and the second hop from the base stations to the mobile users via a Gaussian interference channel. The outer bound is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our outer bound improves upon the existing outer bounds greatly in the medium backhaul capacity range, and as a result, the gap between the outer bounds and the rate of the time-sharing of the known achievable schemes is significantly reduced.

The centralized coded caching problem is studied under heterogeneous cache sizes and channel qualities from the server to the users, focusing on the two-user case. A server holding N files is considered to be serving two users with arbitrary cache capacities of M1and M2, and it is assumed that in addition to a shared common link, each user also has a private link from the server available during the delivery phase. Optimal caching and delivery strategies that minimize the worst-case delivery latency are presented for an arbitrary N. The converse proof benefits from Tian's observation that it suffices to consider file-index symmetric caching schemes, while the achievability is obtained through memory-sharing among certain special (M1, M2) pairs. The optimal scheme is shown to exploit the private link capacities by transmitting part of the corresponding user's request in an uncoded fashion. When there are no private links, the results presented here improve upon the two known results in the literature, namely, i) equal cache capacities and arbitrary number of files; and ii) unequal cache capacities and N = 2 files.

In this paper, we study the error exponent in the problem of lossless source coding with a helper. We use the inherently typical subset lemma to remove the Markov chain constraint in the proof of the converse and obtain an upper bound on the error exponent, which is very close to the existing lower bound. The proposed upper bound and the existing lower bound meet when the rate pair is close to the boundary of the optimal rate region.

We focus on the coded caching problem of N files and K users with heterogeneous cache sizes. By utilizing coded caching placement, we improve upon existing schemes in terms of the delivery rate. This is done by first finding an optimal caching and delivery scheme for some special heterogeneous-cache-size cases where L out of K users has no cache and the other K-L users are each equipped with a cache of size N-L. The optimal caching and delivery scheme exploits the Cauchy matrix and its properties. Next, memory-sharing between the special heterogeneous-cache-size points and existing equal/unequal-cache-size points are performed. Through numerical results, we demonstrate the gain of the proposed scheme to existing schemes for heterogenous cache sizes.

In this paper, we study upper bounds on the sum capacity of the downlink multicell processing model with finite backhaul capacity for the simple case of 2 base stations and 2 mobile users. It is modeled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity, and the second hop from the base stations to the mobile users via a Gaussian interference channel. The upper bound is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our upper bound improves upon the existing upper bound greatly in the medium backhaul capacity range, and as a result, the gap between the upper bounds and the sum rate of the time-sharing of the known achievable schemes is significantly reduced.

Caching popular contents in the base stations(BSs) is a promising way to reduce backhaul congestion by improving the transmission efficiency of the content dissemination. In this paper, we consider the scenario of BS cooperation for a cacheenabled HetNet where the helpers with cache are distributed around the MBSs. To evaluate the dependence between different tiers, we model the locations of the MBSs and the helpers as a Poisson point process (PPP) and a correlated Poisson cluster process (PCP), respectively. We adopt a hybrid cache placement strategy for different files based on the content request probability, in which the most popular files are fully cached in the helpers and the less popular files are partially cached. We then design three BS cooperation strategies to save the backhaul cost while providing satisfied rate for the typical user. For different BS cooperation strategies, explicit expressions of the average spectrum efficiency are derived using tools from stochastic geometry. Building upon the analytical results, we then effectively address the problem of how to design the optimal cache placement for the average spectrum efficiency maximization. Simulation results justify the significant gain of the optimal design and provide valuable insights for cache placement design in the cache-enabled HetNet with dependence.

In this study, the authors investigate the problem of energy‐efficient packet transmission with arbitrary arrival instants and deadline constraints over a point‐to‐point additive white Gaussian noise channel. This is different from previous work where it is assumed that the packets follow a first‐in–first‐out (FIFO) order in that a packet that arrives earlier has a deadline that is also earlier. They first investigate the necessary and sufficient conditions of the optimal offline transmission schedule. They then propose an algorithm which finds the transmission schedule of each packet. Next, they show that their algorithm is optimal by proving it satisfies the sufficient conditions of the optimal offline transmission schedule, and further obtain the computational complexity in the worst case: 𝔼( N 3 ), where N is the number of packets. In addition, based on the proposed optimal offline policy, an efficient heuristic online policy which assumes only the causal arrival information of the packets is proposed. Finally, simulation results show that the proposed offline policy can achieve significant energy savings compared with the existing FIFO offline policy, and the proposed online algorithm can achieve a comparable performance with the proposed optimal offline policy.

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency (EE) of a multiuser multiple-input multiple-output-aided distributed antenna system. The fronthaul link's power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints, some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the EE optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Second, a triple-layer iterative algorithm is proposed for solving the approximated EE optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened EE optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms are capable of achieving an order of magnitude higher EE than its conventional counterpart operating without considering link selection.

We study the Ahslwede-Han problem of a pointto-point communication with partial state information available at the destination. For a class of channels, by establishing a tight converse, we show that the Wyner-Ziv compression of the channel state treating the destination's channel output as side information is optimal. This result is more general than the modulo-sum channel studied by Aleksic et al. and the symmetric binary erasure channel with two states studied by Tandon and Ulukus. Thus, for this more general class of channels, we prove the Ahlswede-Han conjecture.

This paper investigates the problem of energy-efficient packet transmission with arbitrary arrival instants and deadline constraints over a point-to-point additive white Gaussian noise (AWGN) channel. This is different from previous work where it is assumed that the packets follow a First-In-First-Out (FIFO) order in that a packet that arrives earlier has a deadline that is also earlier. We first investigate the necessary and sufficient conditions of the optimal transmission schedule. We then propose an algorithm which finds the transmission schedule of each packet. Next, we show that our algorithm satisfies the sufficient conditions of the optimal transmission schedule and thus, is optimal. Finally, simulation results show that the proposed policy can achieve significant energy savings compared to the existing FIFO policy.

Wireless Network Virtualization (WNV) enables high resource utilization, inter-slice isolation and customizable resource allocation, and has attracted much interest. However, most of works on WNV did not consider the business model between Infrastructure Providers (InPs) and Service Providers (SPs) nor did they focus on the isolation and customization issues. This paper proposes a WNV framework for LTE where hypervisor does not have direct access to user information, and Resource Block (RB) allocation problem in this framework is investigated by adopting the Vickrey-Clarke-Groves (VCG) mechanism to model auction games between InPs and Mobile Virtual Network Operators (MVNOs) where MVNOs have the incentive to bid truthfully to compete for RBs. A unified utility function is utilized to enable MVNOs to determine their own scheduling strategies by setting the parameters in it. The RB allocation problem is formulated to divide the total RB set into different subsets to maximize the summation of reporting valuations of all MVNOs. Due to its complexity, a heuristic algorithm is proposed. Simulation results show that the intra-slice customization and inter-slice isolation of virtual networks are satisfied in our model.

We consider a downlink cloud radio access network (C-RAN) in which all the base stations (BSs) are connected to a central processor (CP) via digital fronthaul links with finite capacities. The problem of joint base station selection and beamforming design to coordinate interference for power minimization is studied.We formulate the problem of minimizing the network power, which includes the transport link power consumption, as an l0minimization problem for the network beamforming design.By assuming that the fronthaul capacity is finite, the fronthaul capacity constraints and the objective function of the network power both include l0norms, which are non-convex. To solve the l0minimization problem, we utilize the smooth function method in which the Majorization-Minimization (MM) algorithm iteratively solves a difference of convex (DC) problem after approximating the l0norm in the objective with a logarithmic function. We then utilize the reweighted l1norm technique to reformulate the per-BS fronthaul constraints into convex reweighted l1forms and solve the power minimization problem through an iterative algorithm.

Self-healing is an interesting topic in SON (Self- Organizing Networks). In this paper, we investigate cell outage detection problem, and propose an improved TCM (Transductive confidence machines) based automatic cell outage detection algorithm. By incorporating a hypothesis test with the Neyman-Pearson criterion to improve the detection accuracy, the improved TCM can effectively detect cell outage using normal data for training. The simulation results demonstrate that the proposed scheme has lower false alarm rate while ensuring high detection accuracy than the traditional outage detection methods.

This paper investigates the problem of energy-efficient packet transmission with a non-FIFO Packet over a point-to-point additive white Gaussian noise (AWGN) time-invariant channel under the feasibility constraints. More specifically, we consider the scenario where there is a packet that has a deadline that is earlier than that of the previously arrived packet. For this problem, the First-In-First-Out (FIFO) transmission mode adopted in the existing literatures is no longer optimal. We first propose a novel packet split and reorder process which convert the inconsistency in the order of deadlines and arrival instants of the packet sequence into a consistent one. After the split and reorder process, the original problem considered in this paper is transformed into the problem of finding the optimal split factor. We propose an algorithm that finds the split factor which consists of checking four possibilities by applying the existing optimal transmission strategy \emph{``String Tautening''} for FIFO packets. In addition, we prove the optimality of the proposed algorithm in the presence of a non-FIFO packet by exploiting the optimality properties of the most energy efficient transmission strategy. Based on the proposed optimal offline scheme, an efficient online policy which assumes causal arrival information is also studied and shown to achieve a comparable performance to the proposed optimal offline scheme.

We study the Enhanced Inter-Cell Interference Coordination (eICIC) for co-channel deployments of pico cells in macro cells. In particular, we consider a non- uniform topology where the number of pico cells within the coverage area of each macro cell is different. To alleviate the interference caused by the co-channel deployment, the macro cells employ low-power almost- blank subframe. We consider the joint problem of power and resource allocation,where the goal is to maximize the proportional fairness utility of the system. A convergent iterative algorithm is proposed and simulation results demonstrate that our proposed algorithm improves both the system throughput and user fairness compared to existing schemes.

In a system that performs both encryption and lossy compression, the conventional way is to compress first and then encrypt the compressed data. This separation approach has been proved to be optimal. In certain applications where sensitive information should be protected as early as possible, it is preferable to perform the encryption first and then compress the encrypted data, which leads to the concept of the reversed system. Johnson et al. proposed an achievable scheme for the reversed system, where a modulo-sum encryption is followed by a compression using the Wyner-Ziv distributed source coding with side information. However, in general, this reversed system performs worse than the conventional system in the sense that it requires more compression rate and secrecy key rate. In this paper, we propose a new achievable scheme for the reversed system, where the encryption is conducted by a permutation cipher, and then, the encrypted data is compressed using the optimal rate-distortion code. The proposed scheme can achieve the optimal compression rate and secret key rate. As a result, we show that reversing the order of the encryption and compression does not necessarily compromise the performance of an encryption-compression system. We show that the proposed system attains strong secrecy, and the information leakage vanishes exponentially.

In this paper, we propose a permutation-based code for the wiretap channel. We begin with an arbitrary channel code from Alice to Bob and then perform a series of permutations to enlarge the code to achieve secrecy to Eve. We show that the proposed code achieves the same performance as the traditional random code, in the sense that it achieves the random coding bound for the probability of decoding error at Bob and an exponentially vanishing information leakage at Eve. Thus, the permutation-based code we propose offers an alternative method of code construction for the wiretap channel.

In this paper, we study the multiple access diamond channel, where each relay has cached a part of the message intended for the destination node. It is assumed that the cached information at the two relays are independent. We propose an achievability scheme and show that the scheme of sending correlated codewords through the multiple access channel with the superposition structure proposed for the multiple access diamond channel can be easily adapted to the case where there are cached information at the relays. We further show the optimality of our proposed scheme when the channel satisfies certain conditions. Under these conditions, it is suboptimal for the source node to inform each relay the cached message of the other relay to form common data, rather it is optimal to have no common data and use the cached message at each relay to form the correlated codewords.

In this paper, energy saving of two-tier network: macrocells overlaid by randomly distributed picos is considered. A pico on/off algorithm based on offload bandwidth is proposed to save energy and improve the energy efficiency. Pico on/off selection will be triggered when the traffic load of macro base station (BS) exceeds thresholds. The objective of the algorithm is to minimize the number of active picos. Simulation results show that the proposed algorithm can save more energy compared with state of the art scheme.

In this paper, we study the probability of successful deception of an uncompressed biometric authentication system with side information at the adversary. It represents the scenario where the adversary may have correlated side information, e.g., a partial finger print or a DNA sequence of a relative of the legitimate user. We find the optimal exponent of the deception probability by proving both the achievability and the converse. Our proofs are based on a connection between the problem of deception with side information and the rate distortion problem with side information at both the encoder and the decoder.

In this paper, we study the capacity of the diamond channel. We focus on the special case where the channel between the source node and the two relay nodes are two separate links with finite capacities and the link from the two relay nodes to the destination node is a Gaussian multiple access channel. We call this model the Gaussian multiple access diamond channel. We first propose an upper bound on the capacity. This upper bound is a single-letterization of an $n$-letter upper bound proposed by Traskov and Kramer, and is tighter than the cut-set bound. As for the lower bound, we propose an achievability scheme based on sending correlated codes through the multiple access channel with superposition structure. We then specialize this achievable rate to the Gaussian multiple access diamond channel. Noting the similarity between the upper and lower bounds, we provide sufficient and necessary conditions that a Gaussian multiple access diamond channel has to satisfy such that the proposed upper and lower bounds meet. Thus, for a Gaussian multiple access diamond channel that satisfies these conditions, we have found its capacity.

We study the power allocation for incremental redundancy (IR) hybrid automatic repeat request (HARQ) in block fading channel where causal channel state information is known at both the transmitter and the receiver. In IR-HARQ, the traffic is assigned into consecutive transmission rounds, and at each round, the instantaneous mutual information that is required for successful decoding is upper bounded by the data rate that is to be delivered. We propose an HARQ power allocation method that maximizes the average of incremental mutual information at each round, and its throughput quickly converges to the ergodic capacity as the number of retransmissions increases. The numerical results show that the proposed power allocation method achieves almost the full channel capacity with moderate average transmission delay and that it maintains good throughput under stringent delay requirement.

In ultra dense heterogeneous networks (HetNets), the interference becomes more serious since multiple small cells coexist in the coverage area of the macrocells and share the same spectrum. An efficient interference coordination method is adjusting the transmit powers of all cells in a cooperative way. However, under practical serving cell selection rules in which serving cells of users alter with the variation of cell powers, finding optimal cell powers becomes a difficult problem. In this paper, an improved modified particle swarm optimization (MPSO) is proposed to tackle this difficult problem caused by the altering of serving cell. Local search and multi-restart process are introduced to guarantee the local and then global optimality, and the convergence conditions and global optimality are proved by mathematical deduction to guide the selection of the parameters. Simulations show that the proposed algorithm can significantly improve system throughput compared with existing algorithms which do not consider the alteration of serving cells. By improving MPSO, the proposed algorithm can spend less iteration time to achieve higher system throughput, and exhibit similar performance as exhaustive search in both system throughput and the signal to interference plus noise ratio (SINR) of users.

In this paper, we study the probability of successful deception of an uncompressed biometric authentication system with side information at the adversary. It represents the scenario where the adversary may have correlated side information, e.g., a partial finger print or a DNA sequence of a relative of the legitimate user. We find the optimal exponent of the deception probability by proving both the achievability and the converse. Our proofs are based on the connection between the problem of deception with side information and the rate distortion problem with side information at both the encoder and decoder.

In the scenario of downlink multicell processing, for the case of two base stations and two mobile users, by viewing the problem as a multiple access diamond channel with two destinations, we propose an achievability scheme that combines both the achievability of correlated codewords into the multiple access channel and Marton's achievability for the general broadcast channel. Compared with previously known schemes, our scheme focuses more on exploiting the gain of correlation between the transmitted signals of the base stations. We demonstrate through examples that our proposed scheme outperforms known achievability schemes when the capacities of the backhaul links are in the medium range.

We study a two-transmitter two-receiver network where Receiver 1 can only hear the transmitted signal of Transmitter 1. Transmitter 1 has two messages, one of which is intended for Receiver 1 while both are intended for Receiver 2. Transmitter 2 has one message which is intended for Receiver 2. We call this channel model the Z channel with degraded message sets. For networks with the element of distributed encoding, it has been shown that when the multiple access link between the two encoders and the decoder satisfies the conditions proposed by Liu and Goldsmith, capacity results can be obtained for a variety of problems. In this paper, we generalize these conditions and show that for a larger class of multiple access links, the capacity region of the Z channel with degraded message sets can be characterized despite the presence of distributed encoding.

We study the Ahlswede-Han problem of single-user communication with partial state information available at the destination. For a class of channels, we show that the Wyner-Ziv compression of the channel state treating the receiver's channel output as side information is optimal. This result is more general than the modulo-sum channel studied by Aleksic et. al in 2009. Thus, for this more general class of channels, we prove the Ahlswede-Han conjecture.

Following the work of Liu and Goldsmith in 2009, we study a class of Z-interference channels that satisfy the shift-invariant condition but not the maximum entropy condition. We provide a new capacity region outer bound for this class of Z-interference channels. We show the tightness of the proposed outer bound by finding the capacity region of certain Z-interference channels which were not previously known.

The intensive downlink (DL) inter-cell interference created by cell range expansion (CRE) of picocells is an urgent problem needing to be solved under macro-pico heterogeneous network (HetNet) scenario. A promising approach is taking advantage of additional degree of freedom brought by carrier aggregation (CA). However, poorly arranged carrier configurations or interference coordination schemes can lead to a degradation of system overall performance. In this paper, we propose a novel dynamic interference coordination scheme based on carrier aggregation to alleviate the DL interference from macrocells to users located in the cell range expansion area. Novel carrier configuration pattern and dynamic power control scheme based on price algorithm are applied to mitigate detrimental interference to picocell-edge users and improve the availability of macrocells. Simulation results show that the proposed scheme can boost the picocell-edge throughput significantly while ameliorating the overall system throughput.

In this paper, we investigate the energy efficiency problem in macro-relay network. Considering users with different locations and rate requirements, our objective is to serve all users with strict rate guarantee and meanwhile minimize the total energy consumption of the network. First, we formulate an integer optimization problem with the variables as stationuser connections. Then we analyze the complexity of the optimal brute-force search (BFS) method and propose a practical algorithm, i.e., serving station selection for energy minimization (SSSEM). The proposed SSSEM algorithm dynamically alters user's serving station according to both the radio frequency energy and the circuit energy, thus minimizing the total energy consumption of the network. Extensive simulations are conducted and the results show that, compared with existing schemes, the proposed SSSEM algorithm can significantly reduce the total energy consumption of the network, and meanwhile achieve the optimum of BFS.

We study the discrete memoryless Z-interference channel where the transmitter of the pair that suffers from interference is cognitive. We first provide an outer bound on the capacity region of this channel. We then show that, when the channel of the transmitter–receiver pair that does not experience interference is deterministic and invertible, our proposed outer bound matches the best known inner bound. The obtained results imply that in the considered channel, superposition encoding at the noncognitive transmitter as well as Gel'fand–Pinsker encoding at the cognitive transmitter is needed in order to minimize the impact of interference. As a byproduct of the obtained capacity region, we obtain the capacity under the generalized Gel'fand–Pinsker setting where a transmitter–receiver pair communicates in the presence of interference noncausally known at the encoder.

In this paper, we propose a novel opportunistic scheduling algorithm in multi-cell cooperation scenario, i.e., joint multi-user diversity (MUD) exploitation and inter-cell interference (ICI) mitigation based scheduling (MEIMS). Our algorithm jointly considers the intended channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring cells so as to exploit MUD and mitigate ICI simultaneously. The performance of our algorithm is evaluated through simulation. Results show that, our scheme can significantly enhance the received signal to interference plus noise ratio (SINR) with relatively better fairness guarantee, thus achieves the largest throughput and utility comparing to several well-known scheduling algorithms.

3GPP LTE networks can provide a higher capacity by adopting advanced physical layer techniques and serve users with different Quality of Service (QoS) requirements. However, unbalanced user distributions and strong inter-cell interference (ICI) still deteriorate network performances severely. Since fractional frequency reuse (FFR) technique is recommended to mitigate ICI, we investigate the load balancing problem in a 3GPP LTE multi-cell FFR network with heterogenous services in this paper. Firstly we formulate a multi-objective optimization problem, whose objectives are intra- and inter-cell load balancing index for users with QoS requirements and total utility function for users without QoS requirements. Then we analyze the complexity of the problem and propose a practical algorithm which includes QoS aware intra- and inter-cell handover and call admission control. Extensive simulations are conducted, the results show that our algorithm can lead to significantly better performances, i.e., a lower new call blocking rate for users with QoS requirements, a larger utility for users without QoS requirements at the cost of a bit degradation of total throughput.

In this paper, we propose a novel opportunistic scheduling algorithm, joint useful and interference channel based scheduling (JUICS), in coordinated multi-point (CoMP) transmission scenario. Our scheme jointly considers the useful channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring CoMP cells, thus to exploit multi-user diversity (MUD) and mitigate inter-cell interference (ICI) simultaneously. The performance of the proposed algorithm is evaluated through simulation in terms of the cumulative distribution performance of the received signal to interference plus noise ratio (SINR) and that of the scheduled times of all CoMP users. Results show that, with limited complexity and overhead, our scheme can significantly enhance the received SINR with relatively better fairness guarantee, thus to achieve the largest throughput and utility comparing to several well-known scheduling algorithms.

In this paper, we investigate call admission control (CAC) with opportunistic scheduling and propose a novel CAC algorithm for users with quality of service (QoS) requirements. Our main contribution is threefold. First, we verify that, compared with several other scheduling schemes, cumulative distributed function based scheduling (CS) makes the best tradeoff between efficiency and fairness in full-load scenario and exploits the best opportunism with absolutely fair resource allocation. Then we deduce and validate the multi-user diversity gain (MDG) of CS, which determines its long-term average performance and is used for estimation of resource occupation in CAC algorithm design. After that, we use opportunistic round robin (ORR) method to calculate the statistical low performance bound of CS, and propose CS/ORR based CAC (COCAC) algorithm, which guarantees the heterogeneous minimum rate requirement (MRRs) of both new access users and existing ones. Finally, we evaluate the performance of the proposed COCAC algorithm via simulation. Results show that COCAC can significantly reduce new call block probability, effectively make use of system resources, as well as strictly guarantee all users' MRRs.

In this paper, we investigate load balancing problem in 3GPP Long Term Evolution (LTE) networks and propose our solution which considers users with different Quality-of-Service (QoS) requirements. Load unbalance among neighboring cells often yields negative impacts on user experience and network performance, and it has mainly been considered for only data services without QoS guarantee. However, 3GPP LTE network aims to support multi-class services with different QoS requirements, on which the influence of load unbalance is quite different. For those with minimum rate requirements, it may result in high block probability, while for others without rate requirements, the throughput of boundary users may be degraded. In this paper, we incorporate all the differences into a network utility maximization framework and formulate it as a multi-objective optimization problem. The objectives in the problem are load balancing index of services with QoS requirements and the total utility of other services, and the constraints are physical resource limits and QoS demands. Then we analyze the complexity of the problem, and propose our solution, which includes a QoS guaranteed hybrid scheduling scheme, handover of users with and without QoS requirements, and a call admission control algorithm. Extensive simulation is conducted and the results show that the proposed framework leads to significantly better load balancing, and thus the decrease in call block probability of users with QoS requirements, and the increase in throughput of boundary best effort users.

In this paper, we study the capacity of the diamond channel. We focus on the special case where the channel between the source node and the two relay nodes are two separate links of finite capacity and the link from the two relay nodes to the destination node is a Gaussian multiple access channel. We call this model the Gaussian multiple access diamond channel. We first propose an upper bound on the capacity. This upper bound is a single-letterization of the n-letter upper bound proposed by Traskov and Kramer, which is tighter than the cut-set bound. Next, we provide a lower bound based on sending correlated codes through the multiple access channel. Since the upper and lower bounds take on similar forms, it is expected that they coincide for certain channel parameters. To show this, we further focus on the symmetric case where the separate links to the relays are of the same capacity and the power constraints of the two relays are the same. For the symmetric case, we give necessary and sufficient conditions that the upper and lower bounds meet. Thus, for a Gaussian multiple access diamond channel that satisfies these conditions, we have found its capacity.

We study the problem of secure communications over the multiple access channel where there is one eavesdropper in the system and the eavesdropper can only overhear the transmitted signal of one of the transmitters. This channel model can be seen as a special case of both scenarios currently studied in the literature for multiple access channels with secrecy constraints. In this simplified model, we obtain the secrecy capacity region if the multiple access channel satisfies certain conditions. We also compare the capacity regions of this special class of multiple access channels with and without secrecy constraints to illustrate the price paid for secrecy.

This paper studies the problem of secure communication over a wiretap channel where the transmitter and the legitimate receiver share a secret key, which is concealed from the eavesdropper. We find the secrecy capacity under this scenario. This result generalizes that of Yamamoto, which is applicable only to less noisy wiretap channels, to the general wiretap channel when no distortion is allowed at the legitimate receiver.

Load imbalance that deteriorates the system performance is a severe problem existing in 3GPP LTE networks. To deal with this problem, we propose in this paper a load balancing framework, which aims at balancing the load in the entire network, while keeping the network throughput as high as possible. In this framework, the objective is formulated as a network-wide utility function balancing network throughput and load distribution, and then it is transformed to an integer optimization problem under resource allocation constraints. After that, the complexity of the problem is analyzed, network structure constraints are presented, and a practical suboptimal algorithm, called Heaviest-First Load Balancing (HFLB), is proposed. Extensive simulation is made and the results show that using the HFLB algorithm the network can get significantly better load balancing while maintaining the same network throughput at the price of a bit more handovers compared with the traditional signal strength-based handover algorithm.

The problem of joint source-channel coding in transmitting independent sources over interference channels with correlated receiver side information is studied. When each receiver has side information correlated with its own desired source, it is shown that source-channel separation is optimal. When each receiver has side information correlated with the interfering source, sufficient conditions for reliable transmission are provided based on a joint source-channel coding scheme using the superposition encoding and partial decoding idea of Han and Kobayashi. When the receiver side information is a deterministic function of the interfering source, source-channel separation is again shown to be optimal. In addition to these source-channel coding problems, a new channel model that generalizes the classical interference channel is introduced: the interference channel with message side information. Achievable rate regions are given and a single letter characterization of the capacity region for a special class of Z-interference channels is provided. Using this capacity result and the optimality of source-channel separation, we demonstrate that our sufficient conditions for reliable transmission when each receiver has side information correlated with the interfering source are also necessary for some special cases. As a by-product, the capacity region of a class of Z-channels with degraded message sets is also provided.

In this paper, we study secure communications over a two-user semi-deterministic broadcast channel, i.e., one of the receivers is connected to the transmitter through a deterministic channel. We consider the case where the deterministic receiver is also the eavesdropper for the other receiver's message. We derive the secrecy capacity region by showing that superposition encoding plus Gel'fand-Pinsker encoding is optimal. We find that due to the deterministic component of the channel, Gel'fand-Pinsker binning alone is enough to achieve perfect secrecy. We also compare our scheme with the capacity-achieving scheme of Marton for the semi-deterministic broadcast channel where there is no secrecy constraint.

We study the discrete memoryless Z-interference channel (ZIC) where the transmitter of the pair that suffers from interference is cognitive. We first provide upper and lower bounds on the capacity of this channel. We then show that, when the channel of the transmitter-receiver pair that does not face interference is noiseless, the two bounds coincide and therefore define the capacity region. The obtained results imply that, unlike in the Gaussian cognitive ZIC, in the considered channel superposition encoding at the non-cognitive transmitter as well as Gel'fand-Pinsker encoding at the cognitive transmitter are needed in order to minimize the impact of interference. As a byproduct of the obtained capacity region, we obtain the capacity result for a generalized Gel'fand-Pinsker problem.

We define a class of Z-interference channels for which we obtain a new upper bound on the capacity region. The bound exploits a technique first introduced by Korner and Marton. A channel in this class has the property that, for the transmitter-receiver pair that suffers from interference, the conditional output entropy at the receiver is invariant with respect to the transmitted codewords. We compare the new capacity region upper bound with the Han/Kobayashi achievable rate region for interference channels. This comparison shows that our bound is tight in some cases, thereby yielding specific points on the capacity region as well as sum capacity for certain Z-interference channels. In particular, this result can be used as an alternate method to obtain sum capacity of Gaussian Z-interference channels. We then apply an additional restriction on our channel class: the transmitter-receiver pair that suffers from interference achieves its maximum output entropy with a single input distribution irrespective of the interference distribution. For these channels, we show that our new capacity region upper bound coincides with the Han/Kobayashi achievable rate region, which is therefore capacity-achieving. In particular, for these channels superposition encoding with partial decoding is shown to be optimal and a single-letter characterization for the capacity region is obtained.

We find the secrecy capacity of the 2-2-1 Gaussian MIMO wiretap channel, which consists of a transmitter and a receiver with two antennas each, and an eavesdropper with a single antenna. We determine the secrecy capacity of this channel by proposing an achievable scheme and then developing a tight upper bound that meets the proposed achievable secrecy rate. We show that, for this channel, Gaussian signalling in the form of beam-forming is optimal, and no pre-processing of information is necessary.

We apply a technique introduced by Korner and Marton to the converse of a class of Z-interference channels. This class has the properties that, for the transmitter-receiver pair that suffers from interference, 1) the conditional output entropy is invariant with respect to the input and, 2) the maximum output entropy is achieved by a single input distribution irrespective of the interference distribution. We show that for this class of channels, superposition encoding and partial decoding is optimal. We thus provide a single-letter characterization for the capacity region, which was previously unknown.

We provide a single-letter characterization for the capacity region of a class of discrete degraded interference channels (DDICs). The class of DDICs considered includes the DADIC studied by Benzel in 1979. We show that for the class of DDICs studied, encoder cooperation does not enlarge the capacity region, and therefore, the capacity region of the class of DDICs is the same as the capacity region of the corresponding degraded broadcast channel.

We investigate the optimal performance of dense sensor networks by studying the joint source–channel coding problem. There are$N$uniformly spaced sensor nodes sampling noiselessly a one-dimensional spatial random process over an interval$[0,U_{0}]$. The overall goal of the sensor network is for the sensor nodes to code and transmit the measurement samples to a collector node over a cooperative multiple-access channel with noisy feedback, and for the collector node to reconstruct the entire random process with minimum expected distortion. We provide separation-based lower and upper bounds for the minimum achievable expected distortion when the underlying random process is Gaussian. When the Gaussian random process satisfies some general conditions, such as the eigenvalues of its Karhunen–Loeve expansion decrease roughly inverse polynomially in order$x$, i.e., the$k$th eigenvalue is roughly$k^{-x}$, we evaluate the lower and upper bounds explicitly, and show that they are of the same order for a wide range of power constraints. Thus, for these random processes, under these power constraints, we show that the minimum achievable expected distortion decreases as$\left (\log NP(N) \right )^{1-x}$, where$P(N)$is the sum power constraint on the sensor nodes. Further, we show that the achievability scheme that achieves the lower bound on the distortion is a separation-based scheme that is composed of multiterminal rate-distortion coding and amplify-

We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a co-operative multiple access channel with feedback, and reconstruct the entire random process at the collector node. We provide lower and upper bounds for the minimum achievable expected distortion when the underlying random process is stationary and Gaussian. In the case where the random process is also Markovian, we evaluate the lower and upper bounds explicitly and show that they are of the same order for a wide range of sum power constraints. Thus, for a Gauss-Markov random process, under these sum power constraints, we determine the achievability scheme that is order-optimal, and express the minimum achievable expected distortion as a function of the sum power constraint.

We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a cooperative multiple access channel with imperfect feedback, and reconstruct the entire random process at the collector node. We provide lower and upper bounds for the minimum achievable expected distortion when the underlying random process is Gaussian. In the case where the random process satisfies some general conditions, we evaluate the lower and upper bounds explicitly and show that they are of the same order for a wide range of sum power constraints. Thus, for these random processes, under these sum power constraints, we determine the achievability scheme that is order-optimal, and express the minimum achievable expected distortion as a function of the sum power constraint

A Gaussian multiple-access channel with common data is considered. Capacity region when there is no fading is known in an implicit form. We provide an explicitly characterization of the capacity region and provide a simpler encoding/decoding scheme than that previously mentioned in the literature. Next, we give a characterization of the ergodic capacity region when there is fading, and both the transmitters and the receiver know the channel perfectly. Then, we characterize the optimum power-allocation schemes that achieve arbitrary rate tuples on the boundary of the capacity region. Finally, we provide an iterative method for the numerical computation of the ergodic capacity region and the optimum power-control strategies.

A Gaussian multiple access channel (MAC) with common data is considered. Capacity region when there is no fading is known in an implicit form. We provide an explicit characterization of the capacity region and provide a simpler encoding/decoding scheme than that mentioned in work by Slepian and Wolf. Next, we give a characterization of the ergodic capacity region when there is fading, and both the transmitters and the receiver know the channel perfectly. Then, we characterize the optimum power allocation schemes that achieve arbitrary rate tuples on the boundary of the capacity region. Finally, we provide an iterative method for the numerical computation of the ergodic capacity region and the optimum power control strategies

We investigate the capacity region of the Gaussian Z-channel with a small crossover link gain, i.e. /spl alpha//spl les/1. For the case of /spl alpha/<1, we provide an achievable region, and the converse for most of the achievable region. We also derive lower and upper bounds for the part of the region where the capacity boundary is unclear. For the case of /spl alpha/=1, we determine the capacity region exactly.