The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type for the next-generation mobile wireless networks that support a massive number of mobile users (MUs) demanding the diverse and stringent quality-of-services (QoS) on both short delay and low error-rate. Clearly, supporting mURLLC while guaranteeing the security QoS is crucial for implementing the cell-free massive multiple-input-multiple-output (cell-free massive MIMO) mobile network using finite blocklength coding (FBC). Towards these ends, in this paper we propose to develop a new statistical security-QoS provisioning scheme using FBC while simultaneously guaranteeing statistical delay-bounded QoS and error-rate bounded QoS for mURLLC. First, we establish a cellfree massive MIMO channel model for supporting mURLLC between distributed WiFi access points (APs) and multiple mobile users with the existence of an eavesdropper. Second, we maximize the achievable secrecy rate for cell-free massive MIMO while upper-bounding the secrecy-information leak-probability. Third, we define and develop the new metric of secrecy effective capacity for three-dimensional (3D) statistical QoS provisioning over cell-free massive MIMO networks. Finally, we use numerical analyses to validate and evaluate our developed statistical security-QoS guaranteed schemes.

Reconfigurable intelligent surface (RIS) has been widely envisioned as a key technique, which can enhance communication quality of service (QoS) for mobile users (MUs) by reconfiguring wireless propagation environments. Unlike traditional RISs that can only reflect signals, simultaneously transmitting and reflecting RIS (STAR-RIS) can extend half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. In this paper, we propose the joint optimization for the transmission and reflection coefficients, i.e., phase-shifts and amplitudes, of STAR-RIS and the transmit beamforming of base station (BS) over STAR-RIS aided downlink communications, where MUs move in real time and can change their movement patterns dynamically. First, taking into account MUs' mobility, we formulate a rate maximization problem to maximize the average transmission rate between BS and multiple MUs. Then, since the traditional deep reinforcement learning (DRL) methods struggle to adapt to dynamic network environments, we develop a meta-learning based scheme to optimize STAR-RIS's transmission and reflection coefficients and BS's transmit beamforming. Finally, we validate and evaluate our developed scheme through extensive simulations, showing that our meta-learning based scheme significantly outperforms baseline schemes and can adapt to MUs' new movement patterns quickly.

Integrated sensing and communications (ISAC) has been proposed to significantly improve the performance of applications through highly-efficient spectrum/hardware sharing between channel-sensing and data-communications. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information to mobile users to support massive ultra-reliable and low-latency communications (mURLLC) has imposed many new challenges not encountered before. To address these challenges, in this paper we investigate the channel capacity-distortion tradeoff for ISAC-enabled mURLLC over massive multiple-input multiple-output (MIMO) mobile networks using finite blocklength coding (FBC). First, we establish system models for ISAC-based architectures using massive-MIMO. Second, we define the capacity-distortion function under the distortion constraint for the estimated channel state. Third, we develop a new statistical quality of service (QoS) metric, termed ISAC-based$\epsilon$-effective capacity, to simultaneously guarantee statistical-delay and error-rate bounded QoS by optimizing the sensing-communication power splitting ratio of ISAC. Finally, we use numerical analyses to validate and evaluate our developed ISAC schemes in supporting mURLLC.

The convergence of machine learning and searchable encryption enhances the ability to protect the privacy and security of data and enhances the processing power of confidential data. To enable users to efficiently perform machine learning tasks on encrypted data domains, we delve into oblivious keyword search with authorization (OKSA). The OKSA scheme effectively maintains the privacy of the user’s query keywords and prevents the cloud server from inferring ciphertext information through the searching process. However, limitations arise because the traditional OKSA approach does not support multi‐keyword searches. If a data file is associated with multiple keywords, each keyword and corresponding data must be encrypted one by one, resulting in inefficiency. We introduce an innovative approach aimed at enhancing the efficiency of search processes while addressing the limitation of current encryption and search systems that handle only a single keyword. This method, known as the oblivious multiple keyword search with authorization (OMKSA), is designed for more effective keyword retrieval. One of our important innovations is that it uses the arithmetic techniques of bilinear pairs to generate new tokens and new search methods to optimize communication efficiency. Moreover, we present a detailed and rigorous demonstration of the security for our proposed protocol, aligned with the predefined security model. We conducted a comparative experiment to determine which of the two schemes, OKSA and OMKSA, is more efficient when querying multiple keywords. Based on our experimental results, our OMKSA is very efficient for data searchers. As the number of query keywords increases, the computational overhead of connected keyword searches remains stable. Finally, as we move into the 5G era, the potential applications of OMKSA are huge, with clear implications for areas such as machine learning and artificial intelligence. Our findings pave the way for further exploration and deployment of these frontier areas.

Service-caching, computation-offloading, and mobile edge-computing (MEC) have been widely recognized as three key 6G mobile wireless neworking techniques which can efficiently support implementing the ultra-dense networks (UDNs) with massive small-cell base stations (SBSs). But, these impose the new challenges for the UDNs to solely rely on grid power for energy supplying and to jointly optimize service-caching, computation-offloading, and resource-allocations. To overcome the above described difficulties, integrating energy-harvesting (EH) techniques with MEC-enabled 6G UDNs, we propose to develop the joint optimization schemes for cooperative service-caching, computation-offloading, and resource-allocations. In our considered UDNs, there exist a large number of EH-based stationary users (SUs) or mobile users (MUs), and a mixture of on-grid SBSs powered by electric grid and off-grid SBSs power-supplied by solar, radio frequency (RF) energy, etc. Specifically, first we formulate an energy minimization problem under a non-linear RF-energy EH model to minimize the sum of weighted energy consumption of users and off-grid SBSs. Second, for scenarios with SUs, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocations scheme using the hierarchical multi-agent deep reinforcement learning. We derive cooperative service-caching in each time frame, and then derive computation-offloading and resource-allocations in each time slot. Third, we extend our work to scenarios with MUs, where MUs can move with certain trajectories at low speeds. Finally, we validate and evaluate the performances of our proposed schemes through the extensive simulations.

Intelligent reflecting surface (IRS) has been widely recognized as one of the key techniques to improve secure communications performances. However, most existing works mainly focus on the passive beamforming, i.e., phase-shifts, design of a single IRS, without taking into account the cooperations among multiple IRSs and the optimizations of their relative positions. To overcome these deficiencies, in this paper we propose the joint optimizations between the transmit beamforming of massive multiple-input multiple-output (massive-MIMO) access point (AP) and double-IRS’ positions and passive beamforming over 6G secure mobile networks. In our proposed networking architectures, AP transmits data to multiple mobile users (MUs) through reflections of two cooperative IRSs under the existence of one eavesdropper (Eve). First, we formulate a secrecy rate optimization problem to maximize the expectations of all MUs’ aggregate secrecy rates when Eve’s exact position is unknown. Second, leveraging the deep reinforcement learning (DRL), we develop two joint deploying and beamforming schemes to tackle the uncertainty of Eve’s exact position. Finally, we validate and evaluate our developed schemes by conducting the extensive simulations, showing the significant performances improvements of our developed schemes through jointly optimizing IRSs’ positions/orientations and transmit-beamforming of massive-MIMOAP as the function of the predicted Eve’s deploying areas.

To satisfy the stringent quality-of-service (QoS) requirements of 6G mobile wireless networks on both channel state estimations and information transmissions, the integrated sensing and communication (ISAC) has been an enabling technique to sense and communicate by sharing the same frequency band and hardware. Simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. However, how to integrate ISAC with SWIPT to support the 6G traffic has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose an integrated sensing, communications, and powering (ISACP) scheme for supporting statistical-QoS provisioning over 6G wireless networks using massive multiple-input and multiple-output (massive MIMO) communications. First, we establish system models for our ISACP scheme and define the Cramér-Rao bound and channel estimation distortion under our proposed ISACP scheme. Second, we develop the performance metrics of Cramér-Rao bound and channel estimation distortion to derive the capacity-distortion function to jointly measure the performance of integrated sensing, communication, and powering. Third, we integrate the capacity-distortion function with the finite blocklength coding (FBC) through deriving the ISACP-based ϵ-effective capacity to support the statistical delay and error-rate bounded QoS provisioning in 6G. Fourth, we maximize the energy-efficiency by controlling the optimal wireless power transfer for our ISACP scheme. Finally, we use numerical analyses to validate and evaluate our proposed ISACP scheme.

Since the 6G mobile wireless networks require the high-performances on both channel state estimations and information transmissions, the technique of integrated sensing and communication (ISAC) has attracted considerable research attention due to its ability to sense and communicate by sharing the same frequency band and hardware. However, how to jointly optimize the sensing and communication functions of the ISAC to support the 6G traffic transmissions over a time-varying wireless fading channel has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose the ISAC scheme to jointly sense the channel state and transmit the wireless-streaming data using massive multiple-input and multiple-output (massive MIMO) communications over the Rician fading channel. First, we establish the system models for the ISAC scheme under the Rician fading wireless channel and the channel state estimation scheme using the radar sensing feedback. Second, we define the channel state estimation distortion and the capacity-distortion function of a massive MIMO channel to jointly measure the performances of sensing and communication in our ISAC scheme. Third, we integrate the capacity-distortion function with the finite blocklength coding (FBC) scheme by developing the concept of the ISAC-based E-effective capacity to implement the statistical delay and error-rate bounded provisioning for supporting the 6G traffic under our ISAC scheme. Finally, we use numerical analyses to validate and evaluate our proposed ISAC scheme with massive MIMO in the non-asymptotic regime.

The upcoming 6G mobile wireless networks are expected to support massive ultra-reliable and low-latency communications (mURLLC), which is an emerging service that demands more stringent requirements than the fifth generation (5G) wireless networks on delay and error-rate bounded quality-of-services (QoS) with massive connectivity. Finite blocklength coding (FBC) techniques based short packets communication techniques have been shown to be able to support both the statistical delay and error-rate bounded QoS provisioning. Rate splitting (RS) multiple access schemes have been proposed to address the sum degree-of-freedom loss problem over massive multiple-input and multiple-output channels when massive mobile users request to access the network. However, how to support the statistical delay and error-rate bounded QoS provisioning using FBC among massive mobile users to enable the massive access has not been sufficiently studied. In this paper, we propose to integrate FBC with RS techniques to achieve mURLLC transmissions. First, we define the ∊-effective capacity to measure the performance of statistical delay and error-rate bounded provisioning, and obtain a closed-form ∊-effective capacity under the RS scheme. Then, we maximize the aggregate ∊-effective capacity over all mobile users by deriving an optimal transmit power allocation strategy for the RS scheme. Finally, using numerical analyses, we validate and evaluate our proposed RS schemes to support statistical delay and error-rate bounded QoS provisioning over 6G communication networks in the non-asymptotic regime.

Abstract Massive medical data are indispensable for training diagnostic models to provide high-quality health monitoring services. The methods for sharing data in existing works involve securely and essentially copying data but often overlook the integration and efficiency of data storage, exchange and application. In this paper, we propose a Secure Decentralized Trading Alliance (SDTA) to encompass the entire process holistically. With monetary incentives, we formulate a chain-net structure for recording data digests and authentic transactions, thereby transforming data sharing into data trading without duplicating data storage. Data privacy is promised by encryption. To manage and employ encrypted medical data, users can update and search their encrypted data using an index and keywords, subsequently retrieving data within the SDTA framework. It is realized by a novel dynamic searchable symmetric encryption (SSE) with an $l$-level access strategy, which confines users to data pertinent solely to them, thus circumventing unnecessary data leakage. We scrutinize the storage efficiency and prove the fairness and security of SDTA. Finally, we generate datasets of varying sizes, where the time required to search for a single keyword is approximately 0.04 s with 1 000 000 (keyword, identifier) pairs, showing it quite acceptable.

Metaverse streaming, as one of the key wireless services over 6G mobile networks, generates the delay/error-sensitive and bandwidth-intensive wireless traffics with stringent quality-of-service (QoS) requirements. Consequently, metaverse streaming can be modeled as a new type of massive ultra-reliable low-latency communications (mURLLC) traffic over 6G mobile networks. However, how to efficiently support metaverse streaming with constrained wireless resources and dynamic network conditions has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose the Neyman-Pearson criterion driven network functions virtualization (NFV) and software-defined network (SDN) architectures and optimal resource-allocations for statistical-QoS theory based mURLLC streaming over 6G metaverse mobile networks using finite blocklength coding (FBC). First, we use Neyman-Pearson hypothesis tests for characterizing metaverse streaming requests’ distribution profiles to predict their future accessing frequencies/patterns. Second, our formulated NFV/SDN architectures and virtual-network slices are assigned to the designated metaverse mobile users with the same predicted data request distributions, categories, and statistical-QoS requirements. Third, integrating the statistical QoS theory with FBC, we develop metaverse-streaming schemes by maximizing aggregate$\epsilon $-effective capacity and deriving optimal transmit power allocations. Finally, we use numerical analyses to validate and evaluate our proposed schemes over 6G mobile networks.

Cloud computing has become prevalent due to its extensive storage resources and robust computational capacities. To protect data security and privacy, data owners opt for uploading encrypted data to the cloud. Flexible sharing of these encrypted data in a group of users is a critical functionality in cloud storage. In addition, given that users may exit the group, revocation becomes a crucial requirement in group data-sharing systems. The Key-Aggregate Cryptosystem (KAC) has become a promising mechanism for group data sharing. The decryption rights for any set of ciphertexts can be efficiently delegated by distributing a constant-size aggregate key, while the confidentiality of other ciphertexts outside the set is maintained. However, in previous KAC schemes, revocation remains a challenging task regarding key update, ciphertext re-encryption, and collision resistance. In this paper, we propose a Key-Aggregate Cryptosystem with User Revocation (KAC-UR) scheme to overcome this challenge. The KAC-UR scheme not only achieves flexible data sharing, but also can perform secure and efficient user revocation with properties including collision resistance, revocation without data owner-user communication, and constant ciphertext size. The KAC-UR scheme also enables the cloud server to perform partial decryption, thereby significantly alleviating the computational burden for users. The KAC-UR scheme is chosen plaintext attack secure under the decisional Bilinear Diffie-Hellman Exponent assumption.

In this paper, we propose a Mergeable Searchable Symmetric Encryption (MSSE) scheme to enable secure keyword search and updates over encrypted cloud data. Particularly, MSSE allows flexible keyword merging, where users can remotely merge file identifiers associated with keywords to create new keyword-to-file identifier relationships. The function is designed for a user to manage their outsourced data conveniently. To this end, we first introduce a new encrypted index where each keyword's relevant file identifiers are grouped, encoded, and encrypted with super-increasing sequences and homomorphic encryption. With such an index, users leverage Distributed Multi-point Functions (DMPFs) to achieve secure keyword search and merge, maintaining efficiency while ensuring high privacy. To address the issue of maintaining “merging consistency” between pre-merged entries and newly updated entries, we employ the DMPF on clusters that incorporate the updated files. The approach significantly minimizes client-side computational overhead compared to re-executing the entire keyword merging process. We formally prove that MSSE can achieve parallel privacy. Extensive performance evaluation shows that MSSE is efficient in terms of computational and communication overheads.

Mobile edge-computing (MEC) enabled ultra-dense networks (UDNs), which merges edge-computing with UDNs, can provide enormous benefits, e.g., ultra-low latency. However, due to the ultra-dense deployment of small-cell base stations (SBSs), it becomes infeasible to just depend on the grid power for energy providing, and also it is challenging to jointly optimize service-caching, computation-offloading, and resource-allocation. Integrating energy-harvesting (EH) techniques into MEC-enabled UDNs, we investigate the joint optimization for cooperative service-caching, computation-offloading, and resource-allocation. In our considered UDNs, there exist a large number of EH-based mobile users (MUs) and a mixture of on-grid SBSs, powered by electric grid, and off-grid SBSs, powered by solar, radio frequency (RF) energy, etc. We formulate an energy minimization problem to minimize the sum of weighted energy consumption of all MUs and off-grid SBSs. Also, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocation scheme based on the hierarchical multiagent deep reinforcement learning (HMDRL). Using HMDRL, we first derive SBSs' cooperative service-caching policies which are updated in each time frame consisting of multiple time slots. Then, we derive MUs' and SBSs' computation-offloading policies and SBSs' computation resource-allocation policies, which are updated in each time slot. Finally, we validate and evaluate the performances of our proposed schemes through simulations.

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs.

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ϵ-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC.

Abstract Multi-keyword ranked searchable encryption (MRSE) supports multi-keyword contained in one query and returns the top-k search results related to the query keyword set. It realized effective search on encrypted data. Most previous works about MRSE can only make the complete keyword search and rank on the server-side. However, with more practice, users may not be able to express some keywords completely when searching. Server-side ranking increases the possibilities of the server inferring some keywords queried, leading to the leakage of the user’s sensitive information. In this paper, we propose a new MRSE system named ‘multi-keyword ranked searchable encryption with the wildcard keyword (MRSW)’. It allows the query keyword set to contain a wildcard keyword by using Bloom filter (BF). Using hierarchical clustering algorithm, a clustering Bloom filter tree (CBF-Tree) is constructed, which improves the efficiency of wildcard search. By constructing a modified inverted index (MII) table on the basis of the term frequency-inverse document frequency (TF-IDF) rule, the ranking function of MRSW is performed by the user. MRSW is proved secure under adaptive chosen-keyword attack (CKA2) model, and experiments on a real data set from the web of science indicate that MRSW is efficient and practical.

Abstract Verifiable symmetric searchable encryption is a keyword search technology that supports verification of search results. Many schemes improve search performance by dividing each keyword label into segments and storing them in a Trie-tree at the expense of high storage. And the index will degenerate into a linear linked list when all keyword labels have the same prefix except for the last segment. But it will greatly affect the search efficiency. In this paper, we propose a verifiable symmetric searchable encryption scheme based on the AVL Tree (abbreviated as VSSE-AVL), which uses complete keyword labels to build the index. Compared with the Trie-tree index, VSSE-AVL not only balances storage and search performance, but also avoids degradation. To verify the correctness and completeness of empty search results, we store path information in each leaf node and node with only one child node. Considering the substitution attack, we bind the file identifier and the file so that the client will find out once the server returns inconsistent search results. Rigorous security analysis shows VSSE-AVL satisfies privacy and verifiability. Compared with the verifiable SSE-2 with the same security, the experimental evaluation shows that our proposed scheme performs better on storage, search and verification.

The correctness and reliability of data sources are the keys to the practicality of data collected by Internet of Things (IoT) devices. Attribute-based signature (ABS) is a cryptographic primitive for users to sign with their own attributes, which can be applied to the authentication process in IoT scenarios. The attribute authority is responsible for issuing the attribute key to the user in ABS. Multiple authorities can complete attribute management tasks to avoid the threat of a single authority. However, attribute authorities need to execute multiple interactions to collaborate to generate attribute keys for users, which brings a large transmission burden. In addition, a lot of resource-constrained terminals in the IoT mostly play the role of signer or verifier in authentication protocols. The signature generation and verification algorithms often have heavy pairing and exponentiation operations. Currently, no ABS scheme takes into account the efficiency of all participating entities simultaneously. In this article, we present an aggregated anonymous key issue (AAKI) protocol to reduce the transmission burden between multiple authorities. Meanwhile, the noninteractive zero-knowledge proof aggregate exponentiation (NI-ZKPoKAE) protocol is designed to aggregate the transmitted secret values in AAKI. To reduce the computational burden of signers and verifiers, Blakley secret sharing, where the Hadamard matrix is used more efficiently to handle the$(n, n)$-threshold, is used to construct an efficient and fine-grained multiauthority ABS (EFMA-ABS) scheme. This brings high efficiency to all three types of parties involved in IoT authentication. Our above-mentioned protocols have been proven to be feasible and effective.

We address secure communications over energy-harvesting based orthogonal frequency-division multiple-access (OFDMA) cooperative cognitive radio networks, where one primary user (PU) cooperates with several size-limited secondary users (SUs) in terms of both data transmission and energy harvesting. To improve spectrum utilization and ensure that SU can harvest as much energy as possible, we let the size-limited SUs be equipped with orthogonally dual-polarized antennas (ODPAs). Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply the power splitting technique to harvest energy from radio frequency (RF) signals radiated by the PU, and then use the harvested energy to concurrently transmit their own and the PU’s data. Under our proposed framework, we develop three secure resource allocation schemes for scenarios when SUs are untrusted users, which implies that each SU may overhear the PU’s and the other SUs’ confidential information. For these scenarios, which has hardly been studied, we jointly optimize the allocation of relays, subcarriers, power splitting ratios, and powers when SUs adopt the decode-and-forward (DF) strategy or the amplify-and-forward (AF) strategy to relay the PU’s data, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU’s secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and secure resource allocation schemes through numerical analyses.

The sixth generation (6G) mobile wireless networks are expected to provide a wide range of massive ultra-reliable and low-latency communications (mURLLC) for multimedia transmissions, which require extremely stringent delay and error-rate bounded quality of services (QoS). Massive multiple-input and multiple-output (MIMO) communication has been recognized as one of the promising techniques to support mURLLC thanks to its advantages in the beamforming gain and spatial multiplexing, etc. On the other hand, finite blocklength coding (FBC) based small packets communication technique has recently shown to be able to support the statistical delay and error-rate bounded QoS provisioning. However, how to achieve the statistical delay and error-rate bounded QoS provisioning through massive MIMO techniques has not been sufficiently studied. In this paper, we propose to apply the ∊-effective capacity into massive MIMO communications to achieve the statistical delay and error-rate bounded provisioning for mURLLC traffic transmissions. First, we develop the FBC based scheme over a Nakagami-m fading wireless channel. Then, using the developed FBC based system model, we derive a closed-form expression for ∊-effective capacity of massive MIMO communications, representing the maximum packet's arrival rate that a wireless channel can support under a given stringency of delay requirement and a constrained decoding error-rate. Finally, we use numerical analyses to validate and evaluate our proposed statistical delay and error-rate bounded QoS provisioning scheme over massive MIMO communication networks.

Searchable Symmetric Encryption (SSE) is a promising method for users to store data in remote clouds securely and search them using keywords over an encrypted index. In this paper, we explore a new function named “keyword diverting” and propose a variant of SSE named Divertible Searchable Symmetric Encryption (DivSSE). Specifically, the index in DivSSE is encoded into an inverted, compressed, and encrypted format, by using the super-increasing sequence, symmetric homomorphic encryption (SHE), and a secure hash function. According to the homomorphic properties of SHE, users can construct a unique keyword diverting token, which can be utilized to update the encrypted index by obliviously merging data identifiers corresponding to different keywords without searching in advance and thus achieve keyword diverting. Moreover, based on function secret sharing, DivSSE can protect users' search patterns and reduce communication costs with the assistance of two independent clouds. Detailed security proof demonstrates that DivSSE can achieve parallel privacy, forward privacy, and backward privacy. Extensive performance evaluation also shows that DivSSE is efficient in terms of computational and communication overheads.

Tremendous research efforts have been made in conceptualizing 6G mobile wireless networks to support unprecedented scenarios with extremely diverse and challenging delay and error-rate bounded quality-of-services (QoS) requirements for ultra-reliable and low latency communications (URLLC), especially for cell-edge users. However, QoS performance is greatly limited by the computation capacity and finite battery capacity. To address this issue, mobile edge computing (MEC) has been developed by enabling mobile users to offload partial or complete computation-intensive tasks to MEC servers for computing. In addition, leveraging the significant improvements in coverage rate and spectral efficiency, intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) integrated MEC systems, which smartly reconfigure and design wireless propagation environments by bypassing blockage of line-of-sight (LOS) communications, can avoid service starvation of cell-edge users while supporting QoS for URLLC. However, how to statistically upper-bound both delay and error rate for URLLC in multiple-input multiple-output (MIMO)-UAV/IRS-based MEC systems still remains a challenging problem, especially when considering short-packet communications, such as finite blocklength coding (FBC). To overcome these difficulties, in this paper we propose FBC-based joint beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS for URLLC with MEC. First, we develop MIMO-UAV/IRS-based 3D wireless channel models using FBC. Second, we formulate and solve the ϵ-effective energy-efficiency maximization problems by converting non-convex problems into convex problems in both single-user and multiple-user scenarios. Finally, the obtained numerical analyses validate and evaluate our developed MIMO-UAV/IRS-based schemes.

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing mobile wireless network architectures for ultra-reliable and low-latency communications (URLLC). One of the major design issues raised by URLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying wireless fading channels. To efficiently accommodate statistical QoS provisioning for URLLC traffic, it is crucial to model and investigate wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, outage capacity, etc., in the non-asymptotic regime. However, how to rigorously and efficiently characterize the stochastic dynamics of mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither well understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling frameworks and controlling mechanisms for statistical delay and error-rate bounded QoS provisioning in the non-asymptotic regime. First, we establish FBC-based system models by characterizing various information-theoretic specifications. Second, we characterize the outage-probability and outage capacity functions in the non-asymptotic regime. Third, we develop a set of new statistical delay and error-rate bounded QoS metrics and control mechanisms including delay-bound-violation probability, QoS-exponent functions, and the -effective capacity in the non-asymptotic regime. Finally, the obtained simulation results validate and evaluate our proposed controlling mechanisms for statistical QoS in supporting URLLC.

In order to support extremely diverse and challenging delay and error-rate bounded quality-of-service (QoS) requirements for ultra-reliable and low latency communications (URLLC), a number of of promising 6G techniques, including unmanned-aerial-vehicles (UAVs), intelligent reflecting surfaces (IRSs), finite blocklength coding (FBC), etc., are being developed for potential use in 6G wireless networks. In addition, to implement over-the-air intelligent reflection and enlarge wireless service areas, integrating UAVs and IRSs provides a promising means to significantly enhance line-of-sight (LOS) coverage due to the relatively high altitude and 3D mobility of the UAVs. However, it is very challenging to characterize system models and guarantee statistical delay and error rate bounded QoS requirements in such complicated and dynamic UAV/IRS-based wireless network environments while supporting URLLC. To overcome these difficulties, in this paper we propose joint passive IRS beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS provisioning for URLLC over UAV/IRS-based wireless networks using FBC. First, we develop UAV/IRS-based 3D wireless channel models in the finite blocklength regime. Second, we formulate and solve the FBC-based ϵ-effective energy-efficiency maximization problem by jointly optimizing power allocation, passive IRS beamforming, and UAV trajectory for our developed schemes. Finally, the obtained simulation results validate and evaluate our proposed schemes over UAV/IRS-based wireless networks.

While the fifth generation (5G) of wireless networks is currently being rolled out, wireless networks still need further development to meet the requirements of dramatically increasing numbers of users and new applications and the resulting traffic expected in the coming decade and beyond. The sixth generation (6G) of wireless networks is envisioned to respond to this by providing services with massive access, ultra-reliability, low latency, intelligence, and security while maximizing the spectral/energy/cost efficiency. Unmanned aerial vehicles (UAVs) have attracted considerable research attention due to their mobility and ability to line-of-sight (LoS) coverage in areas that suffer from low channel quality. However, how to characterize a UAV’s coverage area is a challenging problem and has not been thoroughly studied. To address this issue, in this paper we investigate the coverage performance of base station (BS) to UAV communications with a number of interfering mobile users. We first establish a Nakagami-m fading channel model for BS-toUAV wireless communications. Then, we derive a closed-form expression for the UAV’s average coverage probability under the scenario of interfering mobile users. Finally, numerical results confirm our derived analytical results and evaluate the UAV’s performance under different scenarios that anticipate 6G wireless networking models.

As widely recognized 6G promising techniques, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have recently attracted much research attention in both academia and industries. In this paper, we propose the schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over the radio-frequency (RF)-powered 6G mobile wireless networks, which consists of an UAV serving as an MEC-server to collect/receive data from multiple ground users (GUs) and several sets of IRSs to significantly enhance the simultaneous wireless data and energy transmissions. To overcome the difficulties of on-board energy limitation significantly affecting UAV’s sustainability and performances, we propose the schemes to minimize UAV’s total flying-time while enabling all GUs’ data to be collected/received. We formulate a processing-time minimization problem which jointly optimize IRSs’ phase shifts, UAV’s control in trajectory, flying-time, and resource-allocation, and scheduling of GUs. Since our joint-optimization problem is non-convex, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which thus can be iteratively solved. Finally, we validate and evaluate our proposed schemes through the conducted numerical analyses, showing that the total UAV flying-time can be significantly saved by around 20% under our proposed IRS/UAV-based schemes simultaneously supporting both wireless data and energy transmissions.

The sixth generation (6G) wireless networks are required to provide the massive ultra-reliable low-latency communication (mURLLC) services for massive subscribers, and thus, need to be supported by new techniques. Since the massive multiple-input multiple-output (massive MIMO) technique with massive antennas is able to substantially improve the channel performance, it has been widely applied to achieve the goal of mURLLC networks. Moreover, based on the inherent advantages of high mobility and dynamically deployment, the emerging unmanned aerial vehicle (UAV) technique has also been considered as one of the promising candidate techniques in the 6G wireless networks. However, how to integrate the massive MIMO and UAV techniques has never been thoroughly studied. In this paper, we first establish the massive MIMO channel model between a set of UAVs and a ground station, equipped with uniform rectangular antenna array. Then, we derive the expression of channel capacity for this channel model, which is a function of the distance between each UAV and each antenna. To support the mURLLC traffics in the 6G wireless networks, we employ the effective capacity theory to measure the maximum packet arrival rate, and we also derive the upper-bound on the effective capacity, which is a function of our obtained channel capacity. Finally, we validate and evaluate our derived results of the UAV communication with massive MIMO channel over 6G wireless networks through numerical analyses.

The multiple access technique has been proposed to accommodate a number of heterogeneous communication devices to support a wide variety of applications and services in the sixth generation (6G) mobile wireless networks. Due to the inherent merits in programmability, mobility, and dynamic configuration, unmanned aerial vehicle (UAV) is admitted as the candidate technique for the 6G wireless communication networks. Moreover, UAVs are becoming the important enablers of various applications in military, surveillance, monitoring, supplies delivery, and connection recovery as a temporary hotspot, etc. However, how to efficiently integrate UAV wireless communication system with their trajectory control for 6G networks has neither been well understood nor thoroughly studied. To overcome this challenge, in this paper we propose and develop a control scheme for jointly optimizing UAV coverage probability and trajectory tracking control to efficiently support UAV communications over 6G mobile wireless networks. First, we develop a base station (BS) to UAV communication channel model, and derive the UAV’s coverage probability under the Nakagami-m fading channel. Since the UAV’s coverage probability depends on its relative posture (i.e., position and angle) to the BS and interfering mobile users, we then derive the UAV flying trajectory control scheme to minimize its trajectory tracking error. We also show that our proposed control schemes can attain the Lyapunov stability of trajectory error. Finally, we validate and evaluate our derived results of the UAV trajectory control scheme over 6G networks through numerical analyses.

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) coexist. By optimizing MUs' network access (through traffic-offloading) and system resource-allocation, we aim to minimize the energy consumption of MUs by using the low-energy consumption of the backscatter communications. First, we develop a distributed traffic-offloading and resource-allocation scheme, when the short-range ambient backscatter (AB) communication (generally with communication range being several meters) is employed to assist MUs' data transmission. Based on our developed scheme, MUs choosing to access nearby SAPs can backscatter ambient RF signals for data transmission to reduce energy consumption. Then, we employ the long-range bistatic backscatter (BB) communication (with communication up to 270 meters) to support data transmission between MUs and the BS, and thus MUs can convey data by backscattering RF signals emitted from dedicated carrier emitters. Accordingly, we develop a joint traffic-offloading and resource-allocation scheme for the scenarios when the AB and BB communications are adopted by MUs accessing the SAPs and the BS, respectively. Finally, we validate and evaluate the performance of our developed schemes through numerical analyses.

Taking advantage of the broadcast nature of radio frequency (RF) wave propagation, simultaneous wireless information and power transfer (SWIPT) has recently gained significant research attention since it can prolong the battery-life of energy-constrained and low-power-supported mobile devices. In addition, due to the potential benefits of favorable propagation and channel hardening, cell-free (CF) massive multi-input multi-output (m-MIMO) can significantly enhance the QoS performance of SWIPT in terms of the achievable data rate and energy efficiency. On the other hand, finite blocklength coding (FBC) has been proposed to guarantee stringent QoS requirements while reducing the access latency using short-packet communications. However, how to efficiently integrate these new techniques using FBC based statistical delay-bounded QoS theory has imposed many new challenges not encountered before. To overcome these difficulties, in this paper we propose and develop statistical delay and error-rate bounded QoS provisioning schemes over SWIPT-enabled CF m-MIMO 6G wireless networks in the finite blocklength regime. In particular, we establish SWIPT-enabled CF m-MIMO based system models by using FBC. We also formulate and solve the optimization problems for the tradeoff between the E-effective capacity and harvested energy for our proposed statistical delay and error-rate bounded QoS provisioning mechanisms. The obtained simulation results validate and evaluate our developed schemes.

To support the new and dominating traffic services – ultra-reliable and low latency communications (URLLC), the short-packet data communication techniques, such as finite blocklength coding (FBC), have been developed to guarantee the stringent delay and error-rate bounded quality-of-services (QoS) requirements for delay/age-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information for the delay/age-sensitive data transmissions. Since the status updates normally only consist of a small number of information bits and require ultra-low latency, integrating AoI with FBC creates another promising solution for supporting delay/age-sensitive URLLC services. However, how to characterize the relationships between AoI and delay over URLLC-enabled wireless networks has neither been well understood nor thoroughly studied in the finite blocklength regime. To overcome these challenges, we propose the joint optimization and tradeoff modeling for both peak AoI and delay-bound violation probabilities to support URLLC in the finite blocklength regime. First, we build up FBC based AoI-driven system models in the finite blocklength regime. Second, we apply the stochastic network calculus (SNC) to upper-bound both the peak AoI violation probability and delay-bound violation probability. Third, we jointly optimize the peak AoI violation probability and delay-bound violation probability and characterize their tradeoff in the finite blocklength regime. Finally, we conduct the extensive simulations to validate and evaluate our proposed AoI-driven schemes in the finite blocklength regime.

The large-scale interactive services distribute clients' requests across a large number of physical machine in data center architectures to enhance the quality-of-service (QoS) performance. In parallel and distributed data center architecture, even a temporary spike in latency of any service component can significantly impact the end-to-end delay. Besides the average latency, tail-latency (i.e., worst case latency) of a service has also attracted a lot of research attentions. The tail-latency is a critical performance metric in data centers, where long tail latencies refer to the higher percentiles (such as 98th, 99th) of latency in comparison to the average latency time. While the statistical delay-bounded QoS provisioning theory has been shown to be a powerful technique and useful performance metric for supporting time-sensitive multimedia transmissions over mobile computing networks, how to efficiently extend and implement this technique/performance-metric for statistically bounding the tail-latency for data center networks has neither been well understood nor thoroughly studied. In this paper, we model and characterize the tail-latency distribution in a three-layer parallel and distributed data center architecture, where clients request different types of services and ten download their requested data packets from data center through a first-come-first-serve M/M/1 queueing system. We first define the statistical tail-latency bounded QoS, and investigate the tail-latency problem through generalized extreme value (GEV) theory and generalized Pareto distribution (GPD) theory. Then, we propose a scheme to identify the dominant sources of latency variance in a semantic context, so that we are able to optimize the instructions of those sources to reduce the latency tail. Finally, using numerical analyses we validate and evaluate our developed modeling techniques and schemes for characterizing the tail-latency QoS provisioning theories in supporting data center networks.

Massive ultra-reliable and low latency communications (mURLLC) has been developed as a new and dominating 6G standard traffic service to support statistical delay and error-rate bounded quality-of-services (QoS) provisioning for real-time data-transmissions. Inspired by mURLLC, finite blocklength coding (FBC) has been proposed to upper-bound both delay and errorrate by using short-packet data communications. On the other hand, to solve the massive connectivity problem imposed by mURLLC, the unmanned aerial vehicle (UAV)-enabled systems are developed by leveraging their deploying flexibility and high probability of establishing line-of-sight (LoS) wireless links while guaranteeing various QoS requirements. In addition, the age of information (AoI) has recently emerged as a new QoS performance metric in terms of information freshness. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning over 6G standards has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support mURLLC over UAV-enabled 6G wireless networks in the finite blocklength regime. Specifically, first, we develop the UAV-enabled 3D wireless networking models with wireless-link channels using FBC. Second, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Third, taking into account the peak AoI violation probability, we formulate and solve the AoI-driven ε -effective capacity maximization problems to support statistical delay and error-rate bounded QoS provisioning. Finally, we conduct the extensive simulations to validate and evaluate our developed schemes.

The new and important service class of massive Ultra-Reliable Low-Latency Communications (mURLLC) is defined in the 6G era to guarantee very stringent quality-of-service (QoS) requirements, such as ultra-high data rate, super-high reliability, tightly-bounded end-to-end latency, etc. Various 6G promising techniques, such as finite blocklength coding (FBC) and Terahertz (THz), have been proposed to significantly improve QoS performances of mURLLC. Furthermore, with the rapid developments in nano techniques, THz wireless nano-networks have drawn great research attention due to its ability to support ultra-high data-rate while addressing the spectrum scarcity and capacity limitations problems. However, how to efficiently integrate THz-band nano communications with FBC in supporting statistical delay/error-rate bounded QoS provisioning for mURLLC still remains as an open challenge over 6G THz wireless nano-networks. To overcome these problems, in this paper we propose the THz-band statistical delay/error-rate bounded QoS provisioning schemes in supporting mURLLC standards by optimizing both the transmit power and blocklength over 6G THz wireless nano-networks in the finite blocklength regime. Specifically, first, we develop the FBC-driven THz-band wireless channel models in nano-scale. Second, we build up the THz-band interference model and derive the channel capacity and channel dispersion functions using FBC. Third, we maximize the ϵ-effective capacity by developing the joint optimal resource allocation policies under statistical delay/error-rate bounded QoS constraints. Finally, we conduct the extensive simulations to validate and evaluate our proposed schemes at the THz band in the finite blocklength regime.

Inspired by the new and dominating traffic services - ultra-reliable and low latency communications (URLLC), finite blocklength coding (FBC) has been developed to support delay and error-rate bounded quality-of-services (QoS) provisioning for time-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information. Since the status updates normally consist only of a small number of information bits but warrant ultra-low latency, exploring AoI in the finite blocklength regime creates another promising solution for supporting URLLC services. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning in the finite blocklength regime has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the AoI-driven statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support URLLC in the finite blocklength regime. First, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Second, we characterizes the upper-bounded peak AoI violation probability. Third, we formulate and solve the peak AoI violation probability minimization and E-effective capacity maximization problems to support our proposed statistical delay and error-rate bounded QoS provisioning. Finally, we conduct the simulations to validate and evaluate our developed schemes in the finite blocklength regime.

Wildcard searchable encryption is an advanced variant of searchable encryption that can simultaneously maintain the searchability and confidentiality of the encrypted data. The wildcard searchable encryption outperforms the standard one for the fact that the users can use it to search the desired data even with the inexact keywords. Considering the millisecond level response time in the era of 5G, there are higher demands on the efficiency and accuracy that may be a pair of contradictions in wildcard searchable encryption. To improve the efficiency without sacrificing the accuracy, we put forward a novel scheme, tree-based index scheme (TBIS), through filtering the search results step by step instead of enumeration in the prior works and in the instantiation of TBIS, the search time drops sharply to the millisecond level. By using more kinds of characters, the accuracy of search result is improved visibly. TBIS achieves nonadaptive security that is indistinguishable against chosen character set attacks proposed in this paper. The security criteria can capture the relationship among characters, keywords and documents. At last, we put forward a frame structure in machine learning as an application of the proposed scheme.

In supporting the new 6G standard traffic services-massive ultra-reliable low-latency communications (mURLLC), several advanced techniques, including statistical delay-bounded quality-of-service (QoS) provisioning theory and finite blocklength coding (FBC), have been developed to upper-bound both delay and error-rate for time-sensitive multimedia applications. On the other hand, cell-free (CF) massive multi-input multioutput (m-MIMO), where a large number of distributed access points (APs) jointly serve a massive number of mobile devices using the same time-frequency resources, has emerged as one of the 6G key promising techniques to significantly improve various QoS performances for supporting mURLLC. However, it is challenging to statistically guarantee stringent mURLLC QoS-requirements for transmitting multimedia traffics over CF m-MIMO and FBC based 6G wireless networks. To overcome these problems, we develop analytical models to precisely characterize the delay and error-rate bounded QoS performances while considering non-vanishing decode-error probability for CF m-MIMO based schemes. In particular, we develop FBC based system models and apply the Mellin transform to characterize arrival/service processes for our proposed CF m-MIMO modeling schemes. Then, we formulate and solve the delay violation probability minimization problem and obtain the closed-form solution of the optimal rate adaptation policy for each mobile user over 6G CF m-MIMO mobile wireless networks in the finite blocklength regime. Our simulation results validate and evaluate our proposed schemes for statistical delay and error-rate bounded QoS provisioning.

One of most important techniques for enabling the sixth-generation (6G) mobile wireless network lies in how to efficiently guarantee various stringent quality-of-service (QoS) performance-metrics to support the emerging massive Ultra-Reliable Low-Latency Communications (mURLLC) in 6G. Correspondingly, finite blocklength coding (FBC) has been developed as an effective technique to significantly improve various QoS indices for mURLLC through implementing short-packet communications. On the other hand, Terahertz (THz) band wireless nano-communications have been widely envisioned as a promising 6G technique to efficiently support utra-high data-rate (up to 1 Tbps). One of the major constraints over THz-band nano-networks is the severely limited energy that can be accessed by nano devices. Towards this end, various novel energy harvesting (EH) mechanisms have been proposed to remedy the energy scarcity problem. However, how to accurately characterize the relationships among THz wireless channels, energy consumption, and EH models for FBC based nano communications remains a challenging problem to support statistical delay and error-rate bounded QoS provisioning over FBC based 6G THz wireless nano-networks. To overcome these challenges, in this paper we propose optimal resource allocation policies to achieve the maximum ε-effective capacity in the THz band over FBC-EH-based nano-networks. Particularly, we establish nano-scale system models and characterize wireless channel models in the THz band using FBC. In order to support statistical delay and error-rate bounded QoS provisioning, we formulate and solve the ε-effective capacity maximization problem under several different EH constraints for our proposed schemes. Simulation results are included, which validate and evaluate our proposed schemes in the finite blocklength regime.

Massive ultra-reliable and low latency communications (mURLLC) has emerged as new and dominating 6G-standard services to support statistical quality-of-services (QoS) provisioning for delay-sensitive data transmissions. To measure the freshness of updated information, age of information (AoI) has recently formed as the new dimension of QoS metric. Since status updates usually consist of a small number of information bits but warrant ultra-low latency, integrating AoI withfinite blocklength coding(FBC) creates an alternative promising solution for mURLLC. On the other hand, to solve the massive connectivity issues imposed by mURLLC,unmanned aerial vehicle(UAV) has been developed to significantly enhance the line-of-sight (LOS) coverage while guaranteeing various QoS requirements. However, how to efficiently integrate the above new techniques for statistical delay and error-rate bounded QoS provisioning in UAV systems has been neither well understood nor thoroughly studied. To overcome these challenges, we propose FBC based statistical delay and error-rate bounded QoS provisioning schemes which leverage AoI as a key QoS provisioning technique for mURLLC over UAV mobile networks. First, we develop FBC based UAV system models. Second, we build up AoI-metric based modeling frameworks to upper-bound peak AoI violation probability using FBC. Third, we formulate and solve FBC based peak AoI violation probability minimization problem. Forth, we jointly optimize peak AoI violation probability and$\epsilon $-effective capacity and characterize their tradeoffs. Finally, our simulations validate and evaluate our developed schemes.

In this paper, we develop a novel super-resolution block-sparse channel estimation approach for uplink massive multi-input multi-output (MIMO) based 5G mobile wireless networks. We first introduce a pattern-coupled Bernoulli-Gaussian (PC-BG) prior to characterize block-sparse channels resulting from a small number of scatterers with a small range of angular spread in the angular domain. Then, we propose a Bayesian inference method to infer the channel vector as well as its hyperparameters associated with the PC-BG prior. Specifically, the proposed algorithm is developed within an expectation-maximization (EM) framework and integrated with the generalized approximate message passing (GAMP) technique of approximating the intractable posterior distribution. Finally, instead of adopting some predefined basis/dictionary, such as a discrete Fourier transform (DFT) basis, we propose to learn the offgrid gap between sampled grid-point and true angle of arrivals (AoAs) iteratively for better sparse channel representation, which thus can improve the sparse channel recovery performance. Our numerical analyses validate and evaluate the effectiveness of our proposed scheme.

In smart grid wireless neighborhood area networks (WNANs) supported by 802.15.4g smart metering utility network (SUN), the a smart meter (SM) wireless transceiver pair using multi-rate and multi-regional orthogonal frequency division multiplexing (MR-OFDM) physical layer (PHY) is highly affected by aggregate interference which can be modeled by symmetric alpha-stable Sα S noise. We propose a two stage cross-correlation (TSCC) symbol timing scheme for MR-OFDM synchronization. The proposed scheme employs a simple sliding-window clipping (SWC) method to mitigate the noxious noise and utilizes a peak balancing algorithm for timing metric optimization. Our conducted simulation results show the proposed scheme can improve the mean squared error (MSE) performance significantly in the low signal-to-noise ratio (SNR) region with low complexity as compared to the other conventional schemes, which is suitable for real-world SM wireless transceiver design.

Underwater wireless optical communication (UWOC) has been a potential technology for short-distance, high-speed data communication, which is capable to enable communications among autonomous underwater vehicles (AUVs), underwater sensors, submarines, etc. In AUV marine exploration, using laser diodes (LDs) for AUV acquisition is a novel scheme and has not been fully explored yet. We propose an optical wireless network employing multiple LDs for cooperative scanning to reduce the acquisition time while maintaining the same acquisition probability as the single laser. Also, we derive the analytical acquisition probability based on beam spread function (BSF) model. We conducted extensive experiments and showed that the analytical results we obtained agree well with the simulations. Using a single laser source to meet a high acquisition probability and a moderate acquisition time can be realized by selecting an appropriate scan interval. Moreover, the AUV can be captured in a very short time by cooperative scanning using multiple lasers.

While 5G is being widely deployed around the world, the efforts from both academia and industry have started to investigate various promising 6G techniques, among which Terahertz (THz) systems have drawn much research attention. Recent developments in nanotechnology have enabled electromagnetic nano-communications in the THz band for supporting very large bandwidths with ultra-high data rates over 6G big-data-driven nano-networks. One of the major bottlenecks over such networks is the very limited energy that can be accessed by nano devices. Towards this end, novel energy harvesting (EH) mechanisms have been proposed to remedy this energy scarcity problem. However, how to accurately model and characterize the relationships among THz-band wireless channel, energy consumption, and EH models still remains a challenging and open problem. To solve the abovementioned problems, we propose to develop a joint optimal resource allocation policy for self-powered nano devices to achieve the maximum channel capacity in the THz band over EH-based nano-networks. Particularly, using the Time-Spread On-Off Keying (TS-OOK) modulation mechanism, we establish the wireless communication and EH models in the THz band. Then, we formulate and solve the channel capacity maximization problem under several different constraints for our proposed THz-band EH-based schemes. Simulation results are included, which evaluate and validate our proposed EH-based nano-communication schemes in the THz band.

While 5G is being widely deployed around the world, the efforts from both academia and industry have started to propose and investigate various promising 6G techniques, among which Terahertz (THz) wireless ad hoc networks have drawn much research attention. With the recent development in nanomaterials, THz systems have been developed to support rapidly increasing demand for ultra-high data rates while addressing the spectrum scarcity and capacity limitation problems of current wireless communication systems. Towards this end, THz wireless techniques have been envisioned as one of the key technologies of 6G wireless networks, which boosts the range of applications of nanotechnology. However, due to the complexity in accurately characterizing THz-band wireless channels and interference models in the nanoscale scenarios, a number of technical challenges, such as capacity and mutual information modelling problems, need to be overcome for achieving such ultra-high-speed data rates in the THz band. To solve the above problems, we propose to maximize the mutual information in the THz band over wireless ad-hoc nano-networks. Particularly, we establish THz-band nano-communication system models. Then, we characterize the interference model and formulate and solve the mutual information maximization problem for our proposed THz-band nano-communication schemes. Simulation results are included, which validate and evaluate our proposed schemes in the THz band.

Among the promising 6G candidate techniques to support big-data mobile wireless networks, the cell-free massive multi-input-multi-output (CF-M-MIMO) technique has received a great deal of research attentions, where a large number of distributed single-antenna access points (APs), whose ensemble forms a distributed massive MIMO array, simultaneously and jointly serve the single-antenna mobile users. However, CF-MMIMO results in the low efficiencies for big-data mobile networks when the size of the served area increases. To overcome this challenge, the user-centric approach has been integrated with CF-M-MIMO such that APs only serve a selected subset of mobile users rather than all of them. However, the performance of this approach cannot outperform the traditional CF-M-MIMO in overloaded cases, where the number of mobile users is larger than that of APs. To efficiently implement the big-data aware usercentric cell-free massive MIMO system and enhance the spectral and energy efficiencies while reducing the interference, in this paper we propose an efficient non-orthogonal multiple access (NOMA) and user-centric based CF-M-MIMO scheme over 6G mobile wireless networks. Our proposed scheme develops a more efficient central-processing-unit based bipartite graph matching algorithm to select the optimal mobile users served by each AP. Then, we propose the NOMA-aided power allocation and pilot cluster assignment schemes. The numerical results show that our proposed schemes outperform the existing schemes without applying the NOMA technique in terms of mitigating the interference and enhancing the energy efficiency.

Time-sensitive multimedia services and applications aim at satisfying users' stringent requirements on the delay-bounded quality-of-service (QoS). The statistical delay-bounded QoS provisioning theory has been developed to guarantee a given delay-bound with a small violation probability in multimedia wireless networks. The age of information (AoI) theory characterizes the freshness of information, which is the time-difference between the current time and the time-stamp of the latest observation, and thus, is another important theory to analyze the information latency in multimedia wireless networks. This paper proposes to integrate the statistical delay-bounded QoS provisioning theory with the age of information theory to investigate information latency and to improve the delay-bounded QoS performance over a stationary and ergodic first-come-first-serve M/M/1 channel in multimedia wireless networks. First, we define an AoI-exponent to measure the decaying rate of the violation probability for the maximum allowed AoI and characterize the relationship between the key parameter QoS-exponent in statistical delay-bounded QoS provisioning theory and the AoI-exponent in AoI theory. Second, we also derive a closed form expression of the AoI-based effective capacity, as a function of both the QoS-exponent and the AoI-exponent, to measure the optimal constant information updating rate for a given service process to support the statistical-bounded AoI guarantee. Third, we jointly optimize the statistical delay-bounded QoS and statistical upper-bounded AoI schemes. Finally, we validate and evaluate AoI based statistical delay-bounded QoS provisioning schemes over multimedia mobile wireless networks through numerical analyses.

Data center networks are expected to enable massive connectivity for a wide range of applications. The traditional way of providing security services for data center is difficult to support these applications flexibly and effectively, because supporting security services will inevitably result in the additional networking operations and complexities, imposing the extra network end-to-end delay and latency. Consequently, the emergent-secure applications in data center networks have extremely stringent delay-bounded quality of service (QoS) requirements for data processing. To support the latency critical service, current secure data center systems employ cache partitioning techniques to share the last level cache (LLC) and to guarantee the low-tail-latency QoS. However, the conventional way-partitioning scheme can only provide a constrained number of partitions, which cannot satisfy the latency critical applications' requirement. To overcome this difficulty, we propose the HiCa scheme, a hierarchical cache partitioning technique, to significantly mitigate the tail-latency and improve performances of commodity multicore with waypartitioned caches without operating system changes. Our proposed HiCa scheme groups applications into clusters according to their cache behavior compatibility, and then, partitions the entire cache into several layers and assigns each application cluster into one layer. We also derive the ratio of the number of instructions per cycle (IPC) of using our proposed HiCa scheme to evenly partitioning the cache, in the scenarios of all instructions being cached with the same probability and instructions being cached according to accessing probability. The performance evaluation results show that our proposed HiCa scheme can outperform the conventional cache partitioning technique and can efficiently mitigate the tail-latency in multicore.

In this paper, to investigate sparse channel estimation in OFDM communication systems, we propose a novel binary particle swarm optimization (BPSO) based pilot pattern design scheme and develop an efficient sparse Bayesian learning (SBL) scheme for sparse channel recovery. First, through modifying the mutual incoherence property (MIP) criterion, we outline a new penalty function for optimizing pilot pattern design, which comprehensively takes into account the overall coherence of the measurement matrix. Second, we modify the conventional BPSO algorithm by proposing a new adaptive inertia weight scheme, in which the inertia weight varies with the current state of particle swarm and the number of iterations. Furthermore, we map the pilot pattern design into the framework of the modified BPSO algorithm. Third, we develop a partitioned matrix iterative mechanism to compute matrix inversion in each iteration involved in SBL techniques. Finally, our numerical and simulation results illustrate the efficacy of the BPSO based pilot pattern design scheme and our proposed SBL algorithms in terms of mean-square estimation (MSE) error performance.

We address the problem of sparse channel representation for downlink channel estimation in multi-user frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Existing methods typically adopt discrete Fourier transform (DFT) matrix as a sparse basis to represent sparse channels. However, a sparse basis constructed through dictionary learning method has proven to have strong sparse channel representation. In this work, we develop a discriminative dictionary learning-based sparse channel representation for downlink channel estimation in a multi-user FDD massive MIMO systems. Considering partially shared support between near users, we present a new discriminative dictionary learning (DDL) method for sparse channel representation, based on which the channel estimation scheme is developed. Compared with learning a shared dictionary for all users, it can provide a better representation, thus improving the performance of recovery in the compressive sensing process. Numerical results demonstrate the superior performance of discriminative dictionary as compared to the shared dictionary in terms of normalized mean square error (NMSE) and symbol error rate (SER).

We address secure resource allocation for the energy harvesting (EH) based 5G cooperative cognitive radio networks (CRNs). To guarantee that the size-limited secondary users (SUs) can simultaneously send the primary user's and their own information, we assume that SUs are equipped with orthogonally dual-polarized antennas (ODPAs). In particular, we propose, develop, and analyze an efficient resource allocation scheme under a practical non-linear EH model, which can capture the nonlinear characteristics of the end-to-end wireless power transfer (WPT) for radio frequency (RF) based EH circuits. Our obtained numerical results validate that a substantial performance gain can be obtained by employing the non-linear EH model.

In orthogonal frequency division multiplexing (OFDM)-based smart grid wireless neighborhood area networks (WNANs), the smart meter communication system is effected by impulsive noise. We propose to apply the additive white symmetric alpha-stable noise (AWS$\alpha $SN) model for characterizing the aggregate impulsive noise generated in WNAN. Because of impulsive noise, the OFDM-based system is difficult to eliminate the symbol timing offset (STO) and carrier frequency offset (CFO) completely, where the residual STO and CFO may further aggravate performance degradation. To evaluate the performance of smart meter system in real-world, we derive the analytical symbol error rate (SER) expressions for OFDM-based smart meter receiver under AWS$\alpha $SN channel with imperfect synchronization. The conducted simulation results show that our derived analytical SER performances agree well with those of the simulated results under various STO and CFO. Our work is also expected to shed light on designing novel synchronization methods for OFDM systems under impulsive noise scenarios.

The sequential hypothesis testing technique has been broadly applied in a large number of engineering applications because it provides the efficient rules to make a decision of accepting one of hypotheses at any stage of the experiment. One important application is the optimal caching scheme design in 5G and beyond mobile wireless networks through observing the data contents requested by mobile users as an experiment and making a decision on what distribution of the requested data-content is. Making this decision, cache stations in mobile wireless network are able to estimate the popularity of requested data contents in the future and thus proactively cache the popular data contents in nearby mobile users to avoid the retransmission and its delay for the same data, optimizing the time-sensitive data download services. Towards this end, we model the optimal caching as the estimation strategy problem of future data popularity through developing the optimal stopping and decision rules under the Zipf sequential hypothesis testing. First, we show that our developed Zipf sequential hypothesis testing is exponentially bounded. Then, we derive the lower bound of the stopping time. Finally, we derive the closed-form solution of optimal stopping and decision rules for sequential hypothesis testing under the Zipf distribution.

As a new and dominating 6G mobile-networks' service class for time-sensitive traffics, massive ultra-reliable and low latency communications (mURLLC) has received tremendous attention. One of key 6G enabling-techniques for achieving mURLLC lies in how to efficiently support statistical delay and error-rate bounded quality-of-services (QoS) provisioning for real-time data-transmissions over time-varying wireless networks. Towards this end, several emerging wireless techniques, including finite blocklength coding (FBC), hybrid automatic repeat request with incremental redundancy (HARQ-IR) protocol, millimeter wave (mmWave), cell-free (CF) massive multiple-input multiple-output (m-MIMO), etc., have been shown to be 6G promising enablers to significantly improve various QoS performances. However, integrating these techniques with the statistical delay and error-rate bounded QoS provisioning theory for mURLLC has imposed many new difficulties not encountered before. To overcome these challenges, in this paper we propose the statistical delay-and-error-rate-bounded QoS provisioning system architecture over mmWave user-centric CF m-MIMO and FBC-HARQ-IR based 6G wireless networks. First, we establish the comprehensive system models by accurately characterizing the integrations of above-described 6G promising techniques with statistical QoS provisioning theory. Then, we integrate FBC with HARQ-IR protocol to derive the channel capacity as a function of error probability. Finally, we obtain the closed-form expressions for effective capacities under our proposed schemes. We also conduct a set of simulations to validate and evaluate our proposed FBC-HARQ-IR based mmWave user-centric CF m-MIMO schemes.

Dynamic Searchable Symmetric Encryption for Multiuser (M-DSSE) is an advanced form of symmetric encryption. It extends the traditional symmetric encryption to support the operations of adding and deleting the encrypted data and allow an authenticated group of data users to retrieve their respective desired encrypted data in the dynamic database. However, M-DSSE would suffer from the privacy concerns regarding forward and backward security. The former allows an attacker to identify the keywords contained in the added data by lunching file-injection attacks, while the latter allows to utilize the search results and the deleted data to learn the content. To our knowledge, these privacy concerns for M-DSSE have not been fully considered in the existing literatures. Taking account of this fact, we focus on the dynamic searchable symmetric encryption for multiuser meeting the needs of forward and backward security. In order to propose a concrete scheme, the primitives of Pseudorandom Functions (PRF) and the Homomorphic Message Authenticator (HMAC) are employed to construct the inverted index and update the search token. The proposed scheme is proven secure in the random model. And the performance analysis shows that the proposed scheme achieves the enhanced security guarantees at the reasonable price of efficiency.

As compared with 2D wireless camera sensor networks (WCSNs), 3D WCSNs can capture more accurate and comprehensive information for exposure-path prevention in supervisory and military applications. However, 3D WCSNs impose many new challenges for energy-efficiency and interference-mitigation subject to required coverage rate constraint due to extensive power consumption over time-varying wireless channels. To overcome the above-mentioned problems, in this paper we propose the AQ-DBPSK/DS-CDMA (alternating quadratures differential binary phase shift keying/direct-sequence code division multiple access) based cooperative MIMO energy-efficient and interference-mitigating scheme under the constraint of optimal tradeoff between power consumption and coverage rate over multi-hop clustered WCSNs. In particular, we build sensing models to characterize the minimum coverage rate constraint and formulate the exposure-path prevention problem using the percolation theory. Then, we derive the critical density of camera sensors subject to minimum exposure-path prevention probability constraint. We develop the AQ-DBPSK/DS-CDMA scheme and also derive the optimal data rate to optimize transmit-power and data-rate trade-off. By deriving the critical density of camera sensors over 3D WCSNs, we apply the cooperative MIMO based NEW LEACH architecture for our multi-hop cooperative MIMO scheme. Also conducted is a set of simulations which show that our proposed scheme can outperform other existing schemes in terms of energy efficiency and interference-mitigation over multi-hop 3D clustered WCSNs.

In the last few decades, the statistical delay- bounded quality-of-service (QoS) technique has been proposed and investigated to support the delay-bounded 5G multimedia wireless services In addition, the energy harvesting (EH) based systems, which enable the mobile devices to harvest energy from various external sources, have been designed to address the energy scarcity problem. Researchers have recently integrated EH based systems with short-packet communications for their potential benefits of small payloads and low latency to support reliable multimedia data transmissions. Under the short delay requirements for the low-latency 5G multimedia wireless services, it is not ideal to apply the traditional Shannon capacity for modeling the data rate under the error-rate bounded in the non- asymptotic regime. Accordingly, researchers have developed the finite wireless data transmission rate in the non-asymptotic regime. However, it is challenging to determine the convexity of the effective-capacity maximization problem subject to the statistical delay-bounded and error-rate bounded QoS constraints due to the complexity of analyzing the maximization problem in the non- asymptotic regime. To overcome these challenges, we design the EH based cross- layer optimization scheme in supporting the statistical delay-bounded and error-rate bounded QoS requirements in the finite blocklength regime in the following steps. First, we characterize the EH based system models under finite blocklength coding (FBC). Second, we derive and analyze the convexity of the /spl epsilon/-effective capacity maximization problem for our proposed EH scheme in the finite blocklength regime. Finally, we conduct several simulation evaluations which validate our proposed EH scheme under the statistical delaybounded and error-rate bounded QoS constraints in the finite blocklength regime.

The integration of millimeter wave (mmWave) and multiple-input and multiple-output (MIMO) techniques has been designed to provide reliable communications with large degrees of freedom while supporting the explosively growing number of mobile users. Under stringent requirements in terms of latency and reliability, due to the infinite blocklength assumption of the Shannon's capacity result, researchers have investigated new methods to characterize wireless data transmissions considering the block error probability. The finite blocklength coding (FBC) technique has been developed to model the finite blocklength coding rate in the non-asymptotic regime while supporting short-packet communications over 5G wireless ad-hoc networks. However, because of the design complexity when characterizing the second-order coding rate over mmWave MIMO based wireless channels while being integrated with FBC, how to accurately derive the finite blocklength coding rate over mmWave MIMO wireless fading channels is still an open problem over 5G wireless ad-hoc networks. To tackle the above-mentioned challenges, we propose and develop a system model that can efficiently integrate mmWave-MIMO techniques with finite blocklength coding over 5G wireless ad-hoc networks. In particular, we derive system equations that characterize the foundational informationtheoretic relationship between the finite blocklength channel capacity and the coding rate over our proposed mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime. Also conducted is a MATLAB-based performance evaluation, which validates and analyzes our proposed schemes over mmWave MIMO based 5G wireless ad-hoc networks in the finite blocklength regime.

A device-to-device (D2D) wireless ad hoc network architecture enables dynamic self-organizing communications among mobile users who can directly exchange information with their peers without a pre-determined network infrastructure. Moreover, finite blocklength coding (FBC) is the promising candidate technique to support time sensitive multimedia wireless networks services, where mobile users transmit short packets to upper-bound the transmission delay of video/audio traffic. The scaling law technique models the maximum D2D channel capacity as a function of the density of mobile users. Recent studies have integrated D2D wireless ad hoc networks with FBC theory to further improve the performance of 5G wireless ad hoc networks. However, how to model and analyze the capacity of D2D wireless ad hoc networks under the finite blocklength regime is not well understood and has not been thoroughly studied. To overcome these challenges, applying the scaling law technique, we derive upper-bounds on the coding rate of each D2D channel and the number of time slots needed to complete all D2D transmissions. Combining the D2D channel's coding rate with the number of time slots needed for all D2D transmissions, we derive the maximum aggregate throughput for wireless ad hoc networks with all mobile users using D2D communications while mitigating interference. We also develop a model where each D2D channel follows the Nakagami-m distribution, under which we derive the average aggregate throughput and its upper-bound. Finally, we evaluate our derived results in the D2D wireless ad hoc networks over finite blocklength regime through numerical analyses.

Wireless cooperative communications and relay networks can significantly improve the performance of fifth generation (5G) wireless networks, which aim at providing a wide range of time-sensitive multimedia services and applications by satisfying users' stringent requirements on the delay-bounded quality of service (QoS). Finite blocklength coding (FBC) is a promising candidate technique to support time-sensitive services in 5G wireless networks, where mobile users transmit short packets to upperbound the transmission delay of multimedia traffic. In this paper, we derive closed-form expressions for the coding rates of direct and relay transmissions, respectively, under the Nakagami-m fading channel in the finite blocklength regime. We compare decoding error probabilities, coding rates, and outage probabilities between direct and relay transmissions. Our comparison results show that while relay transmission yields a larger coding rate than that of direct transmissions under the Nakagami-m fading channel, the outage probability and error rate of relay transmissions are higher than those of direct transmissions. We also derive a closedform expression for the outage probability for relay transmissions with multiple parallel relays. Finally, we evaluate and validate our derived coding rates and outage probabilities for both direct and relay transmissions in the finite blocklength regime through numerical analyses.

Caching in mobile devices is a new paradigm to revolutionize the traditional data contents (i.e., files) sharing methods in wireless cellular networks. By caching the popular data contents in mobile devices and disseminating these data contents through device-to-device (D2D) communications, the wireless cellular network can improve its area spectral efficiency, save the base station bandwidth consumptions, and reduce the transmission delay of downloading. The caching scheme is closely related to the data popularity prediction, but how to accurately estimate the future popularity profile has not been well understood. Moreover, although the hierarchical caching architecture has been shown to yield more benefits than flat caching strategies, the challenge of designing an optimal hierarchical caching scheme has not been thoroughly addressed. In this paper, we use Neyman- Pearson hypothesis testing mechanism to predict the future data popularity, and also propose and optimal hierarchical caching schemes over D2D wireless ad-hoc networks. The key of Neyman- Pearson hypothesis testing in our proposed scheme is to derive the closed form of the decision threshold, which in a function of costs if choosing the incorrect hypotheses. We formulate these costs as the sum of time durations for content placement phase and content delivery phase. We derive the closed form expressions of these two phases respectively and obtain the closed form of optimal decision rule, which maximizes the cache hitting probability and upperbounds the prediction error probability. Finally, we evaluate and validate our proposed Neyman-Pearson hypothesis testing based hierarchical caching schemes through numerical analyses.

The fifth generation (5G) wireless networks aim at providing a wide range of time-sensitive multimedia services and applications to satisfy users’ stringent requirements on delay-bounded quality of service (QoS). One of the important requirements for 5G multimedia wireless networks is to efficiently support the ultra-reliable and low- latency communications (URLLC). Finite blocklength coding (FBC) is a promising candidate technique to support URLLC services, where mobile users transmit short packets to upper-bound the transmission delay of multimedia traffic. In this paper, we model and analyze the packet-block error rate for URLLC under the Nakagamim fading channel in the finite blocklength regime. Then, we derive the minimum energy per bit for data transmissions in the Nakagami-m fading channel under the constraint of block error rate. To derive closed-form expressions for the minimum energy per bit, we obtain achievability and converse bounds on the minimum energy per bit, and show that these two bounds are equal to each other. Finally, we evaluate our derived minimum energy per bit in the finite blocklength regime through numerical analyses, and compare it with those under the Rayleigh fading channel and the wideband approximation, validating our derived analytical results.

The non-orthogonal multiple access (NOMA) has been designed to significantly enhance the spectral efficiency for the massive connections of the mobile devices. Moreover, to guarantee the short latency requirements for 5G multimedia wireless services, researchers have designed the statistical delaybounded quality-of-service (QoS) provisioning for supporting the video transmissions over the statistically varying wireless channels. Accordingly, researchers have proposed the finite blocklength coding (FBC) techniques to model the relationship between data transmission rate and channel capacity in the non- asymptotic regime while supporting short-packet communications under the QoS constraints. Due to its potential to significantly improve spectral efficiency and reduce the transmission latency, a NOMA system can be exploited while being integrated with FBC to guarantee the QoS requirements for both the latency and reliability over mobile wireless ad-hoc networks. However, due to the complexity of the effective-capacity maximization problem in the non-asymptotic regime, the FBC based NOMA scheme have imposed new challenges for determining the convexity of the optimization problem and deriving optimal resource allocation policies for NOMA subject to the statistical delay-bounded and error-rate bounded QoS constraints. In order to solve the abovementioned problems, we define a new concept of /spl epsilon/-effective capacity and propose a corresponding system architecture model for NOMA system under FBC. In particular, we characterize the FBC based NOMA system models in wireless ad hoc networks. Considering the statistical delay- bounded and error-rate bounded QoS constraints, we formulate and solve the max- min fairness problem under FBC. Simulation results are included, which evaluate and validate our proposed FBC based NOMA scheme subject to the statistical delay-bounded and error-rate bounded QoS constraints.

To support the delay-bounded multimedia services for 5G mobile wireless networks, the statistical quality-of-service (QoS) technique has been developed to jointly guarantee statistically delay-bounded video transmissions over different timevarying wireless channels, simultaneously. On the other hand, as one of the 5G promising candidate techniques, energy harvesting (EH) is designed to solve the energy supply problem while bringing new challenges due to the stochastic nature of the harvested energy in supporting the heterogeneous statistical delay-bounded QoS provisionings. However, due to the unknown dynamics of the distributions for energy and data arrival processes, it is challenging to design the optimal EH and resource allocation policies under the heterogeneous statistical delay-bounded QoS constraints. Towards this end, the reinforcement learning algorithms have been designed to find the optimal EH and resource allocation policies by allowing the mobile users to learn from the different network states and historical behaviors until the optimal response set is reached. To overcome the aforementioned problems, in this paper we propose the learning based algorithm for designing the optimal EH and resource allocation policies while satisfying the heterogeneous statistical delay-bounded QoS constraints over EH based 5G mobile wireless networks. In particular, we establish the EH based system model. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate the effective-capacity optimization problem over EH based 5G mobile wireless networks. Then, we apply the learning based EH algorithm for deriving the optimal resource allocation policy. Also conducted is a set of simulations which validate and evaluate the system performances and show that our proposed learning based EH scheme outperforms the other existing schemes under the heterogeneous statistical delay-bounded QoS constraints over 5G mobile wireless networks.

In order to support delay-bounded multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) technique has been designed to jointly guarantee statistically delay-bounded video transmissions over different time-varying wireless channels, simultaneously. In addition, with short transmission delay requirements for the multimedia data transmissions, the traditional Shannon's capacity is no longer appropriate to characterize the maximum achievable data transmission rate given the block error probability. Towards this end, the finite blocklength coding (FBC) technique has been developed for reliable delay-bounded 5G multimedia mobile wireless networks. Recent results have derived the throughput of FBC subject to statistical delay-bounded QoS constraints given the block error probability. However, it is challenging to characterize the effective capacity for multimedia data transmissions over fading channels while guaranteeing statistical delay-bounded QoS constraints in the finite blocklength regime. To effectively remedy the above-mentioned deficiencies, we propose FBC based cross-layer design while guaranteeing statistical delay-bounded QoS requirements over 5G multimedia mobile wireless networks. In particular, we establish and analyze FBC based wireless network models. Given statistical delay-bounded QoS constraints, we formulate and solve the e-effective-capacity optimization problem with FBC. Also conducted is a set of simulations which validate and evaluate our proposed FBC scheme under statistical delay-bounded QoS constraints.

To support delay-bounded multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) technique has been designed to jointly guarantee statistically delaybounded video transmissions over different time-varying wireless channels, simultaneously. On the other hand, energy harvesting (EH) wireless channels/networks have received a great deal of research attention recently to address the issue of terminals that need to refresh their energy supplies remotely. An EH device is considered as a rechargeable battery which stores the incoming energy from various external sources. As one of the 5G promising technologies, the study of EH systems has brought many new challenges, such as how to characterize the wireless channels for EH systems using finite blocklength coding (FBC). In addition, given blocklength n, error probability e, and c-effective capacity in the finite blocklength regime, the achievable data transmission rate under statistical delay-bounded and error-rate bounded QoS requirements for EH based systems still remains as a challenging and open problem. To overcome the aforementioned problems, we propose FBC based cross-layer design for EH systems in supporting statistical delay-bounded and error-rate bounded QoS requirements over 5G mobile wireless networks. In particular, we establish and analyze FBC based EH system models. Given statistical delay-bounded and error-rate bounded QoS constraints, we derive an approximate lower bound on the data transmission rate in terms of the effective capacity for our proposed EH system in the finite blocklength regime. Also conducted is a set of simulations that validate and evaluate our proposed FBC based EH system under statistical delay-bounded and error-rate bounded QoS constraints.

In this paper, we propose the full-duplex and cloud radio access network (C-RAN) based multi-cell non-orthogonal multiple access schemes over 5G mobile wireless networks. To cope with the severe intra-cell and inter-cell interferences as well as perform the centralized optimization, we adopt the C-RAN architecture, where the baseband processing and resource management are conducted at a central unit (CU). With the goal of maximizing the weighted sum achievable rate, we formulate the sum rate maximization power allocation problem as a non-convex problem. Thanks to the hidden monotonicity structure of the considered problem, the optimal power allocation algorithm is developed by the monotonic optimization method. Besides, we propose another suboptimal algorithm by employing successive convex approximation method to obtain the close-to-optimal solution with a significantly reduced computational complexity. Extensive simulations are conducted to verify the effectiveness of our proposed power allocation schemes, and confirm the superiority of our proposed C-RAN architecture.

The fifth generation (5G) wireless networks are expected to provide a wide range of time-sensitive multimedia services and applications to satisfy users' stringent requirements on delay-bounded quality of service (QoS). Finite blocklength coding theory can efficiently address the issue of delay-bounded QoS constraint guarantees, where mobile users send messages using packets with small numbers of bits to achieve low latency transmissions. In this paper, we employ the Nakagami-m fading model to analyze channel coding performance in the finite blocklength regime, in terms of average block error rate, capacity outage probability, and symbol error probability. We first derive closed-form expressions for upper and lower bounds on the average block error rate. Then, we compare these bounds with the capacity outage probability and show that the average block error rate is larger than the capacity outage probability. We also obtain a lower-bound in closed-form for the symbol error probability under M-ary phase shift keying (MPSK). Finally, we validate and evaluate our derived average block error rate bound, capacity outage probability, and symbol error probability in the finite blocklength regime through numerical analyses.

With the recent developments in energy harvesting (EH) technologies, mobile devices are able to support the wireless multimedia services by harvesting energy from various external sources. As one of the promising technologies to solve the energy scarcity problem, EH schemes have brought many new challenges due to the stochastic nature of the wireless channel and the harvested energy in supporting the statistical quality-of-service (QoS) provisionings. On the other hand, the full-duplex spectrum sensing (FD-SS) scheme has been designed to improve the spectrum efficiency while significantly enhancing the system performance over cognitive radio networks (CRNs). However, due to the unknown dynamics of the channel state information and the energy state information, it is challenging to design the efficient EH based power allocation policies for all the users with different operating modes while guaranteeing the statistical delay-bounded QoS constraints. To overcome the aforementioned problems, in this paper we develop the collaborative learning system model by choosing the optimal operation strategies and power allocation policies through learning from the EH process while satisfying the heterogeneous statistical delay-bounded QoS constraints over CRNs. In particular, we establish and analyze the FD-SS based EH system models over CRNs. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate and solve the max-min fairness effective-capacity optimization problem for the battery-free EH based CRNs. Then, we apply the collaborative learning algorithm for deriving the optimal joint EH based mode selection and power allocation schemes. Also conducted is a set of simulations which evaluate the system performances and show that our proposed collaborative learning based EH scheme outperforms the other existing schemes under the heterogeneous statistical delay-bounded QoS constraints over CRNs.

With the exponentially increasing demand for wireless multimedia services over 5G mobile wireless networks, the statistical quality-of-service (QoS) provisioning has been proven to be able to effectively guarantee the multimedia data transmissions over highly time-varying wireless channels. On the other hand, many research efforts have been focused on various 5G-promising candidate techniques, such as device-to-device (D2D) caching based communications to offload cellar traffics and address the data explosion problem for the next generation wireless networks. Furthermore, in order to enhance the reliability of video delivery without causing too much interference to other mobile users, researchers have applied the coordinated joint transmission techniques for collaborative D2D caching scheme, which enables mobile users to share popular multimedia files within a D2D communication group instead of downloading from remote backhaul networks. Towards this end, one of the key issues lies in the power allocation problems subject to the heterogeneous statistical delay-bounded QoS requirements for the collaborative D2D caching over edge-computing networks. To overcome the aforementioned challenges, in this paper we propose the collaborative D2D caching model and D2D communication schemes over edge-computing networks. Under the heterogeneous statistical delay-bounded QoS requirements, we formulate and solve the effective-capacity optimization problem for our proposed collaborative D2D caching schemes over edge-computing networks. Then, we develop the collaborative D2D-cache matching algorithms by using bipartite graph technique for selecting D2D-caching users to maximize the effective capacity. Also conducted is a set of simulations which evaluate the system performances and show that our proposed collaborative D2D caching schemes outperform the other existing schemes under heterogeneous statistical delay-bounded QoS constraints.

The device-to-device (D2D) communication is an efficient mobile-data offloading technique to significantly improve the cellular base station (BS) spectrum efficiency in the fifth generation (5G) wireless networks by enabling mobile users to directly receive the multimedia data from a nearby mobile users through D2D communication. On the other hand, D2D multimedia-data offloading imposes the new modeling challenges in characterizing the statistical delay-bounded quality of service (QoS) provisioning over two-hop tandem wireless links when a mobile user receives its requested data from the cellular BS and relays it to the next mobile user which is requesting the same data via D2D communications. Thus, one of the most important challenges of the D2D multimedia-data offloading architecture lies in how to satisfy the statistical delay-bounded QoS requirements over the two-hop wireless links which consists of two tandem single-hop links. To tackle these challenges, we propose the overall two-hop wireless link QoS provisioning schemes based on its two individual single-hop statistical QoS requirements. We derive the overall effective capacity's expression of the two-hop tandem wireless links as a function of the single-hop's effective capacities and prove that the overall equivalent effective capacity of the two-hop wireless link is upper bounded by the single-hop effective capacity with the smaller QoS exponent. Moreover, we derive the upper-bound and lower-bound of the equivalent QoS exponent for the overall two-hop wireless links and also show its monotonicity with respect to single-hop QoS exponents. To show the network performance's improvement after integrating the cellular networks and D2D offloading, we compare the scalability of the BS-based cellular wireless networks with and without D2D offloading. We also evaluate and validate our proposed two-hop wireless link statistical delay bounded QoS provisioning schemes over multimedia D2D offloading architecture through numerical analyses.

The current wireless networks have been designed solely for data communication purposes, imposing many new challenges when supporting both power and information transmissions simultaneously. One of these main challenges lies in resource allocations. To overcome these difficulties, we propose the resource allocation schemes to efficiently support wireless power transmission over a relay-assisted full-duplex (FD) wireless network, where the access point (AP) conducts wireless information and power transfer (WIPT) to multiple mobile users in the downlink (DL), and in the meantime, mobile users transmit information to the AP via relays' assistances in the uplink (UL). Under DL WIPT, each mobile user uses the power splitting technique to harvest energy and receive data simultaneously. Aiming at maximizing the minimum (max-min) sum of DL and UL transmit rates among all mobile users, our proposed schemes jointly optimize the allocation of subcarriers and powers, and the selection of relays and power splitting ratios. Also, our proposed schemes consider two scenarios, where the network employs either orthogonal frequency-division multiple access (OFDMA) or non-orthogonal multiple access (NOMA) taking into account both perfect channel state information (CSI) estimation and imperfect CSI estimation cases. We first approximate the formulated non-convex max-min optimization problems as convex optimization problems. Then, we develop an asymptotically optimal algorithm and a suboptimal algorithm for OFDMA case and NOMA case, respectively. Finally, we validate and evaluate the performances of our proposed schemes through numerical analyses.

The statistical delay-bounded quality-of-service (QoS) theory has been developed to efficiently support multimedia transmissions over 5G wireless networks. On the other hand, unlike in Shannon's information-theoretic formalism requiring infinite blocklength, finite blocklength coding (FBC) has recently emerged for error control in the non-asymptotic regime, guaranteeing stringent statistical QoS requirements in terms of both latency and reliability for ultra-reliable low-latency communications (URLLC) in 5G services. Moreover, integrated with FBC, millimeter wave (mmWave) massive multi-input multi-output (m-MIMO) schemes have been designed to significantly improve the performance in guaranteeing delay/error-rate bounded QoS. However, due to the complexity of modeling and solving the optimization problems over mmWave m-MIMO fading channels in the non-asymptotic error-control regime, it is challenging to derive an optimal resource allocation policy for maximizing the ε-effective capacity to guarantee statistical delay/error-rate bounded QoS. To overcome the above problems, in this paper we propose heterogeneous statistical-QoS driven resource allocation policies for mmWave m-MIMO based 5G wireless networks in both asymptotic and non-asymptotic regimes. In particular, we develop an mmWave m-MIMO based 5G wireless networks model to optimize the effective capacity for our proposed schemes. Our simulations show that our proposed schemes outperform the existing schemes in guaranteeing heterogeneous statistical delay/error-rate bounded QoS.

The multimedia transmission represents a typical big data application in the fifth-generation (5G) wireless networks. However, supporting multimedia big data transmission over 5G wireless networks imposes many new and open challenges because multimedia big data services are both time-sensitive and bandwidth-intensive over time-varying wireless channels with constrained wireless resources. To overcome these difficulties, in this paper we propose the information-centric virtualization architectures for software-defined statistical delay-bounded quality of service (QoS) provisioning over 5G multimedia big data wireless networks. In particular, our proposed schemes integrate the three 5G-promising candidate techniques to guarantee the statistical delay-bounded QoS for multimedia big data transmissions: 1) information-centric network (ICN), to derive the optimal in-network caching locations for multimedia big data; 2) network functions virtualization (NFV), to abstract the PHY-layer infrastructures into several virtualized networks to derive the optimal multimedia data contents delivery paths; and 3) software-defined networks (SDNs), to dynamically reconfigure wireless resources allocation architectures through the SDN-control plane. Under our proposed architectures, to jointly optimize the implementations of NFV and SDN techniques under ICN architectures, we develop the three virtual network selection and transmit-power allocation schemes to: 1) maximize single user's effective capacity; 2) jointly optimize the aggregate effective capacity and allocation fairness over all users; and 3) coordinate non-cooperative gaming among all users, respectively. By simulations and numerical analyses, we show that our proposed architectures and schemes significantly outperform the other existing schemes in supporting the statistical delay-bounded QoS provisioning over the 5G multimedia big data wireless networks.

Mobile cloud-computing is a wireless network environment that focuses on sharing the publicly available wireless resources. Wireless network virtualization provides an efficient technique to implement the mobile cloud-computing by enabling multiple virtual wireless networks to be mapped onto one physical substrate wireless network. One of the most important challenges of this technique lies in how to efficiently allocate the wireless resources of physical wireless networks to the multiple virtual wireless network users. To overcome these difficulties, in this paper we propose a set of novel game-theory based schemes to resolve the wireless resources allocation problem in terms of transmit power and wireless spectrum. We formulate this wireless resources allocation problem as the gaming process where each mobile user bids for the limited wireless resources from physical substrate wireless networks, and competes with the other mobile-user players bidding for the same resources. Under our proposed game-theory framework, we develop three types of wireless resources request strategies: price-based strategy, correlation-based strategy, and water-filling-based strategy to allocate wireless resources under three different gaming mechanisms. The extensive simulation results obtained validate and evaluate our proposed schemes.

With the increasing popularity of video delivery among mobile users, the problem of explosive traffic growth becomes more and more serious for the mobile wireless networks. To deal with this problem, in this paper we propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Based on this idea, a Quality of VIDEO (QoV) oriented pricing incentive scheme, namely Vbargain, is proposed to stimulate mobile users to deliver video data collaboratively. More specifically, in our scheme, the network is regarded as a Virtual Market, and the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Driven by the profit-hunting nature of mobile users, the video data is delivered from the source to its destination. In order to gain deep insight into our scheme, we also analyze the overhead and the influence of start-up capital on the performance of video delivered theoretically, and give its lower bound and upper bound. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme and analysis.

This paper investigates the power allocation problems for non-orthogonal multiple access with coordinated direct and relay transmission (CDRT-NOMA), where a base station (BS) communicates with its nearby user directly, while communicating with its far user only through a dedicated relay node (RN). The RN is assumed to operate in either half-duplex relaying (HDR) mode or full-duplex relaying (FDR) mode. Based on instantaneous channel state information (CSI), the dynamic power allocation problems under HDR and FDR schemes are formulated respectively, with the objective of maximizing the minimum user achievable rate. After demonstrating the quasi-concavity of the considered problems, we derive the optimal closed-form power allocation policies under the HDR scheme and the FDR scheme. Then, a hybrid relaying scheme dynamically switching between HDR and FDR schemes is further designed. Moreover, we also study the fixed power allocation problems for the considered CDRT-NOMA systems based on statistical CSI so as to optimize the long-term system performance. The simulations show that our proposed power allocation policies can significantly enhance the performance of CDRT-NOMA systems.

In this paper, we propose a novel three-dimensional (3D) multi-confocal ellipsoid simulation model with uniform planar antenna array (UPA) for massive multiple-input multiple-output (MIMO) communication systems. Firstly, by employing the spherical wavefront, we characterize near-field effects including the angle of arrival (AoA) shifts and Doppler frequency variations in both space and time domains, and we derive the closed-form expressions of impulse responses of the theoretical model. Secondly, we develop a corresponding simulation model with finite and discrete scatterers within a cluster for the theoretical model. Additionally, we develop the cluster evolution algorithm with a 3D extension of our previously proposed scheme for modeling non-stationary properties of clusters. Their impacts on the proposed channel model are investigated via key statistical properties, e.g., the spatial-temporal cross-correlation function. Moreover, we also discuss the impacts of the range of offset angles and the number of scatterers within a cluster on statistical properties of the proposed simulation model. Finally, our numerical and simulation results show that our proposed simulation channel model is able to capture characteristics of massive MIMO channels while well agreeing with the results obtained from the theoretical modeling.

We investigate the resource allocation to enable wireless power transmission over the full-duplex (FD) wireless networks, where an access point (AP) conducts downlink wireless information and power transfer (WIPT) to multiple mobile users, and concurrently receives uplink information from the mobile users. To enable downlink WIPT, we assume that mobile users adopt the power splitting technique to concurrently harvest energy and receive information from the received radio frequency (RF) signals. We maximize the minimum (max- min) rate among all mobile users to optimize their rates and guarantee their rate fairness, where the subcarrier allocation, power allocation, and power splitting ratio selection are jointly optimized. To resolve the formulated non-convex max-min optimization problem, we first convert the objective function which is a D.C. function (namely, difference of two concave functions) into a concave function, and prove that all mobile users need to attain the same optimal rates. Then, we propose an asymptotically optimal algorithm (AORA) and a suboptimal algorithm (SORA) using the convex programming and linear-programming. Finally, we evaluate our AORA and SORA based schemes through numerical analyses.

We investigate the resource allocation to enable wireless power transmission over a full-duplex (FD) wireless network, where an access point (AP) conducts downlink wireless information and power transfer (WIPT) to multiple mobile users, and in the meantime receives uplink information from the mobile users. To enable downlink WIPT, mobile users employ the power splitting technique to harvest energy and receive information from the received radio frequency (RF) signals concurrently. In particular, the system adopts the non-orthogonal multiple access (NOMA), and then multiple mobile users can leverage the same subcarriers for data transmission and reception. We maximize the minimum (max-min) uplink transmit rate among all mobile users to guarantee their fairness, where the subcarrier allocation, power allocation, and power splitting ratio selection are jointly optimized. Due to using NOMA, the formulated optimization problem is non- convex. Consequently, we develop the suboptimal scheme, by converting the non-convex objective function into a concave function and solving the formulated optimization problem using the convex programming and linear-programming. Finally, we validate and evaluate our proposed scheme through the numerical analyses.

As an emerging and promising technique, the mobile edge computing (MEC) can significantly enhance the computational capability and save computing energy of mobiles, by offloading the computation-intensive tasks from the resource-constrained mobiles to the resource-rich MEC servers. However, since mobiles are generally energy constrained, mobile applications may still be interrupted when the energy of mobiles runs out. To overcome this challenge, we propose to integrate energy harvesting (EH) technique, which can enable mobiles to collect recyclable energy from ambient environments, into MEC, and develop the joint computation offloading and resource allocation scheme for the MEC system supporting multiple EH mobiles. In our considered scenario, each mobile first harvests energy from radio frequency (RF) signals emitted by a base station (BS) which is equipped with an MEC server, and then utilizes the harvested energy to execute its own task either locally at the mobile or by offloading to MEC. Moreover, our developed MEC system employs non- orthogonal multiple access (NOMA) so that multiple mobiles can utilize the same system subcarriers for task offloading to improve system performance. We first formulate the computation offloading and resource allocation problem of interest into an optimization problem, aiming to minimize the total task execution time of all mobiles under their strict timely-execution requirements. Then, we develop the joint computation offloading and resource allocation schemes, through which we can dynamically determine: 1) the energy harvesting time for mobiles; 2) the CPU clock frequencies of mobiles which intend local computing on their own; and 3) the set of mobiles which choose data offloading as well as the subcarriers and power allocations for these mobiles. Finally, we validate and evaluate the proposed offloading and resource allocation scheme through numerical analyses.

We consider the problem of joint dynamic WiFi offloading and resource allocation for radio frequency (RF) powered wireless systems assisted by ambient backscatter, where a cellular base station (BS), a WiFi access point (AP), and multiple energy harvesting (EH) mobiles coexist. In the system, each mobile can dynamically access the cellular network or the WiFi network. When the mobile accesses the WiFi network, it can either adopt the ambient backscatter technique to backscatter ambient RF signals for data transfer (i.e., backscatter mode), or harvest energy from ambient RF signals and then utilize the harvested energy to transmit data (i.e., harvest-then-transmit (HTT) mode). When the mobile accesses the cellular network, it can only work in HTT mode, since the communication range of the ambient backscatter technique is limited nowadays which may not be suitable for data transmission in the cellular network. We first formulate two dynamic WiFi offloading and resource allocation problems for the cases without and with concurrent ambient backscatter (i.e., multiple mobiles can backscatter signals concurrently), aiming to minimize the weighted sum of energy consumption and data transmission time of all mobiles. Then, we develop an asymptotically optimal algorithm and a suboptimal algorithm for the cases without and with concurrent ambient backscatter, respectively. Finally, we validate and evaluate the performance of the proposed algorithms through numerical analyses.

In order to effectively guarantee the statistically delay-bounded multimedia services over time-varying wireless channels, the statistical quality-of-service (QoS) technique has been developed over 5G big data mobile wireless networks. On the other hand, as one of the 5G-promising techniques, the wireless caching enabled WiFi offloading technique is shown to be powerful in addressing the data explosion problem and alleviating the network congestion problem in macrocells. Consequently, challenges have been imposed in applying the cooperative caching schemes for maximizing the successful playback probability under statistical delay-bounded QoS constraints. To effectively overcome the above-mentioned problems, we propose the statistical QoS-driven power allocation scheme through applying the cooperative caching enabled WiFi offloading system over 5G big data mobile wireless networks. In particular, under the Nakagami- \textit{m} fading model, we establish the system models for cooperative caching and wireless transmissions. Given the statistical QoS constraints, we derive and analyze the aggregate effective capacity and the successful playback probability under our developed optimal power allocation policies for the QoS-driven cooperative caching enabled WiFi offloading. Also conducted is a set of simulations which analyze and show the priority of our proposed cooperative caching enabled WiFi offloading scheme, compared with the schemes without cooperative caching in terms of effective capacity under statistical QoS constraints over 5G big data mobile wireless networks.

This paper studies the secure resource allocation for an energy harvesting (EH) based cooperative cognitive radio networks (CRN), which consists of one primary user (PU), multiple secondary users (SUs), one dedicated energy source (ES), and one malicious eavesdropper. In the network, the ES not only can wirelessly power all SUs in addition to the PU, but also can relay data for the PU and send jamming signals to the eavesdropper for the PU and SUs. Specifically, the ES is employed to transmit wireless power to all SUs while simultaneously receiving the PU's information signals by operating in full duplex mode in the first transmission phase, forward the PU's data together with SUs and send jamming signals for the PU in the second transmission phase, and then transmit jamming signals to the eavesdropper for all SUs in the third transmission phase. The ES is paid by the PU and all SUs as an incentive to support wireless power transfer and wireless information transmission. We consider the joint power allocation, data transmission time allocation, and power splitting ratio selection for the considered system, with the objective to minimize the total system payment made to the ES, while concurrently guaranteeing the PU's and all SUs' secure quality-of-service (QoS) requirements. We consider both the cases with perfect and imperfect self-interference cancellation (SIC) between the ES's transmitting antenna and receiving antenna. Finally, we validate and verify the performance of the proposed resource allocation algorithms through numerical analyses.

To support the multimedia services over 5G mobile wireless networks, the heterogeneous statistical quality- of-service (QoS) technique has been designed to jointly guarantee the statistically delay-bounded video transmissions over different time-varying wireless channels, simultaneously. On the other hand, as one of the 5G-promising candidate techniques, device-to-device (D2D) technique has been shown to improve both energy efficiency and spectrum efficiency for multimedia communications. However, overuse of the D2D transmissions may cause the unnecessary interferences to the original base-station oriented cellular networks. Consequently, under heterogeneous statistical delay- bounded QoS constraints, in-network caching techniques, clustering algorithms, and resource allocation policies have been proposed for D2D cluster-caching based 5G multimedia wireless networks with new opportunities and challenges. To effectively overcome the above-mentioned challenges, we propose the heterogeneous statistical QoS-driven resource allocation scheme through applying the D2D cluster-caching based system. In particular, under the Nakagami-m fading model, we establish the system models for the dynamic D2D clustering based video stream sharing and the wireless transmissions. Given the heterogeneous statistical QoS constraints, we derive and analyze the aggregate effective capacity under our developed optimal resource allocation policies for the heterogeneous QoS-driven D2D cluster-caching based 5G multimedia mobile wireless networks. Also conducted is a set of simulations which validate and evaluate our proposed D2D cluster-caching based scheme, compared with the other existing schemes in terms of effective capacity under heterogeneous statistical QoS constraints.

Edge computing techniques have been developed to support the exponentially increasing service demands in the fifth generation (5G) networks by bringing the data contents and their corresponding computations/communications to the edge of the wireless networks, which is the area near to mobile users. As one of the promising and enabling techniques in edge computing wireless networks, in- network caching stores the data contents close to mobile users to efficiently reduce the transmission delay for time-sensitive multimedia data contents. However, one of the main challenges for implementing in-network caching techniques lies in how to develop the collaborative caching mechanisms among all caches in the edge of wireless networks to upper-bound the data transmission delay while maximizing the cache hitting rate. In this paper, we propose the inter-tier and intra-tier collaborative hierarchical caching mechanisms over 5G edge computing multimedia mobile wireless networks, where the popular multimedia data contents are selectively cached at different wireless network caching tiers (e.g., at routers, cellular- base stations/WiFi-access-point, and mobile devices, respectively). The inter-tier collaborative hierarchical caching minimizes the average number of hops (including wireless hops and wireline hops) through the collaborative caching across three wireless network caching tiers, and the intra-tier collaborative hierarchical caching maximizes the overall cache hitting rate within the same wireless network caching tier. To optimize the intra-tier collaborative hierarchical caching mechanism, we derive the upper-bound and lower- bound of maximum numbers of device-to-device (D2D) pairs at the bottom tier of wireless network caching. Finally, we use numerical analyses to evaluate and validate our proposed collaborative hierarchical caching mechanisms over edge computing mobile wireless networks.

Airborne mobile wireless networks (AMWNs), which use spacecrafts and aircrafts such as satellites, airships, airplanes, unmanned aerial vehicles, and other high/medium/low-altitude platforms (HAPs/MAPs/LAPs) can efficiently support high dynamic network topologies and weakly connected communication links. Due to the dramatic dynamics of the AMWNs, it is very difficult to provide the deterministic delay-bounded quality of service (QoS) provisioning for time-sensitive real-time traffics (such as video and audio) over the AMWNs. Alternatively, the statistical delay-bounded QoS provisioning provides an efficient way for guaranteeing delay-bounded QoS requirements for real-time traffics over the AMWNs. On the other hand, because of the diversity of real-time traffics, it is highly demanded to consider the heterogeneity of delay-bounded QoS requirements for distinct real-time services under different HAPs/MAPs/LAPs in the AMWNs. In this paper, we establish the heterogeneous statistical QoS provisioning framework to support the diverse real-time services over the AMWNs. In particular, we formulate the optimization problem to maximize the aggregate effective capacity subject to heterogeneous statistical delay-bounded QoS requirements for both downlink and uplink transmissions-based AMWNs groups (AMWNGs) in the AMWNs. We solve the aggregate effective capacity maximization problems and derive the optimal heterogeneous statistical QoS-driven power allocation schemes for the AMWNs. The numerical analyses we obtained verify that our developed optimal heterogeneous statistical QoS-driven power allocation schemes can significantly increase the aggregate effective capacity for the AMWNs than the other existing schemes.

As a crucial step moving towards the next generation of super-fast wireless networks, recently the fifth-generation (5G) mobile wireless networks have received a plethora of research attention and efforts from both the academia and industry. The 5G mobile wireless networks are expected to provision distinct delay-bounded quality of service (QoS) guarantees for a wide range of multimedia services, applications, and users with extremely diverse requirements. However, how to efficiently support multimedia services over 5G wireless networks has imposed many new challenging issues not encountered before in the fourth-generation wireless networks. To overcome these new challenges, we propose a novel network-function virtualization and mobile-traffic offloading based software-defined network (SDN) architecture for heterogeneous statistical QoS provisioning over 5G multimedia mobile wireless networks. Specifically, we develop the novel SDN architecture to scalably virtualize wireless resources and physical infrastructures, based on user's locations and requests, into three types of virtual wireless networks: virtual networks without offloading, virtual networks with WiFi offloading, and virtual networks with device-to-device offloading. We derive the optimal transmit power allocation schemes to maximize the aggregate effective capacity, overall spectrum efficiency, and other related performances for these three types of virtual wireless networks. We also derive the scalability improvements of our proposed three integrated virtual networks. Finally, we validate and evaluate our developed schemes through numerical analyses, showing significant performance improvements as compared with other existing schemes.

In the non-time-slotted cognitive radio networks (CRNs), the synchronization between PUs and secondary users (SUs) cannot be guaranteed, resulting in two challenging problems: the reactivation-failure of PUs and the frequently unexpected hand-offs among SUs. The reactivation-failure of PUs is the incident that the SUs cannot detect the PUs' reactivation when the SUs are occupying the channels to transmit their data in non-time-slotted CRNs. The frequently unexpected handoffs among SUs are the events that the SU cannot distinguish between the PUs' reactivation and the other SUs' contention, thus causing many unexpected hand-offs among SUs, which severely degrade the achieved throughput of SUs in non-time-slotted CRNs. Employing the energy-detection based wireless full-duplex spectrum sensing schemes, the PUs' reactivation-failure problem can be efficiently solved, thus guaranteeing the required throughput of PUs. However, the key of CRNs is not only the throughput-guarantees for PUs, but also the throughput-boosts for SUs. To optimize the throughput of SUs in multichannel non-time-slotted CRNs, in this paper we develop the pilot-based full-duplex spectrum sensing (PF-SS) scheme and the pilot-based medium access control (P-MAC) protocol to not only guarantee the required throughput of PUs, but also significantly increase the throughput of SUs in multichannel non-time-slotted CRNs. Using the PF-SS scheme, the SUs can identify whether the PUs' signal or the SUs' signal, thus significantly reducing the frequently unexpected hand-offs among SUs. Then, based on the PF-SS scheme, the P-MAC protocol can significantly increase the throughput of SUs. We conduct extensive numerical analyses to show that our developed PF-SS scheme and P-MAC protocol can significantly increase the throughput of SUs while guaranteeing the required throughput for PUs in multichannel non-time-slotted CRNs.

We address secure communications over energy-harvesting based OFDMA cooperative cognitive radio networks, where one primary user (PU) cooperates with several secondary users (SUs) in terms of both information transmission and energy harvesting. To improve spectrum utilization and ensure that SU transmitters can harvest as much energy as possible, we suppose that SUs are equipped with orthogonally dual-polarized antennas. Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply power splitting technique to harvest energy from radio frequency signals radiated by the PU transmitter, and then use the harvested energy to concurrently transmit their own and the PU's data. Under the proposed framework, we develop the secure resource allocation schemes for the scenarios when SU receivers are untrusted users, implying that each SU receiver may overhear the PU's and the other SUs' confidential information. For this scenario, which has hardly been studied, we investigate the joint allocation of relays, subcarriers, power splitting ratios, and powers, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU's minimum secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and resource allocation schemes through numerical analyses.

As a critical technique to support the multimedia services - the major traffic in cognitive radio networks (CRNs), the statistical quality-of-service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, in modern CRNs, cooperative spectrum sensing is shown to be able to greatly improve the sensing performance in cognitive radio networks. However, secondary users belonging to different service providers tend to be selfish and allocate their resources independently. Accordingly, challenges have been raised in applying the cooperative game to maximize the total network utility. To effectively overcome the above-mentioned challenges, we propose the QoS-driven power allocation scheme implementing the cooperative spectrum sensing game over CRNs. In particular, under the Nakagami-m channel model, we establish the cooperative spectrum sensing system model. Given the statistical QoS constraints, we analyze the effective capacity and Markov chain model for different sensing scenarios. We propose the QoS-aware cooperative power control game for cooperative spectrum sensing system over CRNs. Also conducted is a set of simulations which evaluate the system performance and show that our proposed resource allocation policy can achieve the optimality under the statistical delay-bounded QoS constraints over cooperative spectrum sensing CRNs.

To support the emerging next generation wireless networks, researchers have made a great deal of efforts in investigating promising techniques in multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the two 5G-promising candidate techniques, the multiple-input and multiple-output (MIMO) based full-duplex (FD) and device-to-device (D2D) can also significantly enhance the performance of statistical QoS for time- sensitive traffics over the 5G mobile wireless networks. However, how to efficiently integrate these advanced techniques in supporting statistical QoS impose many new challenges not met before. To effectively overcome the difficulties, in this paper we propose the QoS-driven power adaptation scheme by applying Quadrature- OFDMA (Q-OFDMA) to implement MIMO FD D2D based multimedia services in 5G mobile wireless networks. In particular, under the Nakagami-m channel model, we establish the PHY-layer Q-OFDMA system model and FD D2D model. Given the statistical QoS constraint, we derive and analyze the effective capacity under our proposed optimal power adaptation policy over 5G mobile wireless networks. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of self-interference cancellation to efficiently implement the statistical QoS over 5G mobile wireless networks.

With the explosive development of the next era for mobile wireless networks, there has been a lot of studies in the promising techniques for multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the 5G-promising techniques, multiple input multiple output-orthogonal frequency-division multiple access (MIMO-OFDMA) based cognitive radio schemes are proposed to significantly improve the system capacity while mitigate the interference for future dynamic spectrum access networks. However, due to the heterogeneity caused by different links of simultaneous traffics over the wireless relay, supporting diverse delay-bounded QoS guarantees for MIMO-OFDMA based cognitive radio networks (CRNs) imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this paper we propose the heterogeneous QoS-driven resource allocation scheme by applying the MIMO-OFDMA based relaying scheme over CRNs. In particular, under the Nakagami-m fading model, we establish the MIMO-OFDMA based system model. Then, given the heterogeneous statistical QoS constraints, we derive and analyze the effective capacity under our developed optimal power-allocation policies for the MIMO-OFDMA based CRNs. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of effective capacity to efficiently implement the heterogeneous statistical QoS over MIMO-OFDMA based CRNs.

We propose an energy-efficient joint bandwidth and power allocation scheme for heterogeneous Long Term Evolution (LTE) and WiFi multi-homing networks, where multi- mode user terminals can transmit their traffic data by employing both LTE base station and WiFi access point. Considering the different scenarios of terminals, we divide the mobile users of interest into two groups based on their locations, i.e., single-homed users and multi-homed users. To improve the energy efficiency (EE) performance of networks, we formulate the optimization problem, which maximizes system EE with the constrained bandwidth, battery capacity and the required quality of service. To overcome the difficulties stemming from solving non-linear fractional programming, we use the Dinkelbach's parametric approach to convert the original optimization problem into a convex one. Furthermore, by applying the Lagrange duality principle, we obtain the optimal solutions for both bandwidth and power allocations. The simulation results finally validate our derived theoretical analyses and evaluate the effectiveness of our proposed energy- efficient joint allocation algorithm.

The newly imposed heterogeneous statistical delay- bounded quality of service (QoS) provisioning, which refers to the different/variable delay-bounded QoS guarantees among different wireless links, for the fifth-generation (5G) mobile multimedia wireless networks has received much research attention recently. The heterogeneous statistical delay- bounded QoS provisioning can be classified into two categories: centralized and decentralized heterogeneous statistical delay-bounded QoS provisioning where the delay-bounded QoS requirements are centralized at the base station (BS) and distributed at different mobile user equipments (UEs), respectively. The decentralized heterogeneous statistical delay-bounded QoS provisioning is more challenging than the centralized heterogeneous statistical delay-bounded QoS provisioning. In this paper, we build up the system model for decentralized heterogeneous statistical delay-bounded QoS provisioning in terms of aggregate effective capacity and heterogeneous QoS exponents. Based on this model, we develop the optimal joint bandwidth and power allocations scheme to maximize the aggregate effective capacity of uplink transmissions supporting the heterogeneous statistical delay-bounded QoS provisionings over 5G mobile multimedia wireless networks. The BS allocates the bandwidth based on the QoS exponents of all uplinks while the mobile UEs dynamically allocate the power based on the corresponding instantaneous channel state information (CSI), the corresponding QoS exponent, and the bandwidth allocated by the BS. We conduct the extensive simulations to validate and evaluate our developed optimal joint bandwidth and power allocations schemes, showing that our proposed heterogeneous statistical delay-bounded QoS provisioning scheme can significantly increase the aggregate effective capacity as compared with the homogeneous statistical delay-bounded QoS provisioning schemes.

To support the emerging next era of mobile wireless networks, researchers have made a great deal of efforts in investigating promising techniques in multimedia services - the statistical quality-of-service (QoS) technique, which has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. On the other hand, as the 5G- promising techniques, the full-duplex (FD) technique can also significantly enhance the performance of statistical QoS for real-time traffic over 5G mobile wireless networks. However, due to the heterogeneity caused by different types of simultaneous traffics over the wireless FD relay links, supporting diverse delay-bounded QoS guarantees for wireless FD relay networks imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this paper we propose the heterogeneous QoS-driven resource adaptation scheme by applying the full-duplex relaying scheme. In particular, under the Nakagami-m fading model, we establish the system model for the decode and forward (DF) protocol based FD relay system. Then, we propose the FD based relay selection model. Given the heterogeneous statistical QoS constraints, we derive and analyze the effective capacity under our developed optimal power-adaptation policies for the FD relays over 5G mobile wireless networks. Also conducted is a set of simulations which show that our proposed scheme outperforms the other existing schemes in terms of self- interference cancellation to efficiently implement the heterogeneous statistical QoS over FD relay networks.

We address the resource allocation problem for energy- harvesting (EH) based OFDMA cooperative overlay cognitive radio networks with multiple primary users (PUs) and multiple secondary users (SUs), where PUs and SUs cooperate in terms of both information transmission and energy harvesting. Specifically, SU transmitters first apply the power splitting tech- nique to harvest energy from signals radiated by PU transmitters, and then use the harvested energy to transmit their own and PUs'information. We discuss the joint optimization over relay assignment, subcarrier allocation, power splitting ratio selection, and power control under imperfect channel state information (CSI) conditions, with the objective to maximize SUs' total throughput while guaranteeing PUs' quality-of-service (QoS) requirements. In particular, the direct transmission between each PU transmitter and its corresponding primary receiver is taken into account, and then cooperative transmission with SUs is selected dynamically according to network channel conditions. Although the formulated problem is a non-convex problem with integer variables (e.g., relay assignment variables), we still propose a suboptimal distributed algorithm. Finally, we evaluate and verify the performance of our proposed algorithm through numerical analyses.

As a critical technique to support the multimedia services - the major traffic in cooperative cognitive radio networks (CRNs), the statistical quality-of-service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time- varying wireless channels. On the other hand, in modern cooperative CRNs, the full-duplex spectrum sensing (FD-SS) scheme is designed to be a promising candidate technique for fully utilizing the channel spectrum while significantly enhancing the performance. However, how to efficiently integrate the FD-SS technique in supporting statistical QoS over cooperative CRNs imposes many new challenges not met before. To effectively overcome the above-mentioned difficulties, we propose the QoS-driven resource allocation scheme to implement FD-SS based multimedia services in cooperative CRNs. In particular, under the Nakagami-m channel model, we establish the cooperative spectrum sharing system model. We develop the FD-SS scheme and derive the probabilities of miss detection and false alarm for the proposed FD-SS scheme over cooperative CRNs. Given the statistical QoS constraints, we analyze the effective capacity and our proposed optimal resource allocation policy using the proposed FD-SS architecture over cooperative CRNs. Also conducted is a set of simulations which evaluate the system performance and show that our proposed resource allocation policy can achieve the optimality under the statistical delay-bounded QoS constraints over cooperative CRNs.

We investigate the energy efficiency (EE) maximization problem under the scenario of Device-to-Device (D2D) communications underlaying cellular networks. By jointly considering time-varying channels and stochastic arrivals, we propose a distributed power allocation algorithm for both D2D user groups (DUGs) and cellular user terminals (CUTs) with long- term quality of service (QoS) provision. To effectively deal with the dynamics of D2D based cellular networks, stochastic optimization problems are formulated, so as to optimize the long-term EE of both DUGs and CUTs under the constraints of queue stability, battery capacity and average transmission rate. With weighted sum method and Lyapunov optimization approach, we can sufficiently handle the non-convexity of the original problem by deriving an equivalent one. Through optimal solutions of the problems, each DUG and CUT can manage their power resources more efficiently, which improves their long-term EE performance individually. The simulation results further validate the theoretical analyses and evaluate the effectiveness of our proposed algorithm.

The impulsive noise effect is one of the most dominant factors to cause performance degradation for power-line communication (PLC) links. Most reported literatures characterize such an impulsive noise based on the Bernoulli-Gaussian or Middleton's approach. However those models may not truly depict the noise characteristics of the impulses with stable property. The symmetric alpha-stable (SαS) noise model stems from the generalized central limit theorem (GCLT), which describes practical PLC impulsive noise well. By modeling the bandpass asynchronous impulsive noise as additive white symmetric alphastable noise (AWSαSN), we investigate the analytical symbol error rate (SER) performance of a PLC narrowband system link in smart grid networks. Based on the zero-order statistics, we evaluate the strength of the SαS noise using geometric signal-to-noise ratio (GSNR), and explore the uncoded error performance for the conventional linear receiver. Through extensive experiments and numerical analyses, we show that the analytical SER performance of a PLC link matches the simulation results well, which provides suitable benchmarks and guidance for designation of coded systems.

Peer-to-peer (P2P) cache techniques for information-centric networks (ICN) provide the local data resources services for mobile users by storing the popular data contents in their peer mobile users' cache stations, so that a mobile user can obtain these data contents directly from its peer mobile users instead of the remote service provider. Retrieving the requested data content from the nearby storage locations (i.e., cache stations), the P2P caching technique improves the network performance by reducing the duplicate data transmission traffic. To overcome the difficulties of minimizing the data transmission delay for the P2P caching system, we divide all mobile users in a wireless cell as several P2P caching groups, where a mobile user can obtain its requested data content from a peer mobile user in the same group, or from a peer mobile user in another group. We propose the Markov decision process (MDP) based stochastic optimization scheme in this cache system with dynamic number of peers to derive the optimal lifespan for cached data contents in cache stations. The objective of our proposed stochastic optimization framework is to efficiently minimize the cost function, which jointly measures the cache size/cost and the data content transmission delay, over all P2P caching groups in the wireless cell. To achieve this goal, we apply the finite-state Markov chain to characterize the number of mobile users in each caching group and dynamically adapt the cached data lifespan in cache stations. We also develop an efficient iteration algorithm to implement our proposed MDP scheme. Finally, we validate and evaluate the performance of our proposed schemes through the numerical analyses, which show that our proposed schemes converge to a unit optimal solution.

Cooperative communication is an efficient technique for mobile wireless networks supporting information transmission through relay nodes, which forward the signal received from a source node to its destination. Applying the cooperative communication in wireless networks can significantly improve the network performance. One of the most important challenges is how to efficiently allocate the access opportunity of a relay node to multiple mobile-users while taking into account the delay-bounded quality of service (QoS) for the time-sensitive multimedia data transmissions. To overcome these difficulties, in this paper we propose a novel game-theory based scheme to resolve the relay node allocation problem by measuring the maximum throughput for time-sensitive multimedia data transmission under a QoS requirement as the effective capacity gain. We formulate this one relay allocation problem as a single-relay-auction such that all mobile-user players can decide whether to patriciate in this relay auction, and the mobile-user player who places the highest bid obtains the relay allocation. The mobile-user players who lose or stay out of the auction can only directly transmit information to their destinations. Then, we derive the optimal strategies for the single-relay-auction, maximizing the payoff (effective capacity gain minus costs) in each bid round.The extensive simulation results obtained validate and evaluate our proposed schemes.

Information-centric network technique is developed to reduce duplicate transmissions for popular data contents by adopting caching strategy and enabling mobile users to access popular data contents from cache stations of nearby network gateways. In current information-centric wireless networks, one popular data content can be stored in multiple cache stations and requested by multiple mobile users. One of the main challenges of this multiple-cache-and-multiple- user wireless network lies in how the mobile user obtains a data content from one of these multiple cache stations with the minimum cost while guaranteeing the data transmission quality-of-service (QoS), and which mobile-user should a cache station choose to provide the data content. To overcome these problems, we formulate a many-to-many negotiation game, where each player (a cache station or a mobile user) bargains with all of its negotiation partners (all mobile users and all cache stations, respectively) in terms of desirable prices, aiming at maximizing its individual payoff. In our proposed negotiation game scheme, the payoff for a mobile user is the obtained QoS gain minus corresponding costs, and the payoff for a cache station is the obtained payment from its negotiation partner mobile user minus the cost for providing services. We propose an adaptive concession strategy, such that players can adjust the concession speed and concession amount of their desirable prices according to their current bargain situations in each game round. Then, we mathematically prove that our proposed adaptive concession strategy maximizes players' payoffs and the probability to achieve a negotiation agreement. Finally, we validate and evaluate the performances of our proposed negotiation game scheme and adaptive concession strategy through the numerical analyses.

With the increasing popularity of video delivery among mobile users, the explosive traffic growth problem becomes more and more serious for the mobile wireless networks. We propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Specifically, we propose a QoV (Quality of Video) oriented pricing incentive scheme, namely Vbargain, to stimulate mobile users to deliver video data collaboratively. In our scheme, the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme.

By enabling two adjacent mobile devices to establish a direct link, device-to-device (D2D) communication can increase the system throughput over underlaying wireless networks, where D2D and cellular communications coexist to share the same radio resource. Traditional D2D schemes mainly focus on maximizing the system throughput without taking into account the quality-of-service (QoS) provisioning. To overcome this problem, we develop a framework to investigate the impact of delay-QoS requirement on the performance of D2D and cellular communications in underlaying wireless networks. Then, we propose the optimal power allocation schemes with statistical QoS provisioning for the following two channel modes: 1). co-channel mode based underlaying wireless networks where D2D devices and cellular devices share the same frequency-time resource; 2). orthogonal-channel mode based underlaying wireless networks where the frequency-time resource is partitioned into two parts for D2D devices and cellular devices, respectively. Applying our proposed optimal power allocations into D2D based underlaying wireless networks, we obtain the maximum network throughput subject to a given delay-QoS constraint for above-mentioned two underlaying wireless network modes, respectively. Also conducted is a set of numerical and simulation results to evaluate our proposed QoS-driven power allocation schemes under different delay-QoS requirements.

Since the Information and Communications Technologies (ICT) were designed without taking the energy-saving into account, the unexpected excessive energy consumption of the fourth-generation (4G) and pre-4G wireless networks causes serious carbon dioxide emissions. To achieve green wireless networks, the fifth-generation (5G) wireless networks are expected to significantly increase the network energy efficiency while guaranteeing the quality of service (QoS) for time-sensitive multimedia wireless traffics. In this paper, we develop the statistical delay-bounded QoS driven green power allocation schemes to maximize the effective power efficiency (EPE), which is defined as the statistical-QoS-guaranteed throughput (effective capacity) per unit power, over single-input single-output (SISO) and multipleinput multiple-output (MIMO)-channels based 5G mobile wireless networks. For the SISO-channel based 5G wireless networks, our developed QoS-driven green power allocation scheme converges to the despicking water-filling scheme (despicking channel inversion scheme) when the QoS constraint becomes very loose (stringent). We further develop and analyze the statistical-QoS-driven green power allocation scheme to maximize the EPE over the multiplexing-MIMO based 5G mobile wireless networks. The obtained numerical results show that our developed statistical QoS-driven green power allocation schemes can optimize the EPE over 5G mobile wireless networks, thus enabling the effective implementation of green 5G wireless networks.

Abstract Opportunistic routing (OR) significantly improves transmission reliability and network throughput in wireless mesh networks by utilizing the broadcast nature of the wireless medium. Through the integration of network coding (NC), the complicated coordination to select the best forwarding node (FN) in OR can be bypassed. However, the introduction of NC exacerbates the redundant‐packet‐transmission problem. To mitigate this issue, existing coded OR protocols either adopt the loss‐rate‐based approach, employ orthogonal vectors as coded feedback, or pursue the stream‐based coded OR model. However, these three solutions suffer inaccuracy and obsolescence of the loss‐rate measurement, false‐positive/false‐negative problem, and unavailability of hop‐by‐hop stream‐based OR, respectively. To address the previous problems, we propose a simple but practical coded feedback scheme, cumulative coding coefficient acknowledgement (C3ACK), based on the relevance between forward (coded packets received from upstream nodes) and backward coding traffic (coded packets overheard from downstream nodes), and apply C3ACK to both batch‐based and stream‐based coded OR models in order to prune redundant forward and backward coding traffic. Both testbed evaluation and simulation study show that our code‐pruning schemes can outperform existing approaches in terms of expected throughput and transmission count. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Because of the broadcast and overhearing capability of wireless networks, network coding can greatly improve throughput in wireless networks. However, our investigation of existing inter‐session network coding protocols found that the short‐term unfairness that existed in 802.11‐based medium access control (MAC) protocols actually decreases the coding opportunity, which in turn compromises the throughput gain of network coding. To alleviate the negative impact of this unfairness, we propose a coding‐aware cross‐layer heuristic approach to optimize the coordination of network coding and MAC layer protocol, named FairCoding, which can significantly increase coding opportunities for inter‐session network coding through a fair short‐term traffic allocation for different coding flows. Experiment evaluation shows that the proposed mechanism can bring more coding opportunities and improve the total throughput of wireless mesh networks by up to 20%, compared with the coding mechanism, without considering the negative impact of the short‐term unfairness. Copyright © 2015 John Wiley & Sons, Ltd.

The fifth-generation (5G) communications and wireless networks, which are expected as the next new era of wireless networks, have received much research attention in recent years. The academic and industrial researchers have developed a great deal of 5G candidate techniques to improve the performance of 5G wireless networks, where the most important two metrics are the spectrum efficiency and the quality-of-service (QoS). Jointly using full-duplex (FD) wireless communications and device- to-device (D2D) communications, forming the FD-D2D communications, can significantly increase the spectrum efficiency of 5G wireless networks in frequency/time/space-domains. On the other hand, not only increasing the spectrum efficiency, but also supporting QoS guarantees is very important for 5G wireless networks. However, supporting QoS guarantees for FD-D2D communications imposes the new challenges that we need to provide heterogeneous QoS guarantees for different types of traffics over the same link simultaneously. To overcome the aforementioned problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for FD-D2D communications over 5G wireless networks. In particular, we formulate the optimization problems to maximize the system throughput subject to heterogeneous statistical delay-bound QoS requirements. Then, we convert the resulted non- convex optimization problem into an equivalent convex optimization problem, solving which we can derive the optimal heterogeneous-QoS-driven power allocation scheme to maximize the system throughput while guaranteeing the heterogeneous statistical delay-bound QoS requirements. The extensive simulation results obtained show that our proposed heterogeneous-QoS-driven power allocation scheme can significantly increase the system throughput while guaranteeing heterogeneous statistical delay-bound QoS requirements.

To support the emerging next generation wireless networks, researchers have made a great deal of efforts in investigating various promising techniques, such as full-duplex (FD) multiple-input and multiple-output (MIMO) technique and device-to-device (D2D) communications. FD MIMO technique can practically achieve the theoretical doubling of throughput if the self-interference can be efficiently cancelled. And D2D communication is designed and implemented to significantly enhance the FD communication performance by effectively reducing the overall interference and lowering transmit power over ad hoc networks. However, how to efficiently cancel the selfinterference induced by the FD MIMO transmissions under D2D communications has imposed many new challenges. To overcome the above-mentioned problems, we propose the adaptive power control policy for maximizing channel capacity over FD D2D QOFDMA ad hoc networks. In particular, under the Nakagami-m channel model, we establish the system model for the spatial multiplexing oriented Q-OFDMA system, and apply the selfinterference suppression techniques for FD model. We derive and analyze the energy efficiency for FD system over ad hoc networks. Then, we develop the adaptive power control policy for maximizing the MIMO channel capacity under our proposed spatial multiplexing based Q-OFDMA system over ad hoc networks. Also conducted is a set of simulations which show that our proposed scheme outperform the other existing schemes in terms of energy efficiency and self-interference cancellation over ad hoc networks.

In an orthogonal frequency-division multiplexing (OFDM)-based power-line communication (PLC) system, the background noise is commonly assumed as Gaussian. However this model may not truly depict the effect of the human activities on noise characteristics, leading to system performance overestimation. Plenty of empirical evidences show that the impulsive nature of the PLC background noise can be well modeled by a symmetric alpha-stable (SαS) process. In this paper, we investigate the analytical symbol error rate (SER) performance of M-ary modulation schemes for OFDM-based PLC systems subject to SαS background noise. The strength of the SαS noise is evaluated by geometric signal-to-noise ratio (GSNR) based on the zero-order statistics. We conduct extensive simulation experiments and numerical analyses, showing that under SαS background noise, the fast Fourier transform (FFT) of the received complex baseband SαS noise samples follow another bivariate SαS distribution, and the SER performance of the OFDM system decreases as the employed FFT size grows.

Big data transmission on a wireless network environment focuses on sharing the large volume information data with the effective capacity guaranteed. Wireless network virtualization provides an efficient technique to implement the big data transmission by enabling multiple virtual wireless networks (VWNs) to be mapped onto one physical substrate wireless network (SWN), downloading the information data from SWN. One of the most important challenges of this technique lies in how to efficiently allocate the wireless resources of physical wireless networks to the multiple virtual wireless network users, considering the quality of service for the data transmission. To overcome these difficulties, in this paper we propose a novel auction based scheme to resolve the wireless resources allocation problem in terms of transmit power and wireless spectrum. We formulate this wireless resources allocation problem as an auction process where each mobile user bids for the limited wireless resources from physical substrate wireless networks, and competes with the other mobile-user players bidding for the same resources. First, the mobile users derive their bids for transmit powers, which indicate their willingness to pay for the transmit powers, aiming at maximizing their payoffs (performance-gain minus cost) on each subchannel. Then, according to the derived available transmit power on each subchannel, mobile users derive their bids for the number of subchannels, aiming at maximizing their overall payoffs. The SWN assigns these two types of wireless resources to each mobile user according to the bids for all virtual users. Finally, as the mobile users' bidding processes proceed iteratively, our proposed games are guaranteed to converge to the Nash Equilibrium, where the benefits of SWN and mobile users are both optimized, thus maximizing the aggregate effective capacities for our resources-virtualized big data transmission wireless networks. The extensive simulation results obtained validate and evaluate our proposed schemes.

Wireless network virtualization provides an efficient technique to reduce the cost of overall networks by allowing multiple wireless virtual networks to be mapped onto one physical substrate wireless network. One of its important challenges of this technology is how to efficiently allocate the wireless resources of physical wireless networks to multiple virtual network users. To overcome these challenges, we develop the game- theory based schemes to resolve the resources allocation problem on both buffer/storage space and transmission-rate. Mapping the virtual networks onto the physical wireless network is formulated as the process of bidding for the limited wireless resources from a physical substrate wireless network, and competing with the other mobile-user players, which bid for the same wireless resources of a physical substrate wireless network. Our proposed game- theory schemes work as follows. First, virtual network announces its value function to the physical substrate wireless network, to claim for their required resources (buffer/storage space and transmission bandwidth). Then, the physical substrate wireless network allocates the two types of wireless resources to each virtual wireless network and charges for the provided resources according to the claimed value functions for all virtual users and its current network conditions. Finally our proposed schemes feed back conjectural prices for virtual networks' next-time bid. By accepting the conjectural prices, the game of virtual networks converges the Nash Equilibrium where their long term payoffs are optimized. The simulation results obtained validate and evaluate our proposed schemes through numerical solutions and the simulations.

Recently, both academia and industry are moving their research attention to the fifth-generation (5G) wireless networks - the next new era of wireless networks. The wireless full-duplex transmission, as one of promising candidate techniques for 5G, can significantly boost the spectrum efficiency of the wireless networks, thus providing a powerful thrust to optimize the quality-of-service (QoS) performances for the wireless networks. However, due to the heterogeneity caused by different types of simultaneous traffics over the wireless full-duplex link, supporting QoS guarantees for wireless full-duplex networks imposes the new challenges that we need to provide heterogeneous QoS guarantees for different types of traffics over the same link simultaneously. To overcome the aforementioned problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for bidirectional transmission based wireless full-duplex networks. In particular, we formulate the optimization problems to maximize the system throughput subject to heterogeneous statistical delay-bound QoS requirements. Then, we convert the resulted non-convex optimization problem into an equivalent convex optimization problem, solving which we can derive the optimal QoS-driven power allocation scheme to maximize the system throughput while guaranteeing the heterogeneous statistical delay-bound QoS requirements. The extensive simulation results obtained show that our proposed QoS-driven power allocation scheme for heterogeneous statistical delay-bound QoS requirements can achieve larger aggregate system throughput than the scheme for the homogeneous statistical delay-bound QoS requirement over 5G mobile wireless full-duplex networks.

Motivated by many applications which involve the sparse signals recovery, the joint sparse optimization problem or MMV (multiple measurement vectors) problem has been drawn more and more attentions in recently studies. A special and hot issue in MMV problem is how to find the sparse solutions when not all the entries of the measurements is fully observed, but some of them are missing. Although several works have already focused on this problem and some algorithms have also been proposed to solve the corresponding models, the analysis of recovery ability to the basic model has still not been provided. Thus, this paper presents theoretical analysis of recovery guarantees for joint sparse optimization problem with missing measurements. Simulation results are presented to verify the validity of our theories and also to illustrate the potential applications of our framework.

In a decentralized network where multiple agents exist, each agent takes linearly measurements from the received signal and decodes the corresponding signal by running recovery algorithm at local and also sharing the auxiliary information broadcasted from its neighbors. Motivated by the applications like wireless sensor networks, cooperative spectrum sensing and decentralized event detection in wireless networks, sparse signal recovery or detection in decentralized (distributed) networks has been one of the research focus in wireless network. By exploiting compressive sensing technology, this problem was widely studied in recent years. Although many works have focused on this issue, most of them only consider the situation that all agent (node) measure same signals, like e.g., D-Lasso, DCD-Lasso, which is less suitable for the real wireless network application environment. Thus, this paper proposed a DIRLq (Decentralized Iteratively Reweighted ℓq) algorithm to solve this problem. Different from previous decentralized sparse recovery algorithms like D-Lasso and DCD-Lasso, our algorithm focuses on recovering different signals with joint sparsity structure which were measured in different agents. Besides, although recently proposed DRL1 and DRL2 algorithm have also considered the similar application background, our algorithm presents better performance compared to both of them. Furthermore, we also discuss the convergence behavior of our algorithm. Finally, numerical results are provided to show the effectiveness of our proposed algorithm.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is considered to be one of the most promising technologies for further generation mobile communication systems like 3GPP LTE in recent years. At the same time, as a smart spectrum sharing technology, Cognitive Radio (CR) was also proposed to enhance the utilization of the spectrum usage. Thus, the combination of MIMO-OFDM and Cognitive Radio, MIMO-OFDM based Cognitive Radio technology is treated as a prospect scheme for future dynamic spectrum access network or spectrum sharing system. Since only a finite number of subcarriers are occupied by the primary users (PUs) in CR networks, the secondary users (SUs) can detect the spectrum holes (the unoccupied subcarriers) and opportunistically access those unoccupied spectrum subcarriers. Thus, spectrum sensing or detection is an important component for the implementation of CR. However, in traditional MIMO-OFDM system, the signals received in each antenna are sampled by an individual analog-to-digital converter (ADC), which will lead to a significantly increase of front-end cost for the whole system since multiple ADCs need to be adopted by corresponding to the multiple receiving antennas. Thus, the problem is how to design efficient receiving scheme for reducing the power consume and hardware cost in MIMO system. Considering the sparsity property of the received signals, we proposed a novel spectrum sensing scheme for the MIMO-OFDM based CR network by exploiting compressive sensing technology in this paper. Different to traditional MIMO-OFDM system, by exploiting the sparsity model, the signals received in our receivers are mixed together from multiple antennas and then sampled by a single ADC. Thus, the hardware cost and energy consumption can be significantly reduced in our scheme. Besides, our proposed scheme can detect the spectrum usage without the prior information of sparsity, which is also suitable for the real wireless application environment. Simulation results also show the effectiveness of our proposed scheme.

As compared with 2D wireless camera sensor networks (WCSNs), 3D WCSNs can capture more accurate and comprehensive information for supervisory and military applications. However, 3D WCSNs impose many new challenges for energy-efficiency and interference-mitigation subject to the required coverage rate constraint due to their extensive power consumption for data transmissions and inter-sensor interference over time-varying wireless channels in the 3D WCSNs. To overcome the above-mentioned problems, in this paper we propose the multi-hop cooperative multi-input-multi-output and orthogonal frequency-division multiplexing (MIMO-OFDM) based energy-efficient and interference-mitigating scheme for the 3D clustered WCSNs with the minimum target-object coverage rate constraint. We propose to integrate the cooperative MIMO-OFDM with the new low energy adaptive clustering hierarchy (NEW LEACH) algorithm to increase the spatial diversity of wireless channels, reducing the transmitted power with the constraints of bandwidth and energy in multi-hop WCSNs. In particular, applying the NEW LEACH architecture and using the Nakagami-m model, we develop the cooperative MIMO-OFDM based scheme to implement the energy-efficient and interference-mitigating wireless communications over our multi-hop 3D clustered WCSNs. Then, we model and analyze the performance of our proposed cooperative MIMO-OFDM scheme. Also conducted is a set of simulations which show that our proposed scheme outperform the other existing schemes in terms of energy efficiency and interference mitigation over multi-hop 3D WCSNs.

Underwater acoustic communications have received significant research attention with the increasing ocean explore applications. Due to the frequency-dependent path loss and fading characteristics of the acoustic channel, and scarce available bandwidth, it is more important to employ frequency reuse scheme to improve the spectrum efficiency and the entire network performance. However, the frequency reuse imposes the new and complicated designing tradeoff among co-channel interference on cell-edge users, the overall network throughput, and spectrum efficiency, which has significant impact on the performance for underwater multicell OFDM wireless networks. To overcome these new challenges, in this paper we employ soft frequency reuse (SFR) scheme in underwater acoustic multicell networks to balance the intercell interference mitigation and the spectrum efficiencies while guaranteeing the edge-user's QoS requirements in terms of signal-to-interference-plus-noise (SINR) and outage probability. The extensive simulations validate and evaluate our proposed schemes, showing that the our schemes outperform the other existing frequency reuse schemes for underwater wireless networks in terms of SINR, outage probabilities of cell-edge users, and spectrum efficiency.

Quantum cryptography is one of the most promising technologies for guaranteeing the absolute security in communications over various advanced networks, including fiber networks and wireless networks. In particular, quantum key distribution is an efficient encryption scheme on implementing secure satellite communications between satellites and ground stations. However, it faces many new challenges such as high attenuation and low polarization-preserving capability or extreme sensitivity to the environment. In order to guarantee the quality of service (QoS) provisioning of quantum communications over 3D satellite networks, we need to focus on the security problem and throughput efficiency through correcting the errors resulted from the objective and adversary influences. To overcome these problems, we model the noisy quantum channel and implement an efficient quantum error correction scheme to ensure the security and increase the quantum throughput efficiency in QKD-based satellite networks. The simulation results obtained show that our proposed efficient QEC scheme for QoS guarantee outperforms the other existing quantum error correction schemes in terms of security and the quantum throughput efficiency.

Because of the asynchronization between primary and secondary wireless networks, the synchronization between primary users (PUs) and secondary users (SUs) can be hardly guaranteed in nontime-slotted cognitive radio networks (CRNs). In this paper, we propose a novel framework for multichannel nontime-slotted CRNs, where the PUs randomly access and leave the licensed channels. Since the PUs cannot distinguish between primary and secondary signals, the PUs may sense a busy channel when the PUs start to reactivate during the SUs' transmission, thus generating a collision or entering the backoff stage. To guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs, in this paper, we propose the wireless full-duplex spectrum sensing (FD-SS) scheme for SUs in multichannel nontime-slotted CRNs. Using our developed FD-SS scheme, the SUs can timely sense the PUs' reactivation during the same time when the SUs are transmitting their signals. Then, based on our proposed wireless FD-SS scheme, we further develop and analyze the wireless full-duplex cognitive medium access control (FDC-MAC) protocol for multichannel nontime-slotted CRNs. We conduct extensive numerical analyses, showing that our developed FD-SS scheme and FDC-MAC protocol can efficiently guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs without requiring the synchronization between the PUs and the SUs over the multichannel nontime-slotted CRNs.

To guarantee the real-time transmission for time-sensitive traffic, we need to take delay-bound quality-of-service (QoS) into account when designing the wireless cellular networks. The traditional powerful QoS guarantee technique, called the homogeneous statistical QoS provisioning, assumes that the QoS provisioning of each link can be individually processed. In order to further significantly increase the global system throughput, the available resources of all links need to be jointly controlled, which is typically suitable for downlink transmissions over wireless cellular networks. Under this setup, we need to consider the diverse delay-bound QoS provisionings for different links at the same time, which represents the new heterogeneous statistical QoS provisioning framework and imposes many new challenges not encountered before in wireless networks. To overcome these problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for high-speed downlink transmissions in wireless cellular networks. In particular, we formulate the optimization problem to maximize the downlink throughput subject to heterogeneous statistical delay-bound QoS requirements. For solving this optimization problem, we develop the heterogeneous-QoS-driven power allocation scheme to derive the closed-form solutions which can maximize the global system throughput while guaranteeing the heterogeneous delay-bound QoS for the entire wireless cellular networks. We show that the heterogeneous-QoS-driven power allocation provides more generic framework for downlink transmissions. The extensive simulation results obtained show that our proposed heterogeneous-QoS-driven power allocation scheme can achieve the global optimization of wireless resource efficiency, thus significantly increasing the global system throughput as compared with the homogeneous statistical delay-bound QoS provisioning schemes.

By enabling two mobile devices, which are far away from the base station (BS) and very close to each other, to establish a direct link, device-to-device (D2D) communications can significantly increase the system throughput for wireless cellular networks. Traditional D2D schemes are mainly based on the half-duplex transmission mode. However, the short distance between the two D2D mobile devices can not only increase the received signal-to-noise ratio (SNR) but also increase the signal-to-self-interference-plus-noise ratio (SSINR), which motivates us to apply the wireless full-duplex transmission based D2D communications into wireless cellular networks. To further increase the ergodic capacity of the wireless cellular networks, in the paper we propose the full-duplex D2D communications framework for wireless cellular networks. In particular, we formulate the ergodic capacity optimization problem for full-duplex D2D communications based wireless cellular networks. To solve this optimization problem, we develop the optimal full-duplex power allocation schemes to maximize the ergodic capacity of the wireless cellular networks. Also conducted is a set of numerical and simulation results to evaluate the ergodic capacity gain of our proposed full-duplex power allocation schemes as compared with the traditional half-duplex transmission based D2D communications.

Spectrum sensing, which seeks to detect the unoccupied channels or spectrum holes, is the first task for ensuring the functionality of Cognitive Radio (CR) system. But considering hardware limitation, each CR node can only obtain limited information about the spectrum usage on whole spectrum channel. Thus, by exploiting compressive sensing (CS) technology, collaborative compressive sensing for spectrum sensing was proposed to solve this problem. However, due to channel fading and transmission power limitation, fusion center (FC) usually cannot receive complete measurements from all CR nodes. Thus, now the problem is how to obtain the complete information of spectrum usage from the incomplete measurements. Although previous matrix completion recovery (MCR) algorithm has focused on this issue, it's less applicable in the noise environment and the situation of large number of measurements (observations) are missing. Thus, we propose a new recovery algorithm for spectrum sensing with missing observations in this paper. Different to MCR algorithm, our proposed algorithm needs not to run matrix completion algorithms but can detect the occupied channels from the incomplete observations directly. Moreover, our method outperforms MCR algorithm in both the situations of noise corruption and large number of observations are missing. Furthermore, we proposed a sparsity adaptive based dynamic compressive spectrum sensing algorithm which is aiming at solving the problem of spectrum sensing in the dynamic environment. This dynamic algorithm focus on recovering the recent changes which are the newly occupied channels or the released channels. Compared to previous dynamic compressive spectrum sensing (DCSS) algorithm which can only detect single channel change once a time, our method which can detect multiple channels changes is more suitable for the application. Simulation results will also validate the effectiveness of all our proposed schemes.

In Cognitive Radio (CR), spectrum sensing is an important process for implementing the whole CR system. As each single CR node has a limited search range, collaborative compressive sensing can significantly improve the ability of detecting the spectrum usage for whole CR networks. In this model, each CR node takes the linearly measurements from the powers of all channels. Then the measurements are sent to a fusion center (FC), where the occupied channels can be detected through recovery algorithms by exploiting the joint sparsity property. Although a variety of recovery algorithms for collaborative spectrum sensing and joint sparse optimization methods have been proposed, designing some more efficient algorithms is still staying in demand. Thus, we proposed a new recovery method for collaborative spectrum sensing in this paper, which is named as Joint-SAMP (sparsity adaptive matching pursuit) algorithm. Compared with previous collaborative spectrum sensing methods and joint sparse optimization algorithms, our method presents superior performance. Besides, Joint-SAMP also has the feature of sparsity adaptive, which makes it more suitable for the application in spectrum sensing. Furthermore, we also give the theoretical analysis of the recovery behavior of the Joint-SAMP algorithm. Simulation results also confirm the effectiveness of our method.

In WiMAX networks with fixed relay stations (FRSs), mobile users move in and out of the coverage area of FRS in different periods of a day. Frequent handover requests generated by population mobility lead to load imbalances among FRSs and low data rate. Mobile relay stations (MRSs), which can shift between FRSs, can share part of the users' requests, then reduce the burden of FRSs. In this paper, we define and study the Minimum Mobile Relay Path selection problem (MMRP), whose objective is to deploy minimum MRSs to patrol FRSs according to their busy durations. To reflect the FRSs' real situation, Markov chains are adopted to predict FRSs' busy durations which are then represented as a weighted graph. Based on this graph, the original problem is transformed into a Minimum Vertex-disjoint Path Cover problem. After analyzing properties of the weighted graph, we propose the algorithms based on the principles of graph searching, maximum matching and the maximum flow, respectively. Theoretical analysis and simulation results show that, compared with traditional search algorithms, solutions based on maximum matching and the maximum flow have lower complexity, yet better performance on the number of paths and system overhead, and the solution using predicted busy durations is superior to those in paths gains whose busy duration is presupposed.

With fast advances in camera sensor devices and wide applications of wireless camera sensor networks (WCSNs), optimizing the tradeoff between power consumption and coverage rate of WCSNs attracts a great deal of research attention. In contrast to 2D WCSNs, 3D WCSNs capture more accurate and comprehensive information for surveillant applications. The percolation theory has been proved to be powerful and effective in characterizing the exposure path prevention using 2D WCSNs. While percolation theory can be potentially extended into 3D WCSNs to improve the power and coverage performances, there are still many new challenges remaining unsolved. On the other hand, the clustering algorithm is widely cited as an efficient power saving and interference mitigation technique for WCSNs. However, how to integrate the clustering technique with 3D percolation theory in WCSNs is still an open problem. To overcome the aforementioned challenges, in this paper we propose the 3D percolation theory-based exposure-path prevention scheme for optimizing the tradeoff between power consumption and coverage rate over clustered WCSNs. First, we apply and extend the bond-percolation theory to derive the optimal density of camera sensors deployed in 3D WCSNs subject to the minimum exposure-path prevention probability constraint. Then, we apply the mutual entropy to analyze the dependency among 3D neighboring camera sensors, justifying the bond-percolation theory in 3D WCSNs. Finally, we apply the new low energy adaptive clustering hierarchy (LEACH) architecture into our 3D WCSNs for power saving and interference mitigation. The conducted extensive simulations show that our proposed schemes outperform the other existing schemes in optimizing the tradeoff between power consumption and coverage rate over 3D WCSNs.

In recent years, quantum-based techniques have attracted significant research attention because of its unique advantages on satellite communications, especially for security problem. Security guarantee is one of the most important requirements in QoS-driven 3D satellite networks. Quantum key distribution (QKD) is a methodology for generating and distributing random encryption keys using the principles of quantum physics, which enables two distant communications parties to securely communicate in a way that cannot be eavesdropped on without being detected. Although the QKD method can ensure the absolute security transmission over 3D satellite networks, it imposes many new implementation challenges due to the various limitations on quantum communication over long distances via 3D free space, including quantum channel attenuation, photon-state disruption and vulnerability to noise/interference, laser-beam widening, and constrained security-key generation rate. These problems get even more challenging when QoS provisioning is required for the applications over the 3D satellite networks. To overcome the aforementioned difficulties, we propose the framework to efficiently implement the QKD for security guarantees over quantum-repeater-based QoS-driven 3D satellite networks. First, we develop the quantum-repeater-based QKD satellite network architecture. Then, we design the quantum repeater including the purification scheduling algorithm and the optimal QoS-based repeating-router selection scheme in quantum-repeater-based QKD satellite networks. Finally, the obtained simulations evaluation validate and evaluate our proposed algorithms and schemes.

Device-to-device (D2D) communication is an emerging technique that can reduce device transmission power and enhance cellular capacity, which allows direct communication between two mobile devices. Amount of small packets may exchange between devices in burst mode transmission with the constraint of low overhead and low complexity for energy efficient processing. One of the major challenges for outband D2D communications is packet-based synchronization. Due to channel fading or path loss, the received signal-to-noise ratio (SNR) is time-varying with a wide range, resulting in selection of an appropriate threshold for well balancing both missed detection probability (MDP) and false alarm probability (FAP) inconveniently. We propose an efficient packet detection scheme with easily threshold setting based on WiFi interface. By employing a novel normalized factor as a denominator in autocorrelation based timing metric, both theoretical analysis and the conducted simulation results show that the gap between MDP and FAP is enlarged, facilitating threshold selection for a wide range of SNR in additive white Gaussian noise (AWGN) channel. More importantly, the threshold setting in multi-path Rayleigh fading channels can be determined according to the AWGN case straightforwardly.

Symmetric alpha-stable (SαS) distribution has been widely used to model undesirable impulsive noise disturbance in many scenarios. Due to lack of probability density function (pdf) of SαS distribution (except Cauchy and Gaussian cases), the general closed-form expression of the bit error probability or symbol error probability for M-ary modulation schemes has not been derived yet, preventing the derivation of the exact coding gain from being feasible. By employing geometric power involved in zero-order statistics, we create a mapping mechanism which is consistently continuous along the entire range of characteristic values. Then we derive the accurate bit error probability and symbol error probability of M-ary modulation schemes under SαS noise. Our obtained derivations agree well with our simulations, which provide a unified framework for uncoded systems using M-ary modulation under additive white Gaussian noise (AWGN) and additive white symmetric alpha-stable noise (AWSαSN). Also, it enables the design of capacity approaching codes especially for higher-order modulation scenarios.

With the rapid advances in data transmission over various advanced underwater acoustic networks, it is important to develop the efficient protocol to obtain a high data-rate with QoS guarantee requirements for acoustic wireless communications. Because of the harsh underwater acoustic environments, such as time-varying network topology and channel conditions, limited bandwidth and constrained underwater signal propagation, we develop and implement the multi-hop adaptive RTT-driven transport layer flow and error control protocol (ARTFEC) for QoS guaranteed image transmission in underwater wireless networks. ARTFEC is based on the congestion window size control and the Q-learning optimal timeout selection with QoS provisioning. For the congestion window size control, we propose an RTT-based data flow control policy to adapt to the varying acoustic channel environments, aiming to guarantee the reliability and high data rate of data transmission. In addition, we develop a Q-learning based optimal timeout selection algorithm to improve the channel utilization efficiency, which can increase the end-to-end throughput while decreasing the packet loss rate. We implement ARTFEC using our lab testbed, which consists of the Aqua-Net protocol stack and the acoustic OFDM modems. The simulation results obtained show that our developed ARTFEC transport layer protocol outperforms the other existing reliable data transmission schemes, in terms of end-to-end throughput and packet loss rate.

The auto-correlation based coarse symbol timing scheme yields good correlation timing-metric for wireless orthogonal frequency division multiplexing (OFDM) systems under independent Rayleigh fading channels. However, it generates a plateau in its timing-metric due to the structure of the preamble added with cyclic prefix (CP). Inherently, the mean square error (MSE) performance of a coarse symbol timing scheme is closely related to the sharpness of its main-lobe timing-metric. Some of the literatures focus on polishing the auto-correlation timing-metrics to find the frame start point. One of the well-known methods uses arithmetic mean of the auto-correlation timing-metrics to generate a peak after post-processing. We propose a post-processing scheme using weighted arithmetic mean of the auto-correlation timing-metrics, and provide a different viewpoint of analyzing the performance of post-processing schemes in additive white Gaussian noise (AWGN) channels, which may be helpful in timing-metric design. By using the knowledge of root mean square (rms) delay spread, we get a general coarse symbol timing scheme for both AWGN and Rayleigh fading channels. The obtained simulation results indicate that the MSE performance of the post-processing scheme is a function of the main-lobe sharpness.

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity model. For the binary sector model, we construct a special Voronoi diagram, called sector centroids-based Voronoi diagram, to transform the minimal exposure path problem from a continuous geometric problem into a discrete geometric problem. By using the sector centroids-based Voronoi diagram, we develop an approximate algorithm to find the minimal exposure path in the sensors deployment field. For the directional sensitive model, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function and then generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. On the basis of the aforementioned weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive experiment simulations to validate and evaluate our proposed models and algorithms. Copyright © 2012 John Wiley & Sons, Ltd.

Maximizing the lifespan of the wireless sensor networks (WSNs) subject to minimum area coverage rate is a crucial issue in the design and analysis of WSNs. With the fast development of image and video technology, wireless camera sensor networks (WCSNs) have attracted a great deal of research attention, particularly in the area coverage using WCSNs for many military or battle-field applications. Compared with 2D camera WSNs-based coverage, 3D camera WSNs-based coverage can provide with more comprehensive information. However, it imposes new challenges which were not encountered in 2D WCSNs, such as the complexity of the 3D coverage design and analysis. To overcome these problems, in this paper we propose a novel 3D coverage scheme using WCSNs. It aims at maximizing the lifespan by minimizing the energy consumption subject to the minimum coverage rate specified by application requirements. Building up the system model for field of view (FOV) of our 3D WCSNs, we derive the analytical expressions which can characterize the minimum number of randomly deployed camera sensor nodes to ensure the 3D coverage rate. Also, we develop the new low energy adaptive clustering hierarchy (LEACH) architecture with the new features as follows: applying heterogeneous sensor nodes; refining the threshold for cluster head selection; employing hybrid routing algorithms. The obtained simulation results show that our proposed schemes significantly outperform the other existing schemes in terms of maximizing the lifespan of WCSNs subject to the minimum coverage rate over the 3D space.

In order to make a thorough evaluation of the security against eavesdropping in heterogeneous radio access network (RAN) with relays, this paper proposes a novel secrecy graph-based secrecy evaluation scheme for distributed wireless relay networks with eavesdropping present. Our scheme firstly models user and eavesdropper with the nonuniform distributions in a discrete system and decides the best path choice of users and relays for the purpose of security. Secondly, we derive the average eavesdropping effect (AEE) based on the known distribution and arrangement of uplink path, and investigate the derived results and the different relay deployment plans. Taking into account various factors (fluctuation of user traffic, burden limitation of relay etc), we efficiently utilize the available resources to simulate the real scenarios to implement the accurate secrecy evaluations. Finally, the simulation results prove that our proposed scheme outperform the other existing ones.

We propose the semi-Markov decision process (S-MDP) model-based stochastic optimization scheme for QoS-guaranteed services in orthogonal frequency division multiple access (OFDMA) multicell cooperation networks. Our objective is to efficiently maximize the power efficiency at the base stations while guaranteeing the diverse QoS provisionings for mobile users through the multicell cooperation resource allocation. Considering the wireless cells' varying traffic load, user mobility, and the channel state information (CSI), we propose the optimal resource control scheme, in terms of the subcarrier and power allocations for each mobile user in each mobile cell. In particular, we apply the multicell cooperation for increasing power efficiency and also develop the interference penalty-driven resource allocation algorithm for mitigating intercell interference. We also develop and evaluate the stochastic optimization for the multicell cooperation networks.

We consider the full-duplex transmission over bidirectional channels with imperfect self-interference cancelation in wireless networks. In particular, together using propagation-domain interference suppression, analog-domain interference cancellation, and digital-domain interference cancellation, we develop the optimal dynamic power allocation schemes for the wireless full-duplex sum-rate optimization problem which aims at maximizing the sum-rate of wireless full-duplex bidirectional transmissions. In the high signal-to-interference-plus-noise ratio (SINR) region, the full-duplex sum-rate maximization problem is a convex optimization problem. For interference-dominated wireless full-duplex transmission in the high SINR region, we derive the closed-form expression for the optimal dynamic power allocation scheme. For non-interference-dominated wireless full-duplex transmission in the high SINR region, we obtain the optimal dynamic power allocation scheme by numerically solving the corresponding Karush-Kuhn-Tucker (KKT) conditions. While the full-duplex sum-rate maximization problem is usually not a convex optimization problem, by developing the tightest lower-bound function and using the logarithmic change of variables technique, we convert the full-duplex sum-rate maximization problem to a convex optimization problem. Then, using our proposed iteration algorithm, we can numerically derive the optimal dynamic power allocation scheme for the more generic scenario. Also presented are the numerical results which validate our developed optimal dynamic power allocation schemes.

This paper proposes a novel amplify-and-forward with cooperative-jamming (ACJ) scheme. In order to make a balance between secrecy capacity (SC) and energy consumption, we use RSεwhich is defined as the maximum equivalent SC for each unit of energy consumption to evaluate the performance of wireless communications. This paper shows that although SC increases with energy consumption, RSεis not a monotone increasing function. That is to say, when energy consumption exceeds a specific value, the efficiency of each unit of the energy consumed decreases in aspects of the security. In this paper, we not only study the variation tendency of SC changing along with energy consumption, but also use the eigenvalue method to derive a precise equation of RSε. Moreover, simulation results also demonstrate that our model performs quite well in terms of lowering energy consumption to the least, while guaranteeing the security requirements in wireless cooperative networks.

In order to reduce the energy consumption, we propose a novel Macro Base Station (MBS) sleep scheme based on the density of Pico Base Station (PBS) including three sleep approaches for current heterogeneous networks (het-net). Our scheme consists of two parts. In the first part, by setting thresholds for PBS density according to the total coverage of PBSs in a macro-coverage, we divide the network into three scenarios where PBSs are deployed densely, sparsely, and commonly correspondingly. In the second part, the MBS chooses one sleep approach of our scheme by comparing its PBS density with the thresholds. In each approach, minimizing the system energy consumption and meeting the QoS requirements are both considered as our goals. Particularly, the offset value is designed to guarantee blocking probability, especially when the neighboring MBSs are in charge of taking over the migrated users from the sleep MBS. Finally, simulation results demonstrate that the proposed scheme is much more efficient than the other existing schemes in terms of the power consumption and the blocking probability.

Spectrum and power efficiencies are both crucial to design efficient wireless networks. In past two decades, spectrum and power efficiencies of wireless networks are optimized separately. However, to increase the spectrum efficiency while reducing the energy consumption, it is necessary to jointly optimize spectrum and power efficiencies of wireless networks. Supporting the statistical quality of service (QoS) provisionings for real-time traffic is crucial, but imposes new challenges, in the next generation wireless networks. In this paper, we propose an efficient framework to jointly optimize effective spectrum efficiency (ESE) and effective power efficiency (EPE) under different statistical QoS guarantees constraints to support the real-time traffic over wireless networks. In particular, we derive the relationship between ESE and EPE under statistical QoS provisioning constraint. Based on this relationship, we obtain the mutually beneficial (MB) region and the contention-based (CB) region. In the MB region, we propose a novel strategy to achieve the joint effective spectrum and power efficiencies optimization using the average transmit power control. In the CB region, we propose the wireless-relay-based strategy to jointly optimize the effective capacity and power efficiency. In both MB and CB regions, we develop the dynamic transmit-power control strategy and the MIMO-based strategy to jointly maximize the effective spectrum and power efficiencies. Also conducted is a set of numerical evaluations showing that our proposed strategies can achieve superior joint spectrum and power efficiencies optimization for the diverse statistical QoS provisionings.

To enable multiple mobile users to transmit their signals simultaneously over the same sub-channels, the virtual multiple-input multiple-output (V-MIMO) techniques can exploit the multiple-input multiple-output (MIMO) spectrum efficiency gain. Traditional V-MIMO transmission schemes mainly focus on maximizing the throughput of grouped mobile users without taking into account the quality-of-service (QoS) provisionings. In this paper, we propose the optimal power allocation schemes with statistical QoS provisionings to maximize the effective capacity of non-collaborative/collaborative V-MIMO wireless networks, respectively. For non-collaborative V-MIMO wireless networks, the mobile users in one V-MIMO group transmit signals independently over the same sub-channels. In the view point of existing mobile users, they solely occupy the sub-channels. Thus, the existing mobile users employ the QoS-driven single-user power allocation scheme to maximize their effective capacity. By converting the non-collaborative V-MIMO transmission optimization problem into a strictly convex optimization problem, we derive the QoS-driven power allocation scheme for the newly added mobile users to maximize their effective capacity. For collaborative V-MIMO wireless networks where the mobile users in one group can collaboratively transmit their signals, we derive the QoS-driven collaborative power allocation schemes for both the existing and the newly added mobile users. Also conducted is a set of simulation evaluations, showing that our proposed power allocation schemes for V-MIMO wireless networks outperform the other existing schemes.

When transmitting stochastic traffic flows over wireless networks, there exists an inherent tradeoff between average transmit power and corresponding queuing-delay bound. In this paper, we investigate such a tradeoff and show how average power increases as delay-bound requirement for wireless network traffics becomes stringent. Specifically, we propose the resource allocation schemes to minimize the power consumption subject to a delay quality-of-service (QoS) constraint, where the delay constraint is in terms of queue-length decay rate when an arrival traffic is transmitted through the wireless networks. We focus on orthogonal-frequency-division-multiplexing (OFDM) communications under three different network infrastructures, namely, point-to-point link, multihop amplify-and-forward (AF) network, and multiuser cellular network. We derive the optimal resource allocation policies for each scenario, and compare their performances with other existing resource-allocation policies. The obtained simulation and numerical results show that using our proposed optimal resource-allocation policies, significant power saving can be achieved. Furthermore, our OFDM-based communications systems can significantly reduce the power consumption, especially under stringent delay constraint.

Spectrum and power efficiencies are both crucial to design efficient wireless networks. In past two decades, spectrum and power efficiencies of wireless networks are optimized separately. However, to increase the spectrum efficiency and reduce the energy consumption, it is necessary to jointly optimize spectrum and power efficiencies of wireless networks. In this paper, we propose an efficient framework to model the statistical delay quality of service (QoS) guarantees, in terms of QoS exponent, effective spectrum efficiency (ESE), and effective power efficiency (EPE), for joint spectrum and power efficiencies optimization to transmit real-time traffics over wireless networks. In particular, we derive the relationship between ESE and EPE under statistical QoS provisioning constraint. Based on the relationship, we obtain the mutually beneficial region and contention-based region. We also analyze the global maximum EPE and optimal average transmit power under various statistical QoS guarantees. Then, we develop a novel strategy to achieve the joint spectrum and power efficiencies optimization. Also conducted is a set of numerical evaluations showing that our proposed strategy can achieve superior joint spectrum and power efficiencies optimization for various statistical QoS provisionings.

In the practical applications of wireless sensor networks, it is almost inevitable that some sensors become faulty during running. The faulty measurement values will cause a burden to the limited energy of sensor networks. Furthermore, wrong judgement might be deduced because of the faulty data when they reach base station. Therefore, proper fault detection especially for long-term large-scale systems is crucial and challenging. Motivated by the requirement of practical applications, we propose a distributed fault detection approach for wireless senor networks. Firstly, Hidden Markov Random Field (HMRF) model is introduced to characterize the correlations between measurement values and real values of sensor nodes. Then, an errors-in-variables estimation method is presented to obtain the parameters in the HMRF model. Finally, a distributed fault detection algorithm is proposed based on the HMRF model. Both theoretical analysis and simulation results show that the proposed HMRF-based fault detection achieves considerable high detection accuracy and low false alarm rate simultaneously.

In order to conserve energy and achieve better quality of service (QoS) provisioning, this paper proposes a novel security-oriented cooperative (NSOC) scheme for distributed wireless relay networks in presence of eavesdroppers. Our scheme includes two parts. In part one, we model the wireless relay network and analyze the secure connectivity by exploiting the secrecy graph. Then, when the result of the analysis is inferior to the security demands, our developed cooperative jamming strategy is kicked in to further assist the secure communications, which plays the important role in secure communications. In the cooperative jamming strategy, the channels established by candidate nodes are modeled as finite-state Markov channels (FSMCs) owing to the property of time-varying, and the remaining energy of nodes is considered as well. In addition, a jammer selection policy using simple priority index is depicted, taking secrecy capacity (SC) and energy balance into account. The major superiority of this scheme is to reduce unnecessary energy costs and system complexity, while assuring the security and reliability. Finally, the obtained simulation results show that our proposed scheme outperforms the existing ones especially in terms of secrecy capacity and system reward.

To further enhance the quality of service (QoS), this paper proposes a novel multi-objective relay-jammer pair selection scheme in wireless cooperative networks where the wireless channels are modeled as first-order finite-state Markov channels (FSMCs). The FSMC model is used to approximate the time variations of the average received signal-to-noise ratio (SNR), channel power gain, and remaining power of nodes. In addition, the candidate cooperative nodes can be selected as relay and jammer, respectively, to assist the transmission or disturb the eavesdropper. Thus, we can formulate a restless bandit problem to model and analyze how to select the optimal relay-jammer pair according to the desired QoS optimization goals, in terms of energy balance, mitigating error propagation and increasing secrecy capacity (SC). The obtained simulation results show that our proposed schemes outperform the other existing schemes.

In wireless orthogonal frequency division multiplexing (OFDM) systems, coarse frame synchronization and signal-to-noise ratio (SNR) values are two crucial parameters for OFDM receivers. In this paper, we develop a coarse timing estimator to overcome the plateau phenomenon by using delayed correlation between two estimated SNR values. The training symbol is designed for multiple pieces instead of two parts to mitigate the Doppler effect for SNR estimation. Compared with the Minn's scheme, our proposed scheme has smaller estimation variance both in EVA and ETU channels, which can also provide an estimated SNR value in the time domain. The obtained SNR value can be further reused to carrier frequency offset (CFO) estimation or decoding for system performance improvement.

We propose the quality-of-service (QoS) driven power allocation scheme for wireless links under average and peak power constraints. By integrating information theory with the principle of effective capacity, our proposed scheme aims at maximizing the system throughput subject to a given delay-QoS constraint. Over the block fading channel, we derive the optimal power allocation scheme to maximize the effective capacity. The analyses and numerical results show that our optimal power allocation scheme converges to the despicking water-filling scheme when the QoS constraint becomes very loose. On the other hand, our optimal power allocation scheme reduces to the dispicking channel inversion scheme when the QoS constraint gets very stringent. Two non-optimal power allocation schemes are described for comparison purposes. We also propose and evaluate the independent optimization for the multicarrier system.

We propose the quality-of-service (QoS) driven power allocation scheme for full-duplex wireless links. By integrating information theory with the principle of effective capacity, we build two models - local transmit power related self-interference (LTPRS) model and local transmit power unrelated self-interference (LTPUS) model to analyze the full-duplex transmission, respectively. In the LTPRS model, self-interference directly corresponds to the level of local transmit power. In the LTPUS model, self-interference does not directly relate to the level of local transmit power. For both of these two models, we derive the optimal power allocation schemes, which aim at maximizing the system throughput subject to a given delay QoS constraint, over bidirectional wireless links with full-duplex transmission. The analyses and numerical results verify that our proposed power allocation scheme can efficiently support diverse QoS requirement over full-duplex wireless links. For LTPRS model, the optimal power allocation scheme converges to a constant power scheme when the QoS constraint gets very loose and the optimal power allocation scheme reduces to the channel inversion scheme when the QoS constraint becomes very stringent. For LTPUS model, the optimal power allocation scheme converges to the the water-filling scheme when the QoS constraint gets very loose and the optimal power allocation scheme reduces to the channel inversion scheme when the QoS constraint becomes very stringent.

Integrating information theory with the principle of effective capacity, we propose the optimal resource allocation schemes for wireless full duplex and half duplex relay networks, respectively, to support the statistical quality-of-service (QoS) provisioning. In particular, we introduce a new control parameter, termed cancellation coefficient, to characterize the performance of full duplex relay transmission mode. For both amplitude-and-forward (AF) and decode-and-forward (DF) relay networks, we develop the dynamic hybrid resource allocation policies under full duplex and half duplex transmission modes to maximize the network throughput for the given delay QoS constraint measured by the QoS exponent. The numerical results obtained verify that our proposed resource allocation schemes can support diverse QoS requirements over wireless relay networks under full duplex and half duplex transmission modes. Our analysis indicates that the optimal effective capacity of perfect full duplex transmission mode is not the just twice as much as the optimal effective capacity of half duplex transmission mode. Our numerical analyses also show that the hybrid transmission mode can achieve better performance than just using full duplex or half duplex transmission mode alone.

With rapid improvements and miniaturization in hardware, sensor nodes equipped with acoustic and visual information collection modules promise an unprecedented opportunity for target surveillance applications. This paper investigates a critical task of target surveillance, multi-class classification, in distributed multimedia sensor networks. We first analyze the procedure of target classification utilizing the acoustic and visual information. Then, we propose a binary classification tree based framework for distributed target classification in multimedia sensor networks. The proposed framework includes three main components: Generation of binary classification tree, Division of binary classification tree, and Selection of multimedia sensor nodes. Finally, we conduct an experimental application of target classification and extensive simulations to validate and evaluate our proposed framework and related schemes.

Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the minimal exposure path problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. We first formulate the minimal exposure path problem, and then convert it into the shortest path problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, for solving the shortest path problem. Our proposed algorithm is with low-complexity and high-parallelism. Moreover, the core mechanism of our physarum optimization is also helpful for designing new graph algorithms and improving routing protocols and topology control in self-organized networks.

Most existing works on sensor coverage mainly concentrate on the full coverage models which ensure that all points in the deployment region are covered at the expense of high complexity and cost. In contrast, the exposure-path prevention does not require full coverage sensor deployment, and instead it only needs the partial coverage, because the exposure paths are prevented as long as no moving objects or phenomena can go through a deployment region without being detected. Towards this end, we focus on the partial coverage by applying the percolation theory to solve the exposure path problem for wireless sensor networks. Specifically, we propose a bond-percolation based scheme by mapping the exposure path problem into a bond percolation model. Using this model, we derive the analytical expressions of critical densities for wireless sensor networks under random sensor deployment.

The 15 papers in this special issue are divided into four categories: challenges in broadband wireless communications; physical layer techniques; radio resource management techniques; and field measurement and channel modeling.

ABSTRACT Camera sensor networks have recently emerged as a critical research topic. In this paper, we investigate the coverage problem for camera sensor networks. Specially, compared to the coverage problem for target detection which has been intensively studied, this paper studies the coverage problem from the perspective of target localization. We first propose a novel localization‐oriented sensing model based on the perspective projection of the camera sensors. Then, under the random uniform deployment strategy, we analyze how the probability of the localization‐oriented coverage (L‐coverage for short) changes with the sensors number and the parameters of the proposed sensing model. Finally, we conduct extensive simulations to validate our model and theoretical analysis about L‐coverage probability. The obtained results show that our scheme can be effectively applied for practical scenarios. Copyright © 2011 John Wiley & Sons, Ltd.

In some harsh environment, wireless sensor networks without the sink are often deployed. In the network, the nodes just have limited energy and are easy to fail. In order to prevent the data loss due to the failure of nodes, each node disseminates its data to be stored at a subset of nodes in the network for preservation. However, each node just knows the information of its neighbors, and just has limited storage space. Therefore, it is a challenge to manage the processes of data dissemination and storage effectively. In this paper, an adaptive probability broadcast-based protocol, named APBDP (Adaptive Probability Broadcast-based Data Preservation), is proposed to tackle the challenge. In APBDP, each node disseminates its data to the network by an adaptive probability broadcast mechanism. The mechanism can not only enable all nodes receive the data packet, but also reduce the redundance of data transmission to conserve the energy of nodes. Moreover, each node stores the data received by using LT (Luby Transform) codes, which are the first rateless erasure codes that are very efficient as the amount of data grows. After above processes are finished, a collector (e.g., a motor vehicle) can recover all data by visiting a small subset of nodes. To the best of our knowledge, APBDP is the first scheme that uses adaptive probability broadcast to achieve the efficient data preservation. Theoretical analyses and simulations show that APBDP can achieve higher performance of data preservation and energy efficiency than existing protocols.

In sensor networks, the adversaries can inject false data reports from compromising nodes. Previous approaches for filtering false reports share keys between the source node and its upstream nodes on the path to sink, and rely on intermediate nodes to verify the reports generated by downstream nodes in a probabilistic manner. As a result, false reports have to travel several hops before detected. Worse still, these schemes haven't balanced the overheads of all nodes in the process of keys distributing. In response to these, this paper proposes a cluster-based filtering scheme, in which nodes are grouped into clusters once deployed by employing some strong nodes act as cluster heads. We then proposed a distributed method of keys assignment by constructing a sink-rooted tree which comprises of all the cluster heads, guarantees that the keys of a source cluster are stored by several forwarding clusters close to it and thus to filter false reports generated by the source cluster during several hops during forwarding, further, the number of authentication keys held by the forwarding clusters getting smaller with the distance increase from the source cluster and thus to balance the keys stored by each forwarding cluster. Analysis and simulation results show that our scheme outperforms existing schemes in terms of overhead balance and filtering efficiency.

We present an analytical model for end-to-end (e2e) channel systems with lossy communication (E2E-CSLC) of multihop wireless networks using Quasi-Birth Death models (QBDs). Moveover, we show that the state set of level 0 of E2E-CSLC model, i.e., S0, is an attractor according to the special structure of QBDs, which is the foundation of studying the probabilistic reachability for E2E-CSLC. Based on the analytical model, we discuss some communication performances (e.g., e2e packet loss probability, e2e delay, and e2e throughput, etc.). And a case study of E2E-CSLC shows the versatility of our analytical model. The results lead to a significantly analysis foundation and method for designing and improving e2e networks communication protocols where the underlying communication medium is faulty.

We propose the QoS-aware BS-selection and the corresponding resource-allocation schemes for downlink multi-user transmissions over the distributed multiple-input-multiple-output (MIMO) links, where multiple location-independent base-stations (BS), controlled by a central server, cooperatively transmit data to multiple mobile users. Our proposed schemes aim at minimizing the BS usages and reducing the interfering range of the distributed MIMO transmissions, while satisfying diverse statistical delay-QoS requirements for all users, which are characterized by the delay-bound violation probability and the effective capacity technique. Specifically, we propose two BS-usage minimization frameworks to develop the QoS-aware BS-selection schemes and the corresponding wireless resource-allocation algorithms across multiple mobile users. The first framework applies the joint block-diagonalization (BD) and probabilistic transmission (PT) to implement multiple access over multiple mobile users, while the second one employs time-division multiple access (TDMA) approach to control multiple users' links. We then derive the optimal BS-selection schemes for these two frameworks, respectively. In addition, we further discuss the PT-only based BS-selection scheme. Also conducted is a set of simulation evaluations to comparatively study the average BS-usage and interfering range of our proposed schemes and to analyze the impact of QoS constraints on the BS selections for distributed MIMO transmissions.

The distributed multiple-input-multiple-output (MIMO) techniques across multiple cooperative base stations (BS) can significantly enhance the capability of the broadband wireless networks in terms of quality-of-service (QoS) provisioning for wireless data transmissions. However, the computational complexity and the interfering range of the distributed MIMO systems also increase rapidly as the number of cooperative BS's increases. In this paper, we propose the QoS-aware BS-selection schemes for the distributed wireless MIMO links, which aim at minimizing the BS usages and reducing the interfering range, while satisfying diverse statistical delay-QoS constraints characterized by the delay-bound violation probability and the effective capacity technique. In particular, based on the channel state information (CSI) and QoS requirements, a subset of BS with variable cardinality for the distributed MIMO transmission is dynamically selected, where the selections are controlled by a central server. For the single-user scenario, we develop two optimization frameworks, respectively, to derive the efficient BS-selection schemes and the corresponding resource allocation algorithms. One framework uses the incremental BS-selection and time-sharing (IBS-TS) strategies, and the other employs the ordered-gain based BS-selection and probabilistic transmissions (OGBS-PT). The IBS-TS framework can yield better performance, while the scheme developed under the OGBS-PT framework is easier to implement. For the multi-user scenario, we propose the optimization framework applying the priority BS-selection, block-diagonalization precoding, and probabilistic transmission (PBS-BD-PT) techniques. We also propose the optimization framework applying the priority BS-selection, time-division-multiple-access, and probabilistic transmission (PBS-TDMA-PT) techniques. We derive the optimal transmission schemes for all the aforementioned frameworks, respectively. Also conducted is a set of simulation evaluations which compare our proposed schemes with several baseline schemes and show the impact of the delay-QoS requirements, transmit power, and traffic loads on the performances of BS selections for distributed MIMO systems.

We consider a wireless cognitive radio network in which a set of secondary users (SUs) opportunistically utilize the wireless spectrum licensed to the primary users (PUs) to transmit packets to the secondary base station (SBS). It is challenging to maximize the spectrum utilization while limiting the interference imposed to PUs due to SUs. To achieve the optimal tradeoff between the spectrum utilization and the interference caused by SUs, we propose the adaptive spectrum sharing schemes for code division multiple access (CDMA) based cognitive medium access control (MAC) in the uplink communications over the cognitive radio networks. Our proposed schemes address the joint problems of channel sensing, data transmission, and power and rate allocations. Under our proposed schemes, the SUs can adaptively select between the intrusive spectrum sharing and the non-intrusive spectrum sharing operations to transmit data to SBS based on the channel utilization, traffic load, and interference constraints. Our proposed schemes enable the SUs to efficiently utilize the available frequency spectrum which is licensed to the PUs while stringently limiting the interference to the PUs. Also conducted are extensive simulations to validate and evaluate our proposed schemes, which show the superiority of our proposed schemes as compared with the other schemes.

In this paper, we investigate the coverage problem from the perspective of target localization for wireless camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera sensors. Based on the sensing model, we propose a new notion of coverage, Localization-oriented coverage (L-coverage for short), by using Bayesian estimation theory. Furthermore, we analyze the relationship between the density of camera sensors and the L-coverage probability under random deployment where camera sensors are deployed according to a 2-dimensional Poisson process. According to the relationship between the density of camera sensors and the L-coverage probability, we derive the density requirements for an expected L-coverage probability. We validate and evaluate our proposed models and schemes by simulations.

We propose the dynamic BS-selection schemes for distributed wireless MIMO links, which aim at minimizing the average base-station (BS) usage while satisfying the specified statistical delay quality-of-service (QoS) constraint characterized by the delay-bound violation probability. In particular, the distributed transmission system consists of a number of location-independent BS's, a central server, and one mobile station (MS). Based on the channel state information (CSI), the MS dynamically selects the subsets of BS's with variable subset cardinalities to construct the distributed MIMO link, and feeds back the selected BS's indices to the central server. The central server controls these selected BS's to cooperatively transmit data to the MS. Under the above framework, we develop the QoS-guaranteed dynamic BS-selection schemes for scenarios with and without feeding back the selected BS subsets' CSI, respectively. Simulation results show that our proposed dynamic schemes significantly decrease the average BS usage and interfering range as compared to baseline schemes which fix the cardinality of the selected BS subsets.

Spectrum sensing, which finds spectrum opportunities for the secondary users (SUs), plays a pivotal role in the cognitive radio-based dynamic spectrum access networks. In this paper, we develop the optimal spectrum sensing scheduling for the energy-constraint cognitive radio networks where the SUs are not synchronized with the primary users (PUs). First, we develop the analytical models to investigate the tradeoff between the sensing energy consumption and the missed spectrum opportunity for the SUs. Second, building on top of the analytical models, we derive the optimal spectrum sensing scheduling which minimizes the sensing energy consumption while confining missed spectrum opportunity to a predefined threshold. Third, we take into account the sensing errors, namely, missed detection and false alarm, in the analytical models. In particular, we analyze the impact of the sensing errors on the energy consumption and missed spectrum opportunity when adopting the two-threshold based sequential sensing policy. Finally, we conduct simulations to validate and evaluate our proposed scheme.

We propose the network-coding-based relay MAC protocols that aim at providing drive-thru Internet services to the moving vehicles by utilizing the dedicated short range communication (DSRC) over vehicular networks. Our proposed MAC protocols can adaptively switch between two communications modes according to the highway traffic-load conditions. When the highway traffic load is heavy, our system works under the one-hop communications mode. When the density of vehicles on the road is low, the service base station turns on the network-coding-based two-hop relay mode that can extend the coverage of the non-safety data services to the two-hop area. We propose the wireless-network-coding schemes in the relay communications mode to improve the network performance. We also develop the analytical models to analyze the performance of our protocols. Also conducted are simulation experiments to validate and evaluate our proposed MAC protocols. Compared with the traditional IEEE 802.11 MAC protocol for Internet drive-thru services, our proposed relay MAC protocol can significantly increase the network efficiency.

The inefficiency of legacy transmission control protocol (TCP) over wired/wireless networks inspired numerous research results in recent decade. To design a congestion control protocol that can achieve strong stability, fairness, fast convergence and high-utilization in hybrid networks, we propose AcceleRate tRansmission towards Optimal Window size TCP for Wireless networks (ARROW-WTCP) by providing a joint design of source and router algorithms, which enables feasible deployment of ARROW-TCP from wired to wireless networks. In our experimental study, we compare ARROW-WTCP with eXplicit Control Protocol Blind (XCP-B), the wireless version of XCP. Simulation results show that ARROW-WTCP outperforms XCP-B in terms of stability, fairness, convergence and utilization under highly dynamic wireless networks.

To minimize energy consumption in the Wireless Sensor Networks (WSNs), we propose a decentralized sensor coordination optimization scheme for Mobile Multi-Target Tracking (MMTT) in WSNs. Our scheme partitions the available sensor-nodes into clusters using the maximum-entropy based clustering criteria. For each tracked target, a number of neighboring clusters are activated based on their Hausdorff distance to the tracked targets.We propose the corresponding target-position estimation scheme using the particle Probability Hypothesis Density (PHD) filtering algorithm. Furthermore, our sensor coordination scheme dynamically selects the cluster members to jointly optimize the sensing accuracy and the energy efficiency by using our proposed Decentralized Particle Swarm Optimization (DPSO) based algorithm. The conducted performance simulations evaluate our proposed sensor-coordination schemes, which show the optimal performance in terms of energy efficiency of the WSN while maintaining a high target-position estimate accuracy.

We propose the queue-aware spectrum sensing schemes for interference-constrained opportunistic transmissions of secondary users (SUs) in cognitive radio networks. Specifically, we employ the energy detection for SUs to sense the spectrum-usage status of the primary users (PUs). Unlike the traditional energy detector using a fixed energy threshold to decide the presence of the PUs' signals, we dynamically change the energy threshold based on the queue length at the sender of SUs. Our proposed dynamic-threshold policies aim at effectively satisfying the statistical quality-of-service (QoS) requirements such as the queue-length-bound violation probability and buffer-overflow probability, while upper-bounding the probability of causing interferences to the PUs. We develop the dynamic-threshold policies for the scenarios that the sender of SUs has infinite and finite queue buffer sizes, respectively. Simulations evaluations show that under the specified statistical QoS requirements and interference constraints, our proposed schemes can support higher data traffic loads for SUs than the traditional energy-detection based scheme.

This paper focuses on the spectrum sensing issues in the unslotted cognitive radio networks with wireless fading channels. To overcome the energy-inefficiency problem of the existing continuous/fixed-schedule spectrum sensing schemes in the cognitive radio networks, we propose an efficient spectrum sensing scheme for secondary users (SUs). The design goal of our proposed scheme is to save the sensing energy consumption while guaranteeing the priority of the PUs and the spectrum opportunity for SUs in terms of available spectrum usage time. In particular, our proposed energy-efficient spectrum sensing scheme adaptively adjusts the spectrum sensing periods and determines between the presence and vacancy of the PU by taking advantage of PU's activity patterns. We also develop a novel two-threshold based sequential sensing policy to reduce the false alarm probability while limiting the missed detection probability. We conduct simulations to validate and evaluate our proposed scheme.

This paper focuses on the unslotted cognitive radio networks in which the secondary users (SUs) are not synchronized with the primary users (PUs). The SUs can opportunistically utilize the wireless channel as long as the channel is not occupied by the PUs. Due to the half-duplex nature of the wireless medium, the SUs may inevitably impose interference to the PUs because the SUs cannot precisely predict when the PUs becomes active again, and stop transmitting in time. To overcome this challenge, we propose an interference-confined adaptive transmission scheme, which can dynamically adjust the transmission duration of the SUs based on the statistical information of PUs' activity such as to confine the interference with PUs to the acceptable level. Also conducted are extensive simulations to validate and evaluate our proposed scheme. The simulation results show that our proposed scheme can significantly outperform the existing transmission schemes.

Due to the communications and energy constraints, we propose the decentralized-detection based schemes for Mobile Multi-Target Tracking (MMTT) in Wireless Sensor Networks (WSN). Our developed WSN consists of a symmetric-tree structure and a set of target detection and estimation strategies, which achieve the optimal error-exponent decay in detecting the number of the tracked targets. The increase in the target-number estimate accuracy also yields the high target-position estimate accuracy. We apply the Decentralized Probability Hypothesis Density (DPHD) filtering algorithm in deriving the global optimal threshold-levels for our proposed strategies to maximize the estimating accuracy for the positions of the tracked targets. The obtained extensive evaluation analyses validate and evaluate our proposed decentralized-detection structure of WSN and our developed target tracking strategies.

To address the Mobile Multiple Targets Tracking (MMTT) problem, we propose the two-tier Wireless Camera Sensor Network (WCSN) and the corresponding the sensor collaboration scheme, which is developed by the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filtering algorithm. In our proposed scheme, at each time step the tracked targets are regrouped, and the sensor with the best view of each group of the tracked targets is selected as the new Cluster Head (CH), which activates the sensors located within their communication ranges to be the cluster members. Furthermore, we also extend the single-directional WCSN to multiple-directional WCSN to improve the target-location estimate accuracy. Our simulation results validate the performances of our proposed dynamic node collaboration scheme.

Common deployment schemes in wireless sensor networks (WSNs) are built upon the assumption that the boundary effects can be ignored in the sensing field, which result in additional repair work to detect boundaries and redeem coverage holes along them. This repair not only breaks the uniformity of the deployed network, but also causes extra manual and material costs in practice. According to the geometry characteristics of a target area, we propose an innovative deployment scheme GRLD (Growth Rings Like Deployment), which can achieve coverage as well as connectivity without boundary effects. We also prove the full coverage and connectivity of this scheme and compare its efficiency with some popular regular deployment patterns, such as the square grid and the hexagon grid. Our work is the first to apply biological principles to the study and design of deployment schemes in WSNs, which avoids boundary effects efficiently and provides more adaptability.

We consider the time-unslotted cognitive radio based dynamic spectrum access (DSA) networks, where the secondary users (SUs) are unsynchronized with the primary users (PUs). We analytically derive the tradeoff between the energy consumption for spectrum sensing and the spectrum opportunity for SUs in terms of available spectrum usage time. We also consider the impact of the sensing errors, including missed detection and false alarm, on the energy consumption. Taking into consideration the sensing energy consumption, spectrum opportunities, and sensing errors, we propose a power-efficient periodic spectrum sensing scheme, which can save the sensing energy consumption while guaranteeing the priority of the PUs and the spectrum opportunity for the SUs. Also conducted are simulations to validate and evaluate our proposed sensing scheme.

We propose the self-organization based sensor collaboration scheme for the Mobile Multi-Target Tracking (MMTT) in our developed Decentralized Wireless Sensor Networks (DWSN). Our developed DWSN has three-tier hierarchical structure. At each time step, the Cluster Heads (CH) having the more information on the tracked targets are activated. The activated CHs receive the measurement-sets from their own cluster members, and obtain the local multi-target estimates based on these measurement-sets. Using the local target-state estimates, the CHs quantize the measurement-sets, and share with other activated CHs. The CHs that have the sufficient detection capabilities with respect to the same tracked targets are included into the same group and collaborate with each other to achieve the final target-position estimation. Since only the quantized measurement-sets are communicated between sensor-nodes, we also propose the Decentralized Cardinality Balanced Multi-Bernoulli (DCBMeMBer) filtering algorithm, which is based on the adaptive Huffman-tree scheme and is implemented by the Sequential Monte Carlo (SMC) method. The obtained extensive simulation results validate and evaluate our proposed self-organization-based sensor collaboration scheme and our developed DCBMeMBer filtering algorithm.

Due to the time-varying wireless channels, deterministic quality of service (QoS) is usually difficult to guarantee for real-time multi-layer video transmissions in wireless networks. Consequently, statistical QoS guarantees have become an important alternative in supporting real-time video transmissions. In this paper, we propose an efficient framework to model the statistical delay QoS guarantees, in terms of QoS exponent, effective bandwidth/capacity, and delay-bound violation probability, for multi-layer video transmissions over wireless fading channels. In particular, a separate queue is maintained for each video layer, and the same delay bound and corresponding violation probability threshold are set up for all layers. Applying the effective bandwidth/capacity analyses on the incoming video stream, we obtain a set of QoS exponents for all video layers to effectively characterize this delay QoS requirement.We then develop a set of optimal adaptive transmission schemes to minimize the resource consumption while satisfying the diverse QoS requirements under various scenarios, including video unicast/multicast with and/or without loss tolerance. Simulation results are also presented to demonstrate the impact of statistical QoS provisionings on resource allocations of our proposed adaptive transmission schemes.

The 20 papers in this special issue on wireless video transmissions are divided into four categories: wireless video streaming, optimization and scheduling; wireless video broadcast/multicast; retransmission techniques; and video quality assessment.

We integrate the effective capacity theory and game-theoretic approach to develop quality-of-service (QoS) driven power-allocation schemes in fading multiple-access channels (MAC). The effective capacity characterizes the capability of the wireless channels to support data transmission subject to the statistical delay QoS constraints, and the game-theoretic approach can efficiently handle the selfish behaviors of game users. In particular, each user allocates power in a selfish and noncooperative yet rational way, and its transmit power will be treated as the background noise to the other peer users. Under the above setup, we formulate the noncooperative power-allocation game, where each game user attempts to maximize its own effective capacity under the average power constraint. We focus on the two-user case and derive the Nash equilibrium and the corresponding power-allocation policy for this game. Furthermore, we show that as the delay QoS constraint becomes extremely loose, our power-allocation game reduces to the existing water-filling game. Also conducted are numerical and simulation analyses which compare the performance between our QoS-driven game based scheme and the water-filling game based scheme.

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for directional sensor networks. We first develop a directional sensing model: directional sensitivity model. Then, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function, and generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. Based on the above weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive simulations to validate and evaluate our proposed models and algorithms.

Since the unlicensed 60 GHz band has the extensively wide continuous spectrum and its corresponding millimeter-wave signal has high directivity gain, the 60 GHz band is a good option for the broadband wireless mesh networks. This paper focuses on link scheduling and routing over the 60 GHz multi-channel wireless mesh networks, where each mesh router has multiple radios and multiple directional antennas. We formulate a linear programming based framework, which incorporates multi-channel and multi-radio, directional antenna, and 60 GHz millimeter-wave communications, to model the network throughput of the directional antenna based 60 GHz mesh networks. Under this framework, we derive the solution to the problem of maximizing the network throughput subject to the fairness constraint and the directional-antenna based wireless channel interference constraint. Then, we design a heuristic joint link scheduling and routing scheme which aims at approximately attaining the optimal solution to the joint optimization problem under our proposed framework. We conduct extensive simulations to validate and evaluate our proposed scheme.

Cognitive radio technology is widely accepted as an efficient approach to solve the problem that the wireless spectrum has been getting scarcer and scarcer due to the rapid growth in the ubiquitous wireless applications. Several cognitive medium access control (MAC) protocols have been proposed for the secondary users (non-licensed users) to take advantage of the vacant channels whenever they are not occupied by the primary users (licensed users) in the wireless time division multiple access (TDMA)-based networks. In this paper, by exploiting the unique property of the wireless fading channel and cross-layer design technique, we develop a packet scheduling scheme for the primary users in the context of wireless TDMA networks, which is set up to operate friendly towards the secondary users in terms of vacant-channel probability. Our proposed scheme can be implemented with just slight modification on the traditional TDMA scheduling algorithm. We develop a rigorous queuing model and then quantitatively analyze the tradeoff among multiple performance metrics to identify when and where the cost for favoring the secondary users is worthy. The analytical results show that our proposed scheme can generate more vacant-channel opportunities for secondary users, at the expense of little increasing packet delay, as compared with the traditional wireless TDMA scheduling algorithm. In addition, since the implementation of our proposed scheme only needs little modification on the existing TDMA scheduling algorithm, our proposed scheme is a practical and cost-effective approach to increase the wireless spectrum utilization.

A novel congestion control protocol, ARROW-TCP, is proposed to address the issues of stability and convergence in existing transmission control protocols. Theoretical analysis shows that ARROW-TCP is globally stable and achieves exponential convergence to efficiency and fairness in a constant time. Meanwhile, ARROW-TCP obtains ideal performance of zero queuing delay, free packet loss by converging monotonically to the fair allocation and avoiding overshooting link capacity. Moreover, the price mechanism leverages ARROW-TCP into max-min rate allocation in hybrid multi-bottleneck networks. Finally, extensive simulations are conducted to verify our theoretical analysis and the simulation results demonstrate that ARROWTCP outperforms other transmission control protocols in terms of stability, convergence, and packet loss rate.

The fair allocation of the resources among different nodes is one of the critical problems in wireless mesh networks. Existing solutions mainly focus on rate-limitation policies or distributed fair MAC schemes at the potential expense of total network utilization. This paper investigates a special starvation problem among TCP flows that are different hops away from the BS, as well as the recently proposed solution, the "Minimum Content Window" policy based on IEEE 802.11e. It is found that the aggregate throughput degrades sharply because the effect of this policy on the TCP congestion mechanism has been overlooked. This paper proposes a priority-based congestion control by using "Cross-Layer Explicit Congestion Notification". Analysis and simulation results demonstrate that our scheme can improve the fairness of TCP flows while the aggregate throughput is at least 20% higher than the "Minimum Content Window" policy.

Effective capacity is a useful technique to characterize the system throughput with statistical delay-constrained quality of service (QoS) guarantees. In this paper, we integrate effective capacity theory into superposition-coding (SPC) based multicast transmission to devise the efficient channel-aware multicasting scheme in wireless networks. Specifically, we propose to optimize the effective capacity for multicast transmissions subject to the specified loss-rate constraint and the statistical delay QoS requirement in terms of the QoS exponent. We use superposition coding in wireless multicast to handle heterogeneous channel fading across multicast receivers, and apply the pre-drop strategy to gain more flexible rate control. Under our proposed framework, we derive the optimal pre-drop strategy and the optimal power/rate allocation for each layer of superposition coding. Simulation analyses present insightful observations on tradeoff between effective capacity and the QoS requirements, and demonstrate the superiority of the SPC-based scheme over the existing time-sharing (TS) based multicast scheme.

In wireless sensor networks, most existing works on region coverage mainly concentrate on the omnidirectional sensor based full coverage, which ensures that all points in the sensor-deployed region are covered. In contrast, this paper studies the problem of exposure-path prevention for the region coverage in directional sensor networks. Because the exposure paths are prevented as long as no moving objects or phenomena can go through a sensor-deployed region without being detected, exposure-path prevention does not require full coverage, and instead it only needs the partial coverage. Towards this end, we apply the percolation theory to solve the exposure path problem for directional sensor networks. In particular, we map the exposure path problem into a sector based percolation model, and then derive the bounds of critical density where directional sensors are deployed according to a 2-dimensional Poisson process. Also conducted is a set of extensive simulations to validate and evaluate our developed models and schemes.

Wireless network coding (NC) has emerged as a promising technology that improves network throughput and spectrum efficiency. How large can the coding gain be? In this paper, we study the network throughput gains of two types of wireless NC schemes, the conventional wireless NC and the analog NC, respectively, over the traditional non-NC scheme in multi-hop, multi-channel, and multi-radio wireless networks. In particular, we propose an analytical framework, which can exploit the best coding opportunities among all the possible realistic ones, for deriving the network throughput gains of the wireless NC schemes. By solving the problem of maximizing the network throughput subject to the fairness requirements under our proposed framework, we quantitatively analyze the network throughput gains of these two types of wireless NC schemes under various wireless network topologies.

In order to prevent worms from propagating rapidly, it is essential to generate worm signatures quickly and accurately. However, most of recent approaches can not generate accurate signatures for polymorphic worms in environments with noise. In this paper, we present a signature generation algorithm, namely CCSF (color coding signature finding), for polymorphic worms based on color coding. CCSF divides n sequences into m groups and each group contains 20 sequences. Firstly, CCSF generates signatures for each group by adopting color coding and filters them. Then all reserved signatures are clustered to get rid of redundant substrings. In this approach, signature can be generated without any fragment in environments with noise, and it can be used in IDS (intrusion detection system) to detect polymorphic worm. We perform extensive experiments to demonstrate the effectiveness of our approach. Experiment results show distinct advantages in generating accurate signatures over other existed approaches.

Due to the highly-varying wireless channels, deterministic quality of service (QoS) is usually difficult to guarantee for real-time multi-layer video transmissions in wireless networks. Consequently, statistical QoS guarantees have become an important alternative in supporting real-time video transmissions. In this paper, we propose an efficient framework to model the statistical delay QoS guarantees, in terms of QoS exponent, effective bandwidth/capacity, and delay-bound violation probability, for multi-layer video transmission over wireless fading channels. In particular, a separate queue is maintained for each video layer, and the same delay bound and corresponding violation probability threshold are set up for all layers. Applying the effective bandwidth/capacity analyses on the incoming video stream, we obtain a set of QoS exponents for all video layers to effectively characterize this delay QoS requirement. We then develop a set of optimal adaptive transmission schemes to minimize the resource consumption while fulfilling the diverse QoS requirements under various scenarios, including video unicast/multicast with and/or without loss tolerance. Simulation results are also presented to demonstrate the impact of statistical QoS provisionings on resource allocations of our proposed adaptive transmission schemes.

Compared to the other types of sensor networks, the wireless camera sensor networks can offer much more comprehensive and accurate information in mobile target tracking applications. We propose a dynamic node collaboration scheme for mobile target tracking in wireless camera sensor networks. Unlike the traditional sensing models, we develop a nonlinear localization-oriented sensing model for camera sensors by taking the perspective projection and the observation noises into account. Based on our sensing model, we apply the sequential Monte Carlo (SMC) technique to estimate the belief state of the target location. In order to implement the SMC based tracking mechanism efficiently, we propose a dynamic node collaboration scheme, which can balance the tradeoff between the quality of tracking and the network cost. Our scheme deploys the dynamic cluster architecture which mainly includes the following two components. First, we design a scheme to elect the cluster heads during the tracking process. Second, we develop an optimization-based algorithm to select an optimal subset of camera sensors as the cluster members for estimating the target location cooperatively. Also conducted is a set of extensive simulations to validate and evaluate our proposed schemes.

In conventional directional sensor networks, coverage control for each sensor is based on a 2D directional sensing model. However, 2D directional sensing model cannot accurately characterize the actual application scene of image/video sensor networks. To remedy this deficiency, we propose a 3D directional sensor coverage-control model with tunable orientations. In order to improve the efficiency of target-detecting, we develop a virtual potential-field based coverage-enhancing scheme to improve the coverage performance. Furthermore, we apply the simulated annealing algorithm for objective optimization. The extensive simulations show the effectiveness of our proposed 3D sensing model and coverage enhancing method.

We propose the cross-layer based battery-aware time division multiple access (TDMA) medium access control (MAC) protocols for wireless body-area monitoring networks in wireless healthcare applications. By taking into account the joint effect of electrochemical properties of the battery, time-varying wireless fading channels, and packet queuing characteristics, our proposed schemes are designed to prolong the battery lifespan of the wireless sensor nodes while guaranteeing the reliable and timely message delivery, which is critically important for the patient monitoring networks. In addition, we develop a Markov chain model to analyze the performance of our proposed schemes. Both the obtained analytical and simulation results show that our proposed schemes can significantly increase the battery lifespan of sensor nodes while satisfying the reliability and delay-bound quality of service (QoS) requirements for wireless body-area monitoring networks. Furthermore, the case study of the electrocardiogram (ECG) monitoring application shows that besides meeting the delay requirements, our proposed schemes outperform the IEEE 802.15.4 and Bluetooth protocols in terms of battery lifespan.

In this paper, we model the network throughput gains of two types of wireless network coding (NC) schemes, including the conventional NC and the analog NC schemes, over the traditional non-NC transmission scheduling schemes in multihop, multi-channel, and multi-radio wireless ad hoc networks. In particular, we first show that the network throughput gains of the conventional NC and analog NC are (2n)/(2n-1) and n/(n-1), respectively, for the n-way relay networks where n ges 2. Second, we propose an analytical framework for deriving the network throughput gain of the wireless NC schemes over general wireless network topologies. By solving the problem of maximizing the network throughput subject to the fairness requirements under our proposed framework, we quantitatively analyze the network throughput gains of these two types of wireless NC schemes for a variety of wireless ad hoc network topologies with different routing strategies. Finally, we develop a heuristic joint link scheduling, channel assignment, and routing algorithm that aims at approaching the optimal solution to the optimization problem under our proposed framework.

Video multicast over multirate wireless networks imposes great challenges in designing efficient rate adaptation schemes due to the multi-layer hierarchical video coding and heterogeneous fading across multicast receivers. We propose a cross-layer framework for rate selection in video multicast, where different video layers have diverse loss-rate QoS requirements and thus may employ different transmission rates varying with the channel state information (CSI). Specifically, we first propose an orthogonal frequency-division multiple access (OFDMA) - code division multiple access (CDMA) based scheme to avoid feedback collisions while efficiently identifying CSI from all multicast receivers. Second, we develop a queue management strategy such that the transmissions for different video layers can be synchronized. Third, we design the rate adaptation scheme to control receivers' loss rates at each video layer not to exceed the thresholds specified by upper protocol layers. Also conducted are simulation results to show the effectiveness of our designed rate-adaptation scheme.

Most existing works on sensor coverage mainly concentrate on the full coverage models which ensure that all points in the deployment region are covered at the expense of high complexity and cost. In contrast, the exposure-path prevention does not require full coverage sensor deployment, and instead it only needs the partial coverage, because the exposure paths are prevented as long as no moving objects or phenomena can go through a deployment region without being detected. Towards this end, we focus on the partial coverage by applying the percolation theory to solve the exposure path problem for wireless sensor networks. We propose abond-percolationtheorybased scheme by mapping the exposure path problem into a bond percolation model. Using this model, we derive the critical density where sensors are deployed according to a 2-dimensional Poisson process. We evaluate our proposed model by simulations.

One of the critical problems for the wireless sensor networks is how to prolong the battery lifespan of the wireless sensor nodes. We proposed the cross-layer based battery-aware time division multiple access (TDMA) scheduling schemes for wireless sensor networks, which aim at maximizing the battery capacity utilization of wireless sensor nodes while satisfying the delay quality of service (QoS) requirements. Our proposed schemes take into account the joint effect of electrochemical properties of the battery, time-varying wireless fading channels, and packet queuing management. We develop a Markov chain model to analyze the performance of our proposed schemes. Based on the analytical model, we identify the tradeoff between the battery lifespan and the packet transmission delay. The analytical and simulation results show that our proposed schemes can significantly increase the battery lifespan of sensor nodes at the expense of acceptable increase of packet transmission delay.

In mobile multicast over cellular networks, the wireless fading channels for different multicast receivers are usually heterogeneous, from both instantaneous and statistical perspectives. This feature imposes significant challenges in designing efficient multicast schemes to adapt to the channel variations. We investigate the adaptive rate and power control for multicast over generally distributed broadcast fading channels in cellular networks, with the focus on the two-receiver case. Specifically, we aim at optimizing the minimum achievable average good put over all receivers through time-sharing based rate adaptation under the fixed/variable power strategies. By developing an SNR-plane partition based method, we derive the optimal rate adaptation policy for the constant (fixed) power strategy. Moreover, we obtain the optimal power-rate adaptation policy for the variable power strategy by using the Lagrangian duality theory. Simulation results are also presented to show the performance gain obtained from our derived optimal policies.

Recently, smart surveillance is becoming a very important application of camera sensor networks. Most face recognition and tracking techniques employed in surveillance systems rely on the assumption of a frontal view of the human face. In order to detect the precise face orientation by cooperation among multiple cameras, we need to guarantee that each point in monitored region be covered by more than one camera. In this paper, we address the problem of Directional if-Coverage (DKC) in camera sensor networks. The DKC problem is different from that in conventional sensor networks due to the two aspects: directionality of sensing model and effective sensing. In particular, we propose a mathematical model to describe the relation among the number of cameras deployed randomly, the range of effective sensing angle, and the rate of directional if-coverage. Finally, we use simulations to show the effectiveness of deploying our model in practical scenarios.

We propose the adaptive resource allocation schemes for multiuser downlink quality-of-service (QoS) provisionings over broadcast fading channels in time-division (TD) based cellular wireless networks. Specifically, we apply the effective capacity theory to control the service rates with the diverse delay QoS requirements for different mobile users. Subject to the proportional- effective-capacity constraint and the diverse statistical delay-QoS requirements over different downlink users, we formulate the sum effective capacity maximization problem via channel-aware power and time-slot allocation. We decompose the above optimization problem into two sub-problems and then derive the optimal power and time-slot adaptation policy. We also develop a suboptimal scheme called the equal-length TD policy. Simulation results are presented to show the impact of QoS provisionings on the resource allocation across different users and on the network performance.

In order to increase node lifetime and system throughput, the topology of wireless Ad Hoc networks can be controlled by changing the transmission power at each node. In this paper, we propose an energy-e±cient distributed topology- control algorithm, Varying Cone on Gabriel Graph (VCGG). By selectin

Target localization is an important application in sensor networks. Although the localization problem in acoustic /radar sensor networks has been intensively studied, few consider the problem in camera sensor networks. In this paper, we propose a method to utilize the active cameras to localize the target collaboratively. We describe the perspective projection of target firstly, and then utilize the Gaussian error model to establish a more practical target projection model. On the basis of the target projection model, we present a vision-based localization model, and use Shannon entropy to quantify the localization gain due to the observation of camera. Case study demonstrates the validity and efficiency of our design.

Camera sensor networks have recently emerged as a very critical research topic. Target tracking and localization are important applications in camera sensor networks. In this paper, we investigate the coverage problem from the perspective of target localization in camera sensor network, compare to the coverage problem for target detection has been intensively studied. We first propose a novel localization-oriented sensing model based on the perspective projection of the camera. Then, assuming that the camera sensors are deployed as a random uniform process, we study how the probability of the localization- oriented coverage (L-coverage for short) changes with the number of sensors or some other factors. Finally, we use simulations to validate our theoretical analysis and demonstrate the boundary effect onL-coverage probability. The obtained results show that our model can be effectively deployed in practical scenarios.

Target tracking and localization are important applications in camera sensor networks. Although coverage is a very important research topic in wireless sensor networks, and the coverage problem for target detection has been intensively studied, few considers the coverage problem from the perspective of target localization. In this paper, we investigate the coverage problem from the perspective of target localization for camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera. Then, we propose a new notion of localization-oriented coverage (L-coverage for short). We assume that all camera sensors make the measurements independent of other in the field, and these camera sensors can cooperate to make an accurate estimation for the location of the target. In addition, the relationships among L-coverage, 2-coverage, and the density of camera sensors are also discussed in this paper. The obtained results show that our model can be effectively deployed in many practical scenarios.

Cognitive radio technology has emerged as the novel and effective approach to improve the utilization of precious radio spectrum. Employing the cognitive radio technology, secondary (unlicensed) users can opportunistically utilize the unused licensed spectrum in a way that constrains the level of interference to the primary (licensed) users. However, there are many new challenges associated with cognitive radio based wireless networks, such as the multi-channel hidden terminal problem and the fact that the time-varying channel availability is different for different secondary users, in the medium access control (MAC) layer. To overcome these challenges, we propose an efficient Cognitive Radio-EnAbled Multi-channel MAC (CREAM-MAC) protocol, which integrates the spectrum sensing at physical layer and packet scheduling at MAC layer, over the wireless networks. Under the proposed CREAM-MAC protocol, each secondary user is equipped with a cognitive radio-enabled transceiver and multiple channel sensors. The proposed CREAM-MAC enables the secondary users to best utilize the unused frequency spectrum while avoiding the collisions among secondary users and between secondary users and primary users. In addition, we develop the analytical models to quantitatively analyze our proposed CREAM-MAC protocol in the saturated network case. We also conduct simulation experiments to validate our developed analytical models.

When transmitting stochastic traffic flows over wireless networks, there exists an inherent tradeoff between average transmit power and corresponding queuing-delay bound. In this paper, we investigate such a trade-off and show how average power increases as delay-bound requirement becomes stringent. Specifically, we propose the resource allocation schemes to minimize the power consumption subject to a delay quality-of-service (QoS) constraint, where the delay constraint is in terms of queue-length decaying rate when an arrival traffic is transmitted through the wireless networks. We focus on orthogonal-frequency-division-multiplexing (OFDM) communications under three different network infrastructures, namely, point-to-point link, multihop amplify-and-forward (AF) network, and multiuser cellular network. We derive the optimal resource allocation policies for each scenario, and compare their performances with other existing resource-allocation policies. The obtained simulation and numerical results show that using our proposed optimal resource-allocation policies, significant power saving can be achieved. Furthermore, our OFDM-based communications systems can significantly save the power consumption, especially under stringent delay constraint.

We propose the cross-layer based opportunistic multi-channel medium access control (MAC) protocols, which integrate the spectrum sensing at physical (PHY) layer with the packet scheduling at MAC layer, for the wireless ad hoc networks. Specifically, the MAC protocols enable the secondary users to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. In our proposed protocols, each secondary user is equipped with two transceivers. One transceiver is tuned to the dedicated control channel, while the other is designed specifically as a cognitive radio that can periodically sense and dynamically use the identified un-used channels. To obtain the channel state accurately, we propose two collaborative channel spectrum-sensing policies, namely, the random sensing policy and the negotiation-based sensing policy, to help the MAC protocols detect the availability of leftover channels. Under the random sensing policy, each secondary user just randomly selects one of the channels for sensing. On the other hand, under the negotiation-based sensing policy, different secondary users attempt to select the distinct channels to sense by overhearing the control packets over the control channel. We develop the Markov chain model and the M/GY/1-based queueing model to characterize the performance of our proposed multi-channel MAC protocols under the two types of channel-sensing policies for the saturation network and the non-saturation network scenarios, respectively. In the non-saturation network case, we quantitatively identify the tradeoff between the aggregate traffic throughput and the packet transmission delay, which can provide the insightful guidelines to improve the delay-QoS provisionings over cognitive radio wireless networks.

We propose a cross-layer-model based adaptive resource-allocation scheme for the diverse quality-of-service (QoS) guarantees over downlink mobile wireless networks. Our proposed scheme dynamically assigns power-levels and time- slots for heterogeneous real-time mobile users to satisfy the variation of statistical delay-bound QoS requirements. To achieve this goal, we apply Wu and Negi's effective capacity approach to derive the admission-control and power/time-slot allocation algorithms, guaranteeing the statistical delay-bound for heterogeneous mobile users. When designing such an algorithm, we study the impact of physical-layer issues such as adaptive power-control and channel-state information (CSI) feedback delay on the QoS provisioning performance. Through numerical and simulation results, we observe that the adaptive power adaptation has a significant impact on statistical QoS-guarantees. In addition, the analyses indicate that our proposed resource-allocation algorithms are shown to be able to efficiently support the diverse QoS requirements for various real-time mobile users over different wireless channels. Also, in an in-door mobile environment, e.g., the widely used wireless local-area networks (WLAN), our proposed algorithm is shown to be robust to the CSI feedback delay.

Abstract In order to support the quality‐of‐service (QoS) requirements for real‐time traffic over broadband wireless networks, advanced techniques such as space‐time diversity (STD) and multicarrier direct‐sequence code division multiple access (MC‐DS‐CDMA) are implemented at the physical layer. However, the employment of such techniques evidently affects the QoS provisioning algorithms at the medium access control (MAC) layer. In this paper, we propose a space‐time infrastructure and develop a set of cross‐layer real‐time QoS‐provisioning algorithms for admission control, scheduling, and subchannel‐allocations. We analytically map the parameters characterizing the STD onto the admission‐control region guaranteeing the real‐time QoS. Our analytical analyses show that the proposed algorithms can effectively support real‐time QoS provisioning. Also presented are numerical solutions and simulation results showing that the STD can significantly improve the QoS provisioning for real‐time services over wireless networks. Copyright © 2007 John Wiley & Sons, Ltd.

We investigate cross-layer design based rate control for mobile multicast over broadcast fading channels in cellular networks. Specifically, subject to a statistical loss-rate quality- of-service (QoS) constraint required by upper protocol layers, we aim at maximizing the physical-layer throughput by taking advantage of transmission-rate adaptation based on the channel state information (CSI). Using the concept of convex hull and instantaneous sum goodput - throughput gain (IGTG), we derive the optimal rate adaptation policy for the above maximization problem. Simulation results are also provided to show the significant performance gain achieved by our derived policy over the existing fixed dominating-position (FDP) policy.

We propose the cognitive-radio-based multi-channel medium access control (MAC) protocols, which integrate the spectrum sensing and packet scheduling, for the wireless ad hoc networks. Specifically, the cognitive MAC protocols allow secondary users to identify and use the unused frequency spectrum in a way that constrains the level of interference to the primary users. Under our proposed protocols, each secondary user is equipped with two transceivers. One of the transceivers operates on a dedicated control channel, while the other is used as a cognitive radio that can periodically sense and dynamically utilize the identified unused channels. To obtain the channel status accurately, we propose two types of collaboratively channel- sensing policies, namely random sensing policy and negotiation- based sensing policy, to help the MAC protocols detect the unused channels. We also develop the analytical models to study the proposed MAC protocols with the two types of channel-sensing policies.

Virus propagation in the Internet has resulted in significant loss and security breaches. Although significant research effort has been spent on developing antivirus software tools, the propagating dynamics of virus and antivirus is not thoroughly investigated. Both virus and antivirus have similar propagation characteristics to some extent counteracting each other. The spreading dynamics of most viruses are network topology dependent making the investigation a challenging problem. In this paper, we model and analyze the spreading characteristics of viruses as coexisting with the anti-virus spreading process in a modified two-layer small-world topology for hybrid wireless ad hoc and wired networks. We simulate our proposed virus and antivirus model over the hybrid wireless ad hoc and wired networks and arrive at conclusion which can be used by for developing cost effective antivirus solutions.

We propose a power and constellation-size adaptation scheme for the multicast system employing multilevel quadrature amplitude modulation (MQAM) with discrete constellation sizes, which aims at achieving high multicast goodput over wireless fading channels. Specifically, we formulate the optimization problem to maximize multicast goodput via adaptive transmission-mode selection, including power and constellation-size adaptation, and then solve for the optimal adaptation policy through two steps. We first investigate the goodput variation with the transmission-mode selection and equivalently convert the optimization problem into a concave-like form by introducing the concepts of effective marginal goodput gain (EMGG) and adjacent EMGG. Then, we derive the optimal transmission adaptation policy by using a global adjacent EMGG threshold. Finally, simulation results are provided to show the significant multicast goodput improvement gained by applying the optimal power and constellation-size adaption.

We propose a spreading code medium access control (MAC) protocol with each node equipped with single transceiver for wireless ad hoc networks, based on the code division multiple access (CDMA) techniques. Our proposed scheme adopts the time division method to solve the near-far problem inherently associated with the CDMA-based networks. Specifically, by employing the ad-hoc traffic indication messages (ATIM) phase to properly determine the required transmission power for data packets, our scheme enables the interference-limited simultaneous transmissions to achieve the high utilization of the limited/precious bandwidth in wireless ad hoc networks. In addition, our scheme requires only one transceiver per node, which reduces the hardware costs for large scale wireless ad hoc networks. We use the Markov chain model to characterize the throughput of the proposed scheme. Based on the numerical and simulation evaluations, we thoroughly investigate the impact of various parameters on the performance of our proposed scheme.

We propose the QoS-driven opportunistic cooperation schemes for the wireless relay networks. By integrating information theory with the concept of effective capacity, our schemes aim at maximizing the relay network throughput subject to a given delay QoS constraint. Applying the time division multiple access (TDMA) and frequency division multiple access (FDMA) techniques to the relay networks, we derive a set of optimal static and dynamic time/bandwidth allocation strategies under either constant or dynamic power control. When the power allocation is constant, we prove that the FDMA based schemes outperform the TDMA based schemes. On the other hand, when the power allocation is dynamically adjusted, we observe that the TDMA based schemes offer the same effective-capacity performance as the FDMA based schemes. We also analytically characterize the impact of the wireless-relay-network topologies and QoS requirements on the effective-capacity performances of our proposed strategies. The extensive simulation experiments show that our proposed strategies significantly outperform the existing equal-time or equal-bandwidth allocation strategies in terms of the effective capacity.

Applying the code division multiple access (CDMA) techniques, we propose an efficient medium access control (MAC) protocol with single-transceiver for wireless ad hoc networks. Our protocol adopts the time-division method to solve the near-far power control problem inherently associated with the CDMA-based networks. In particular, employing the variable ad-hoc traffic indication messages (ATIM) window to properly determine the required transmission power for data packets, our scheme enables the interference-limited simultaneous transmissions to achieve the high utilization of the limited/precious bandwidth in wireless networks. In addition, our scheme requires only one transceiver per node, which reduces the hardware costs for large scale wireless networks. Using the Markov-chain techniques, we develop an analytical model to evaluate the aggregate throughput under our protocol. Both the analytical and simulation results show that our protocol can improve the network throughput significantly as compared with other existing schemes.

We propose the quality-of-service (QoS) driven power allocation schemes for parallel fading channels when considering imperfect channel estimations. In particular, the parallel communication model plays a fundamental role in physical-layer evolutions of wireless networks. By integrating information theory with the concept of effective capacity, our proposed schemes aim at maximizing the system throughput subject to a given delay constraint. Solving the original non-convex problem by a 2-dimensional convex optimization approach, we develop the optimal allocation algorithms under different QoS and power constraints. Consistent with our previous work assuming perfect channel state information (CSI), our analyses considering imperfect CSI demonstrate that when the QoS constraint becomes more and more stringent, the optimal effective capacity decreases from the ergodic capacity to the zero-outage capacity. Moreover, our results indicate that the channel estimation error has a significant impact on QoS provisioning, especially when the delay constraint is stringent. Specifically, as long as the channel estimation is not perfect, a positive zero-outage capacity is unattainable. On the other hand, our simulations also suggest that a larger number of parallel channels can provide higher throughput and more stringent QoS, while offering better robustness against the imperfectness of CSI.

We propose a power adaptation scheme for downlink multiuser communications over parallel fading channels in mobile wireless networks. This model can be applied to the case of time division or frequency division downlink transmission over broadcast channels in cellular networks. Specifically, our proposed scheme aims at optimizing the sum of the weighted effective capacity over all users each with a specified statistical delay quality of services (QoS) constraint, called QoS exponent. Using a two-step method, we derive the optimal power adaptation policy for the above problem. Numerical evaluations are also presented to investigate the impact of the delay QoS on optimizing the weighted sum effective capacity and that on the power allocation across different users.

The paper proposed a power and rate adaptation scheme for multicastings in wireless networks by taking advantage of the broadcast nature of wireless channels. Specifically, the proposed scheme aims at achieving high system goodput by dynamically allocating the transmit power and data transmission rate jointly based on all multicast receiver's channel state information (CSI). Formulating the multicast goodput maximization problem to derive the rate and power adaptation policy, we observe that this goodput maximization is a non-concave optimization problem. By identifying the fact that the optimal power policy has the water-filling form, and introducing the concept of effective marginal goodput gain, the original goodput maximization was converted into an equivalent concave optimization problem, and then obtain its optimal solution. Also presented are the simulation analyses which show the performance improvement gained by our developed optimal power and rate adaptation policy.

Because of the inherent constraints imposed by sensor nodes themselves, prolonging the span of network lifetime for the wireless sensor networks (WSNs) introduces many new challenges. In this paper, we consider the cluster-based heterogeneous WSNs, which consist of two types of nodes, namely, the powerful cluster-heads and the inexpensive sensor nodes. In particular, in the WSN, the sensor nodes are deployed along the grid points. To better balance the energy consumption, the sensor nodes exchange data with the cluster-heads through mixed communication modes, i.e., they can communicate with cluster-heads by either single-hop or multi-hop mode. Given the initial energy of the sensor nodes, we develop the analytical models to derive the optimal communication range and identify the optimal mixed communication modes to maximize the span of WSN's lifetime. Also presented are the simulation results which verify our developed analytical models.

We propose a set of resource allocation strategies for quality-of-service (QoS) provisioning over the wireless relay networks. By integrating information theory with the concept of effective capacity, our proposed strategies aim at maximizing the relay network throughput subject to a given delay QoS constraint. Applying the time division multiple access (TDMA) and frequency division multiple access (FDMA) to the relay networks, we develop the static and dynamic time/bandwidth allocation strategies with constant power. The simulation results show that our proposed strategies can significantly improve the effective capacity as compared to the conventional equal time/bandwidth allocation strategies. We also observe that the relay location can impact the improvement of the effective capacity, and the FDMA based scheme outperfoms the TDMA based scheme.

While TCP (transmission control protocol) is an efficient transport protocol in the wired Internet, it performs poorly when used in wireless environments. This is because TCP couples the error and flow control by using packet loss to infer the network congestion and thus the random loss in wireless Internet can inevitably mislead TCP dropping its flow-control window unnecessarily, even if the network is not congested at all. To overcome this problem, we propose the second-order rate-based flow control and the decoupled window-based error-control schemes for high-throughput transport protocols over the wireless networks. The second-order rate control minimizes congestive losses by using the explicit congestion notification (ECN)-bit feedback to adapt the rate-gain parameter to the variations of the round-trip time (RTT) and cross-traffic flows. The error-control scheme detects and selectively retransmits the lost packets caused by either congestion or random-noise/handoffs on wireless links, which is decoupled from the flow control such that the rate control is independent of the random loss of wireless links. Using the fluid analysis, we establish the rate-control model, and derive expressions for throughput, losses, and link-transmission efficiency. Through extensive simulations, the proposed transport protocol is shown to possess the TCP-compatibility in bandwidth while coexisting with TCP-Reno traffics in the wired Internet. Our simulations also verify the analysis, and demonstrate the significant superiority of our scheme to TCP in terms of increasing the average throughput over wireless links and the robustness to the variation of wireless random-loss probability while minimizing the losses and retransmissions.

The authors propose a physical-datalink cross-layer resource allocation scheme over wireless relay networks for quality-of-service (QoS) guarantees. By integrating information theory with the concept of effective capacity, the proposed scheme aims at maximizing the relay network throughput subject to a given delay QoS constraint. This delay constraint is characterized by the so-called QoS exponent thetas, which is the only requested information exchanged between the physical layer and the datalink layer in our cross-layer design based scheme. Over both amplify-and-forwards (AF) and decode-and-forward (DF) relay networks; the authors develop the associated dynamic resource allocation algorithms for wireless multimedia communications. Over DF relay network, the authors also study a fixed power allocation scheme to provide QoS guarantees. The simulations and numerical results verify that our proposed cross-layer resource allocation can efficiently support diverse QoS requirements over wireless relay networks. Both AF and DF relays show significant superiorities over direct transmissions when the delay QoS constraints are stringent. On the other hand, the results demonstrate the importance of deploying the dynamic resource allocation for stringent delay QoS guarantees.

We propose a quality-of-service (QoS) driven power and rate adaptation scheme over wireless links in mobile wireless networks. Specifically, our proposed scheme aims at maximizing the system throughput subject to a given delay QoS constraint. First, we derive an optimal adaptation policy by integrating information theory with the concept of effective capacity for a block fading channel model. Our analyses reveal an important fact that there exists a fundamental tradeoff between throughput and QoS provisioning. In particular, when the QoS constraint becomes loose, the optimal power-control policy converges to the well-known water-filling scheme, where Shannon (ergodic) capacity can be achieved. On the other hand, when the QoS constraint gets stringent, the optimal policy converges to the total channel inversion scheme under which the system operates at a constant rate. Inspired by the above observations, we then consider a more practical scenario where variable-power adaptive modulation is employed over both block fading and Markov correlated fading channels. In both cases, we derive the associated power and rate adaptation policies. The obtained results suggest that the channel correlation has a significant impact on QoS-driven power and rate adaptations. The higher the correlation is, the faster the power-control policy converges to the total channel inversion when the QoS constraint becomes more stringent. Finally, we conduct simulations to verify that the adaptation policy proposed for Markov channel models can also be applied to the more general channel models.

We propose a quality-of-service (QoS) driven power and rate adaptation scheme for multichannel communications systems over wireless links. In particular, we use multichannel communications to model the conceptual architectures for either diversity or multiplexing systems, which play a fundamental role in physical-layer evolutions of mobile wireless networks. By integrating information theory with the concept ofeffective capacity, our proposed scheme aims at maximizing the multichannel-systems throughput subject to a given delay-QoS constraint. Under the framework of convex optimization, we develop the optimal adaptation algorithms. Our analyses show that when the QoS constraint becomes loose, the optimal power-control policy converges to the well-known water-filling scheme, where the Shannon (or ergodic) capacity can be achieved. On the other hand, when the QoS constraint gets stringent, the optimal policy converges to the scheme operating at a constant-rate (i.e., the zero-outage capacity), which, by using only a limited number of subchannels, approaches the Shannon capacity. This observation implies that the optimal effective capacity function decreases from the ergodic capacity to the zero-outage capacity as the QoS constraint becomes more stringent. Furthermore, unlike the single-channel communications, which have to trade off the throughput for QoS provisioning, the multichannel communications can achieve both high throughput and stringent QoS at the same.

We propose a cross-layer approach to investigate the impact of physical-layer infrastructure on data-link-layer quality-of-service (QoS) performance over wireless links in mobile networks. At the physical layer, we take multiple-input-multiple-output (MIMO) diversity schemes as well as adaptive-modulation-and-coding (AMC) techniques into account. At the data-link layer, our focus is on how this physical-layer infrastructure influences the real-time multimedia delay-bound QoS performance. To achieve this goal, we first model the physical-layer service process as a finite-state Markov chain (FSMC). Based on this FSMC model, we then characterize the QoS performance at data-link-layer using the effective capacity approach, which turns out to be critically important for the statistical QoS guarantees over wireless links in mobile networks. We also investigate the impact of physical-layer power control and channel-state information (CSI) feedback delay on the QoS performance. The numerical results obtained demonstrate that our proposed cross-layer model can efficiently characterize the interactions between the physical-layer infrastructure and data-link-layer QoS performance.

Under the DSRC architecture, we propose a cluster-based multi-channel communication scheme to provision the quality of service (QoS), especially the real-time delivery of safety traffics, over the vehicle ad hoc networks (VANET). Our scheme integrates the clustering algorithm, contention-free, and contention-based MAC to support the real-time transmission of safety-messages. We develop the analytical model to conduct the safety message delay analyses. Our analyses show that our scheme can guarantee the real-time transmission of safety messages. The analytical results are also verified by our simulation experiments.

Providing packet fair queueing is difficult in CDMA2000 1xEV-DV networks since there are multiple sessions associated with each user and the packets from each session can be served by multiple code channels. Essentially, this is a multi-session multi-server problem, which so far has received little research. The above problem becomes more complicated in CDMA2000 1xEV-DV networks, where Adaptive Modulation and Coding (AMC) schemes are employed for a higher network resource utilization. This means that the underlying service data rate of each code channel will be dynamically changing with the current perceived channel quality and the current number of available code channels. However, current research on packet fair queueing in wireless networks mostly use fixed service data rates. Therefore, how to design a packet fair queueing scheme with multiple servers and time-varying service data rates is very important for QoS provisioning in CDMA2000 1xEV-DV networks. In this paper, we propose a new packet fair queueing algorithm, where it seamlessly integrates the fair queueing with the CDMA2000 1xEV-DV AMC scheme. Both theoretical analyses and simulation results show that the proposed fair queueing algorithm can provide fairness among different sessions as well as satisfy their QoS requirements.

We investigate the variations of using the different transmit-diversity techniques for multicast in mobile wireless networks. In particular, we propose and study two different schemes using transmit-diversity for mobile multicast. First, we propose the antenna-beamforming-based mobile multicast scheme which derives the transmit antenna weights by formulating/solving an optimization problem to minimize the average Symbol Error Rate (SER) over all multicast receivers. The simulations results show that our proposed beamforming-based scheme achieves lower SER compared with the existing beamforming-based mobile multicast algorithms which either maximize the instantaneous combined Signal to Noise Ratio (SNR) averaged over all receivers, or maximize the worst-case instantaneous combined SNR among all multicast receivers. Second, we apply the antenna-selection scheme in mobile multicasting to achieve the low average SER. We identify that the antenna-selection scheme is suitable for the scenario where different receivers/receiver-groups in a multicast session, experiencing different channel qualities, need to receive different error-control data. The analyses and numerical results show that the proposed scheme improves the SER performance through selecting antennas with better channel qualities and can flexibly support various antenna configurations.

One of the most crucial requirements of a wireless ad-hoc network is to work efficiently and reliably over a long period of time. It is important to design a control technique that minimizes the transmission power of each mobile node without losing much of the performance while retaining the fairness of the network. The contribution of this paper is twofold. First, we show that the transmission energy consumption and the media access delay can be expressed as a function of the conditional collision probability in the IEEE 802.11 MAC protocol. Based on the analyses, a fundamental tradeoff between the system energy consumption and its performance can be found by tuning up the system control parameter CWmin. Second, we propose an enhanced energy-aware scheme based on the IEEE 802.11 protocol. The simulation results show that this technique is more energy efficient than the original IEEE 802.11 protocol while keeping a similar media access delay and a better fairness.

We propose a Quality-of-Service (QoS) driven power- and rate-control scheme in mobile wireless networks. Specifically, the proposed scheme aims at maximizing the system throughput subject to a given delay QoS constraint. Over the block fading channel, we derive the optimal adaptation policy by integrating information theory with the concept of effective capacity. The analyses demonstrate that there exists a fundamental tradeoff between spectral efficiency and QoS provisioning. When the QoS constraint is loose, the optimal power control policy converges to the well-known water-filling scheme, where the Shannon capacity can be achieved. On the other hand, when the QoS constraint is stringent, the optimal policy converges to the total channel inversion such that the system operates with a constant service rate.

We investigate adaptive power and rate allocation schemes for multicast over fading channels in wireless networks. Specifically, we take advantage of the superposition coding to achieve high multicast throughput, where the transmitted signal consists of multiple parallel sub-stream signals. Each receiver decodes the sub-stream signals within its decoding capability determined by the received signal to noise ratio (SNR). Under the above architecture, we first formulate the multicast throughput optimization problem to obtain the power allocation policy for each sub-stream signal subject to the total power constraint. Also, we develop a selective retransmission strategy to guarantee all receivers to recover the same data copy without unnecessary data reception. By introducing the concept of compensation ratio, we derive the optimal power allocation policy of the formulated throughput optimization problem for the two-receiver scenario. Numerical results are also presented to show the throughput improvement over the worst-case SNR-dominating (WSD) rate policy, the best-case SNR-dominating (BSD) rate policy, and the constant-rate policy.

We develop energy-efficiency data gathering schemes for wireless sensor networks (WSN), where the channel is characterized by the slow Rayleigh fading. We consider the homogeneous WSN which consists of a large number of sensor nodes equipped with the identical hardware. In the WSN, the sensor nodes are distributed randomly in a specific area. A mobile base station periodically stops by the area to collect the data from the senor nodes. Energy efficiency, which is the performance metric we adopt for our scheme design, is defined as the energy consumption by transmitting a bit sensing information. We develop analytical models to study the proposed data gathering schemes. First, we demonstrate that clustering and data-fusion technology can help improve the energy efficiency compared to the naive data gathering scheme where the sensor nodes send sensing data directly to the mobile base station. Second, we show that making full use of the channel state information (CSI) can further reduce the energy consumption for data gathering. Finally, we found that there is a tradeoff between the energy consumption and the data gathering delay.

We propose several relay selection strategies for distributed space-time cooperative systems, in which the Alamouti scheme is used. Our strategies select the best two nodes among the source and all relays to assist the packet transmissions from the source to the destination. In the amplify-and-forward mode, we develop an efficient algorithm to select the optimal cooperative nodes which can maximize the instantaneous signal-to-noise ratio at the destination. Under the decode-and-forward mode, our relay selection strategies depend on the successful decoding results at the relays. We design our schemes by considering both cases with and without CRC. In addition, we integrate the request- to-send/clear-to-send mechanism into our proposed system and analyze the system throughputs. The simulation results show that our proposed strategies can significantly improve the system throughput and achieve the diversity gain as compared to the conventional existing schemes.

We propose a quality-of-service (QoS) driven power and rate adaptation scheme for multichannel communications systems over wireless links. In particular, we use multichannel communications to model either diversity or multiplexing systems, which play a fundamental role in physical-layer evolutions of mobile wireless networks. By integrating information theory with the concept of effective capacity, our proposed scheme aims at maximizing the multichannel-systems throughput subject to a given delay-QoS constraint. Under the framework of convex optimization, we develop the optimal adaptation algorithms. Our analyses show that when the QoS constraint becomes loose, the optimal power-control policy converges to the well-known water-filling scheme, where the Shannon (or ergodic) capacity can be achieved. On the other hand, when the QoS constraint gets stringent, the optimal policy converges to the scheme operating at a constant-rate (i.e., the zero-outage capacity), which, by using only a limited number of subchannels, approaches to the Shannon capacity. This observation implies that the optimal effective capacity function decreases from the ergodic capacity to the zero-outage capacity as the QoS constraint becomes more stringent. Furthermore, unlike the single-channel communications, which have to trade off the throughput for QoS provisioning, the multichannel communications can achieve both high throughput and stringent QoS at the same time

We propose a cross-layer-model based adaptive resource-allocation scheme for the diverse quality-of-service (QoS) guarantees over downlink mobile wireless networks. Our proposed scheme dynamically assigns power-levels and time-slots for heterogeneous real-time mobile users to satisfy the variation of statistical delay-bound QoS requirements. To achieve this goal, we apply effective capacity approach to derive the admission-control and power/time-slot allocation algorithms, guaranteeing the statistical delay-bound for heterogeneous mobile users. When designing such an algorithm, we study the impact of physical-layer issues such as adaptive power-control on the QoS provisioning performance. Through numerical and simulation results, we observe that the adaptive power adaptation has a significant impact on statistical QoS-guarantees. In addition, the analyses indicate that our proposed resource-allocation algorithms are shown to be able to efficiently support the diverse QoS requirements for various real-time mobile users over different wireless channels.

To save the precious wireless resources in wireless networks, we investigate cross-layer optimization schemes to reduce wireless resource consumption (WRC) for reliable mobile multicast. We characterize WRC by signal-to-noise ratio (SNR) per information bit and by power-delay product for scenarios without and with delay quality of service (QoS) constraints, respectively. Aiming at decreasing WRC, we develop a cross-layer parameter-selection (CLPS) algorithm, which dynamically adjusts the parameters of the physical layer and the medium access control (MAC) layer according to the variations of multicast group size, transport-layer error-control parameters, and delay QoS constraints. In particular, we first derive the optimal physical-layer parameters, including constellation size and transmit SNR, to minimize WRC for Threshold-0 multicast policy, where the sender transmits data regardless of channel qualities. Then, we develop a suboptimal approach for the selection of MAC-layer multicast threshold to achieve low power-delay product. In addition, we study the effects of pure automatic repeat request (ARQ) and hybrid ARQ-forward error correction (FEC) based error-control schemes on the CLPS algorithm. Also conducted is a set of numerical analyses to show the performance gain achieved through the cross-layer design and the impacts of various system parameters on the WRC optimization.

To save the precious wireless resources in wireless networks, we investigate cross-layer optimization schemes to reduce wireless resource consumption (WRC) for reliable mobile multicast. We characterize WRC by signal-to-noise ratio (SNR) per information bit and by power-delay product for scenarios without and with delay quality of service (QoS) constraints, respectively. Aiming at decreasing WRC, we develop a crosslayer parameter-selection (CLPS) algorithm, which dynamically adjusts the parameters of the physical layer and the medium access control (MAC) layer according to the variations of multicast group size, transport-layer error-control parameters, and delay QoS constraints. In particular, we first derive the optimal physical-layer parameters, including constellation size and transmit SNR, to minimize WRC for Threshold-0 multicast policy, where the sender transmits data regardless of channel qualities. Then, we develop a suboptimal approach for the selection of MAC-layer multicast threshold to achieve low powerdelay product. In addition, we study the effects of pure automatic repeat request (ARQ) and hybrid ARQ - forward error correction (FEC) based error-control schemes on the CLPS algorithm. Also conducted is a set of numerical analyses to show the performance gain achieved through the cross-layer design and the impacts of various system parameters on the WRC optimization.

Prolonging the network lifetime is one of the most important designing objectives in wireless sensor networks (WSN). We consider a heterogeneous cluster-based WSN, which consists of two types of nodes: powerful cluster-heads and basic sensor nodes. All the nodes are randomly deployed in a specific area. To better balance the energy dissipation, we use a simple mixed communication modes where the sensor nodes can communicate with cluster-heads in either single-hop or multi-hop mode. Given the initial energy of the basic sensor nodes, we derive the optimal communication range and identify the optimal mixed communication mode to maximize the WSN's lifetime through optimizations. Moreover, we also extend our model from 2-D space to 3-D space.

This paper introduces an approach to generate generalized pairwise complementary (GPC) codes, which offer a uniform interference free windows (IFWs) across the entire code set. The GPC codes work in pairs and can fit extremely power efficient quadrature carrier modems. The characteristic features of the GPC codes include: the set size is 2K, the processing gain is 4NK, and the IFW's width is 8N identically for all codes in a set, where K is the times to perform Walsh-Hadamard expansions and N is element code length of seed complementary codes. Therefore, by using different N, the IFW width of a GPC code set can be adjusted with its set size unchanged. Each GPC code set consists of two code groups, with each having K codes, and they have sparsely and uniformly distributed autocorrelation side lobes and cross-correlation levels outside the IFWs.

We propose the scheme to integrate transmit selection diversity/maximal-ratio combining (TSD/MRC) with multicarrier (MC) direct-sequence code-division multiple access (DS-CDMA) for various wireless networks. Applying this TSD/MRC-based scheme, the transmitter jointly selects the optimal subcarrier-and-antenna pair to significantly decrease the peak-to-average power ratio (PAPR), which is one of the main problems inherently associated with MC DS-CDMA communications. Over the frequency-selective Nakagami-m fading channels, we develop the unified analytical framework to analyze the symbol-error rate (SER) of the scheme implemented in different types of wireless networks, while dealing with the perfect and imperfect channel state information (CSI) feedbacks, respectively. The imperfect feedbacks we focus on include delayed feedbacks and erroneous feedbacks. Taking the imperfectness of the feedback into account, the resultant SER is compared with that of both conventional selection diversity (SD)/MRC-based and space-time block coding (STBC)/MRC-based schemes. Our analyses show that in a wide variation of the feedback imperfectness, our proposed TSD/MRC-based scheme has significant advantages over the other two schemes for both downlink cellular networks and ad hoc wireless networks. However, our analytical findings indicate that TSD/MRC-based scheme cannot always outperform SD/MRC-based and STBC/MRC-based schemes even when the perfect CSI feedbacks are available.

We investigate the Alamouti scheme with joint antenna selection and power allocation over flat-fading Rayleigh channels. Based on the channel state information (CSI) feedbacks, the transmitter selects the optimal two antennas out of all possible antennas to transmit data using space-time block coding (STBC). Then, the transmitter adaptively allocates transmit power among the selected antennas to minimize the symbol-error rate (SER). We derive the SER as either the closed-form expression or the single-fold finite integral when assuming perfect and delayed CSI feedbacks, respectively. Our results show that when the CSI feedback is perfect, the optimal power allocation is to assign all power to the single optimal antenna, such that the selection-combining (SC)-STBC reduces to the simpler selection-combining (SC) scheme. On the other hand, when taking the CSI feedback delay into account, the SC-STBC scheme with dynamic power allocation ensures the better SER performance than the conventional SC scheme and SC-STBC scheme with equal power (EP) allocation.

Viruses spreading over the Internet can cause significant damage and the loss of network security. On the other hand, the anti-virus process also plays an important part affecting the dynamics of the virus spreading. The spreading dynamics of most viruses depend on the underlying network topology. While much research attention has been paid in developing the anti-virus software/tools, the dynamics and propagating model of the virus and anti-virus spreading in the topology-aware networks is neither well understood, nor thoroughly studied. To remedy this deficiency, we model and analyze the spreading characteristics of viruses as coexisting with the anti-virus spreading process in the two-layer small-world topology. Applying the fluid analysis, we derive the analytical solutions to the two-layer model. The simulations experiments confirm the validity of our fluid analyses in characterizing both virus and anti-virus spreading dynamics.

We propose an adaptive hybrid ARQ-FEC erasure-correcting scheme for QoS-driven mobile multicast services over wireless networks. The main features of our proposed scheme include: the low-complexity and dynamic adaptation to the variations of packet-loss levels and QoS-requirements. The low complexity is achieved by using the graph-code with linear time-complexity. To support diverse QoS-requirements and improve the error-control efficiency, we develop the two-dimensional adaptive error-control scheme, which dynamically adjusts not only the error-control redundancy, but also the code-mapping structure. By deriving and identifying the closed-form non-linear expressions for the optimal check-node degree and the required error-control redundancy in each adaptation step as the functions of the packet-loss level, we propose the non-uniformed two-dimensional adaptive coding scheme. Using this scheme, we develop an efficient hybrid ARQ-FEC protocol for mobile multicast services with diverse QoS requirements. Also conducted is a set of numerical and simulation results which analyze and compare our proposed adaptive scheme with those using the non-adaptive graph codes, Reed-Solomon erasure codes, and pure ARQ-based approach. The simulation results show that our proposed scheme can efficiently support QoS-driven mobile multicast services and achieve well-balanced error-control redundancy while imposing low error-control complexity and overhead for mobile multicast services over wireless networks.

We develop the framework to analyze the symbol-error probability (SEP) for the scheme integrating transmit antenna selection (TAS) with maximal-ratio combining (MRC) used in wireless networks. Applying this scheme, the transmitter always selects an optimal antenna out of all possible antennas based on channel state information (CSI) feedback. Over a flat-fading Rayleigh channel, we develop the closed-form SEP expressions for a set of commonly used constellations when assuming perfect and delayed CSI feedback, respectively. We also derive the Chernoff-bounds of the SEP's for both perfect and delayed feedback. Our analyses show that while the antenna diversity improves the system performance, the feedback delay can significantly impact the SEP of TAS/MRC schemes.

We propose a link-adaptation (LA)-based system infrastructure and develop the corresponding dynamic channel allocation (DCA) algorithm for downlink data transmissions over MIMO-OFDM wireless networks. By integrating a set of LA techniques, our system can dynamically select transmit antennas, modulation levels, and transmission power for different mobile users. Jointing with the proposed system infrastructure, we develop the DCA algorithm to maximize the total system throughput. In designing the DCA algorithm, we first focus on CDMA-based MIMO-OFDM systems with any given spreading gain G. Deriving the optimal G to achieve the maximum throughput, we prove that the optimal spreading gain is G = 1. This implies that the system without using spread spectrum (SS) is optimal in terms of maximizing the throughput. Extensive simulations show that the system throughput increases significantly with the numbers of transmit antennas, receive antennas, and mobile users. Moreover, our DCA algorithm can significantly improve the system throughput performance as compared with the conventional fixed channel allocations (FCA).

We propose a PHY-MAC cross-layer based dynamic-resource allocation (DRA) scheme with diverse QoS guarantees for heterogeneous mobile users over MIMO-OFDM wireless network downlinks. Our scheme differentiates real-time users (e.g., audio/video) from non-real-time users (e.g., data). At the PHY layer, the DRA adaptively allocates transmit antennas and modulation levels for real- and non-real-time users to increase the spectral efficiency while guaranteeing the distinct BER-QoS requirements. Combining with the proposed PHY-layer infrastructure, the DRA at the MAC layer treats real- and non-real-time users differently: (1) it dynamically assigns time-slots for real-time users in a TDM-mode to guarantee the bounded delays; (2) it adaptively allocates subcarriers for non-real-time users in an FDM-mode to maximize the system throughput while retaining fairness among users. We analytically derive the admission-control and time-slot allocation conditions to guarantee the statistical delay-bound for real-time users using the effective bandwidth theory. Also, extensive simulations are conducted to evaluate the performance of our proposed scheme.

In order to support the quality of service (QoS) requirements for real-time traffic over broadband wireless networks, advanced techniques such as space-time diversity (STD) and multicarrier direct-sequence code division multiple access (MC-DS-CDMA) are implemented at the physical layer. However, the employment of such techniques evidently affects the QoS provisioning algorithms at the medium access control (MAC) layer. In this paper, we propose a space-time infrastructure and develop a set of cross-layer real-time QoS-provisioning algorithms for admission control, scheduling, and subchannel-allocations. We analytically map the parameters characterizing the space-time diversity onto the admission-control region guaranteeing the real-time QoS. Our analytical analyses show that the proposed algorithms can effectively support real-time QoS provisioning. Also presented are numerical solutions and simulation results showing that the space-time diversity can significantly improve the QoS provisioning for real-time services over wireless networks.

In order to support the diverse quality of service (QoS) requirements for differentiated data applications in broadband wireless networks, advanced techniques such as space-time coding (STC) and orthogonal frequency division multiplexing (OFDM) are implemented at the physical layer. However, the employment of such techniques evidently affects the subchannel-allocation algorithms at the medium access control (MAC) layer. In this paper, we propose the QoS-driven cross-layer subchannel-allocation algorithms for data transmissions over asynchronous uplink space-time OFDM-CDMA systems. We mainly focus on QoS requirements of maximizing the best-effort throughput and proportional bandwidth fairness, while minimizing the upper-bound of scheduling delay. Our extensive simulations show that the proposed infrastructure and algorithms can achieve high bandwidth fairness and system throughput while reducing scheduling delay over wireless networks.

Both space-time (ST) processing and orthogonal frequency division multiplexing (OFDM) are becoming increasingly important techniques used in physical layer to support the quality of service (QoS) for different applications in wireless networks. However, the employment of such techniques affects the scheme designs at higher layers such as channel allocation algorithms at media access control (MAC) layer. Therefore, a crossing-layer development is required. In this paper, we propose the subchannel allocation algorithms based on space-time OFDM-CDMA systems, and develop the throughput-performance analysis frameworks to evaluate the proposed algorithms. Our analyses show that the proposed subchannel allocation scheme can significantly increase the system throughput over wireless networks, while achieving a much lower implementation and computation complexity as compared to the smart-antenna-based systems. Also presented are the simulation results that verify the analytical findings and observations.

Feedback signaling plays a key role in flow control because the traffic source relies on the signaling information to make correct and timely flow-control decisions. However, it is difficult to design an efficient signaling algorithm since a signaling message can tolerate neither error nor latency. Multicast flow-control signaling imposes two additional challenges: scalability and feedback synchronization. Previous research on multicast signaling has mainly focused on the development of algorithms without analyzing their delay performance. To remedy this deficiency, we have previously developed a binary-tree model and an independent-marking statistical model for multicast-signaling delay analysis. This paper considers a general scenario where the congestion markings at different links are dependent - a more accurate but complex case. Specifically, we develop a Markov-chain model defined by the link-marking state on each path in the multicast tree. The Markov chain can not only capture link-marking dependencies, but also yield a tractable analytical model. We also develop a Markov-chain dependency-degree model to evaluate all possible Markov-chain dependency degrees without any prior knowledge of them. Using the above two models, we derive the general probability distributions of each path becoming the multicast-tree bottleneck. Also derived are the first and second moments of multicast signaling delays. The proposed Markov chain is also shown to asymptotically reach an equilibrium, and its limiting distribution converges to the marginal link-marking probabilities when the Markov chain is irreducible. Applying the two models, we analyze and contrast the delay scalability of two representative multicast signaling protocols: Soft-Synchronization Protocol (SSP) and Hop-By-Hop (HBH) algorithms.

Feedback signaling plays a key role in flow control because the traffic source relies on the signaling information to make correct and timely flow-control decisions. Design of an efficient signaling algorithm is a challenging task since the signaling messages can tolerate neither error nor latency. Multicast flow-control signaling imposes two additional challenges: scalability and feedback synchronization. Previous research on multicast feedback-synchronization signaling has mainly focused on algorithm design and implementation. However, the delay properties of these algorithms are, despite their vital importance, neither well understood nor thoroughly studied. We develop both deterministic and statistical binary-tree models to study the delay performance of the multicast signaling algorithms. The deterministic model is used to derive the expressions of each path's feedback roundtrip time in a multicast tree, while the statistical model is employed to derive the general probability distributions of each path becoming the multicast-tree bottleneck. Using these models, we analyze and contrast the signaling delay scalability of two representative multicast signaling protocols - the soft-synchronization protocol (SSP) and the hop-by-hop (HBH) scheme - by deriving the first and second moments of multicast signaling delays. Also derived is the optimal flow-control update interval for SSP to minimize the multicast signaling delay.

We propose a flow-control scheme for multicast ABR services in ATM networks. At the heart of the proposed scheme is an optimal second-order rate control algorithm, called the /spl alpha/-control, designed to deal with the variation in RM-cell round-trip time (RTT) resulting from dynamic drift of the bottleneck in a multicast tree. Applying two-dimensional rate control, the proposed scheme makes the rate process converge to the available bandwidth of the connection's most congested link sensed by the traffic source. It also confines the buffer occupancy to a target regime bounded by a finite buffer capacity as the system enters the equilibrium state. It works well irrespective of the topology of the multicast tree. Using the fluid analysis, we model the proposed scheme and analyze the system dynamics for multicast ABR traffic. We study the convergence properties and derive the optimal-control conditions for the /spl alpha/-control. The analytical results show that the scheme is stable and efficient in the sense that both the source rate and bottleneck queue length rapidly converge to a small neighborhood of the designated operating point. We present simulation results which verify the analytical observations. The simulation experiments also demonstrate the superiority of the proposed scheme to the other schemes in dealing with RM-cell RTT and link-bandwidth variations, achieving fairness in both buffer and bandwidth occupancies, and enhancing average throughput.

We propose an efficient flow and error control scheme for high-throughput transport protocols by using a second-order rate control, called the /spl alpha/-control, and a new sliding-window scheme for error control. The /spl alpha/-control minimizes the packet retransmissions by adjusting the rate-gain parameter to the variations in the number and round-trip times (RTTs) of cross-traffic flows that share the bottleneck. Using selective retransmission, the sliding-window scheme guarantees lossless transmission. By applying the /spl alpha/-control, the proposed scheme can drive the flow-controlled system to a retransmission-less equilibrium state. Using the fluid analysis, we establish the flow-control system model, obtain the greatest lower bound for the target buffer occupancy, and derive closed-form expressions for packet losses, loss rate, and link-transmission efficiency. We prove that the /spl alpha/-control is feasible and optimal linear control in terms of efficiency and fairness. Also presented are the extensive simulation results that confirm the analytical results, and demonstrate the superiority of the proposed scheme to others in dealing with the variations of cross-traffic flows sharing the same bottleneck and their RTTs, controlling packet losses/retransmissions, and achieving buffer-usage fairness as well as high throughput.

Feedback signaling plays a crucial role in flow control because the traffic source relies on the signaling information to make correct and timely flow-control decisions. Multicast flow-control signaling imposes two additional challenges: scalability and feedback synchronization. We developed a binary-tree deterministic model (Zhang and Shin 1999) and an independent-marking statistical model (Zhang and Shin 2000) to study the delay performance of various multicast feedback-synchronization signaling algorithms. In this paper, we consider the general case in which the congestion markings at different links are dependent. We develop a Markov chain model defined by the link-marking state on each path in a multicast tree. The Markov chain can not only characterize link-marking dependencies, but also yield a tractable analytical model. We also develop a Markov-chain dependency-degree model which can he used to quantify/evaluate all possible Markov-chain dependency degrees without knowing a priori the dependency degree information. Using the Markov-chain and dependency-degree models, we derive the general expressions for the probability distribution of each path bring the multicast-tree bottleneck. Also derived are the closed-form expressions for the first and second moments of multicast signaling delays. The proposed Markov chain is also shown to asymptotically reach an equilibrium, and its limiting state distributions converge to the link-marking marginal probabilities when the Markov chain is irreducible. By applying these two models, we analyze and contrast the feedback-delay scalability of two representative multicast signaling protocols: soft-synchronization protocol and hop-by-hop (HBH) signaling algorithms.

We propose a flow control scheme for multicast ABR services in ATM networks. At the heart of the proposed scheme is an optimal second-order rate control algorithm, called the /spl alpha/-control, designed to deal with the variation in RM-cell round-trip time (RTT) resulting from dynamic "drift" of the bottleneck in a multicast tree. Applying two-dimensional rate control, the proposed scheme makes the rate process converge to the available bandwidth of the connection's most congested link. It also confines the buffer occupancy to a target regime bounded by a finite buffer capacity. It works well irrespective of the topology of the multicast tree. Using the fluid approximation, we model the proposed scheme and analyze the system dynamics for multicast ABR traffic. We study the convergence properties and derive the optimal control conditions for the /spl alpha/-control. The analytical results show that the scheme is stable and efficient in the sense that both the source rate and bottleneck queue length rapidly converge to a small neighborhood of the designated operating point. We present simulation results which verify the analytical observations. The simulation results also demonstrate the effectiveness of the proposed scheme in dealing with RM-cell RTT and link-bandwidth variations, and in achieving fairness in both buffer and bandwidth occupancies.

We propose a flow-control scheme that combines the merits of credit- and rate-based flow-control schemes by applying direct control over both bandwidth and buffer resources. The goal of the proposed scheme is to design an optimal rate-control policy for a given finite buffer capacity that maximizes the average throughput and bounds the end-to-end delay. By applying higher-order rate control, the proposed scheme not only makes the rate process converge to the neighborhood of the link bandwidth, but also confines the queue-length fluctuation to a regime bounded by the buffer capacity (thus guaranteeing lossless transmission). Using the fluid approximation method, we model the proposed flow-control scheme and study the system dynamic behavior for ABR (available bit rate) service under the most stressful traffic condition. We derive the expressions for queue build-ups and average throughput in both transient and equilibrium states. The analytical results have shown the proposed scheme to be stable and efficient in that the source rate and bottleneck queue length rapidly converge to the designated operating region. Also presented, are examples showing that the proposed scheme outperforms the other existing schemes.