The covert communication scenario with multiple users involved needs to take into account multiple conflicting optimization goals, such as maximizing the number of served users, minimizing service energy consumption, and balancing resource allocation. And the UAV-assisted multi-user communication is a typical knapsack traveling salesman problem, which is difficult to solve because of its large solution space and NP-hard nature. Therefore, to address this, a novel UAV-assisted multi-user downlink covert communication scenario is considered. To find an optimal balance of the maximum transmission rate and the minimum energy consumption, a low-complexity solution is proposed. First, a closed-form solution for the hovering position and transmission power of the UAV is deduced. Then, the user selection and trajectory planning results from the heuristic algorithm are used to initialize the deep reinforcement learning (DRL) agent, providing a superior starting point for the subsequent policy search. This strategy combines the fast convergence of heuristics with the dynamic adaptability of DRL, resulting in a final design that is more efficient and lightweight than conventional, pure DRL-based methods. The simulation results demonstrate that the proposed scheme improves energy efficiency by at least 40% compared with the benchmark scheme.

Large-scale reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for signal strength enhancement and coverage extension in 6G networks. However, as the antenna scale and the bandwidth increase, large-scale RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face new challenges due to the near-field range expansion and the beam split effect, complicating the acquisition of channel state information (CSI). To tackle these challenges, in this paper, we present a wideband beam training scheme by utilizing the beam split effect to bypass the CSI acquisition. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields. The coverage of codewords in the proposed codebook is expanded benefiting from the beam split effect, leading to fewer codewords compared to conventional narrow-band codebooks. Utilizing such a codebook, a three-stage beam training mechanism is performed to obtain the optimal codeword with low time overhead, thereby facilitating the beamforming. Simulation results demonstrate that the proposed scheme outperforms existing codebook-based beam training schemes in terms of training overhead and sum rate in the hybrid near-far field.

Covert communication is a significant scenario towards 6G, where the transmission between transmitter and receiver should be covert without being detected. In covert communication, the transmitter called Alice transmits signal to the receiver called Bob, and a detecter called Willie continuously observes its received signal. Alice should control the transmit power under a certain value to mislead Willie judging the received signal containing only White Gaussian Noise. Several works have been carried out focusing on the covert communication performance from the viewpoint of timeliness, throughput, etc. However, the covert relay communication, especially the UAV based relay in transparent forwarding manner (TFM) is scarcely considered. In this paper, we study a novel scenario for strict and deteriorative covert communication with the UAV based relay: 1) The transmission of both Alice and the UAV relay cannot be detected by Willie. 2) The UAV must be on the sight of Willie. The three-dimensional placement of the UAV, the transmit power of Alice, and the amplifier gain of the UAV with TFM are jointly optimized by geometric programming, where several Lemmas are also derived. Finally, the performance of the proposed algorithm is verified by extensive simulations.

With the development of millimeter-wave (mmWave) communication systems, large-scale reconfigurable intelligent surfaces (RISs) have gained considerable attention as a promising technology for signal strength enhancement and coverage extension. However, as the antenna scale and bandwidth increase, RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face challenges due to the near-field range expansion and the beam split effect over the high-frequency band, complicating the acquisition of channel state information (CSI). To tackle these challenges, we present a codebook-based three-stage beam training scheme by using the beam split effect to bypass CSI estimation. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields with fewer codewords compared to conventional narrow-band codebooks. Using such a codebook, a three-stage beam training mechanism is adopted to obtain the optimal codeword with low time overhead, thereby facilitating subsequent beamforming. Simulation results demonstrate that the proposed scheme outperforms existing near- and far-field codebook-based schemes in terms of the beam training resolution and sum rate in the hybrid near–far field.

The indoor location awareness has drawn increasing attention for smart speakers as they become essential to provide function-location services. Existing indoor localization solutions either require add-on equipment or could only achieve room-level accuracy, which could not provide a function-location service for smart speakers. In this work, we propose a location identification system utilizing 3-D acoustic images, which are derived from the smart speaker by emitting a beep signal and sensing echoes created by objects in the surrounding environment with its microphone array, as the proof to identify some pre-defined indoor locations. Given the recorded acoustic samplings captured by the microphone array, our image construction component constructs a virtual imaging hemisphere and steers the array towards each grid of the hemisphere to generate a 3-D acoustic image of the surrounding environment. Moreover, we design a transfer-learning based model to derive effective features from the constructed images, and propose a data augmentation scheme for generating synthesized training images. To achieve a more accurate location identification, we further design a distance estimation scheme to identify the distances between the smart speaker and some major surrounding objects by utilizing the constructed 3-D acoustic image, and then adopt such distance information for location identification. Our experimental results show that our proposed system is accurate and robust for location identification under various real world scenarios.

Ultra-dense unmanned aerial vehicle (UAV) plays an important role in the field of communications due to its flexibility and low-cost feature. Ultra-dense unnamed aerial vehicle base station (UAV-BS) can improve communication quality by providing temporary and cost-effective wireless communication services for hotspots. In this paper, a multiple UAV-BSs assisted downlink network is investigated to maximize the system throughput. It is still a challenging problem to jointly optimize the power allocation and the 3D deployment of multiple UAV-BSs. Therefore, in this paper, for effective interference management, the power allocation problem is first formulated as a non-cooperative game with a pricing mechanism to imitate the interactions among users served by UAV-BSs. Then, based on the combination of deep reinforcement learning (DRL) and the game theory, the power allocation and the 3D deployment of UAV-BSs are transformed into a Markov decision problem. Finally, a novel price-based proximal policy optimization (3PO) algorithm is proposed to explore the optimal policy to maximize the system throughput. Simulation results reveal that the proposed 3PO algorithm can significantly improve system throughput and energy efficiency compared to other baselines by jointly optimizing power allocation and 3D deployment for UAV-BSs.

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client selection policy considering client heterogeneity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

Simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) can provide expanded coverage compared with the conventional reflection-only RIS. This paper exploits the energy efficient potential of STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) system. Specifically, we mainly focus on energy-efficient resource allocation with MIMO technology in the STAR-RIS assisted NOMA network. To maximize the system energy efficiency, we propose an algorithm to optimize the transmit beamforming and the phases of the low-cost passive elements on the STAR-RIS alternatively until the convergence. Specifically, we first decompose the formulated energy efficiency problem into beamforming and phase shift optimization problems. To efficiently address the non-convex beamforming optimization problem, we exploit signal alignment and zero-forcing precoding methods in each user pair to decompose MIMO-NOMA channels into single-antenna NOMA channels. Then, the Dinkelbach approach and dual decomposition are utilized to optimize the beamforming vectors. In order to solve non-convex phase shift optimization problem, we propose a successive convex approximation (SCA) based method to efficiently obtain the optimized phase shift of STAR-RIS. Simulation results demonstrate that the proposed algorithm with NOMA technology can yield superior energy efficiency performance over the orthogonal multiple access (OMA) scheme and the random phase shift scheme.

Optical spatial modulation (OSM) and orthogonal frequency division multiplexing (OFDM) are two promising techniques for bandlimited intensity modulation/direct detection (IM/DD) optical wireless communication (OWC) systems. In this paper, we for the first time propose a novel enhanced OFDM-based OSM scheme for spectral efficiency improvement of ban-dlimited IM/DD OWC systems. The proposed enhanced OFDM-based OSM scheme can be considered as the combination of time-domain OSM (TD-OSM) and non-Hermitian symmetry OFDM (NHS-OFDM). In an OWC system adopting enhanced OFDM-based OSM, a pair of light-emitting diode (LED) transmitters are selected from the LED array, which are used to separately transmit the real and imaginary parts of a complex-valued NHS-OFDM signal. A modified maximum-likelihood (ML) detector is further developed to efficiently estimate the indexes of the LED pair and the real and imaginary parts of the transmitted complex-valued NHS-OFDM signal. We show that the proposed enhanced OFDM-based OSM scheme can achieve substantially improved spectral efficiency with moderate inter-channel interference and low transceiver complexity. Simulation results clearly verify the superiority of the proposed enhanced OFDM-based OSM scheme over the existing OFDM-based OSM schemes.

Network function virtualization (NFV) is a promising paradigm that network functions can be deployed on commodity servers instead of dedicated servers to enhance the resource utilization and reduce the management difficulty. Based on the NFV technology, a complex network service can be composed of a series of ordered virtual network functions, known as service function chain (SFC). In this context, how to efficiently place SFCs in acceptable running time to improve resource utilization and service quality while meeting the constraints of the physical network is a critical issue for infrastructure providers. In this paper, we propose a deep reinforcement learning-based approach called DRL-SFCP for adaptive SFC placement. DRL-SFCP maximizes the long-term average revenue by combining both the graph convolution network which extracts the features of the physical network and sequence-to-sequence model which captures the ordered information of the SFC request to generate placement strategies. It learns to make SFC placement decisions via observations of the corresponding performance of past decisions rather than a hypothetical environment. Extensive experimental results show that our DRL-SFCP can achieve 11.6% and 9.6% improvement in terms of the acceptance ratio and the long-term average revenue, compared with existing benchmarks.

The influence maximization (IM) problem, which aims to find$k$most influential individuals from a social network to maximize the influence spread, has been extensively studied. Existing works all rely on the assumption that influenced individuals will definitely try to propagate the information to their neighbors through social trust. However, in real-world this assumption can be over-simplistic since trust-levels among different individuals usually exhibit high diversity. As a result, an influenced individual may choose not to further propagate the information to his neighbors. Motivated by the observation, in this paper we propose a new accepted influence maximization (AIM) problem where the influenced individuals are further divided into two subgroups, i.e., accepted and active, where only active individuals will continue to propagate the information. We prove this problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with ($1-1/e-\epsilon$) approximation guarantee. Considering the low computational efficiency of the greedy algorithm, we further propose a scalable path-based algorithm ALDAG. We conduct experiments on real datasets and the results demonstrate the effectiveness and efficiency of our method.

In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical environment. Specifically, the physical environment is divided into multiple grids, where one spot for UAV hovering as well as the wireless charging of UAV is located at the center of each grid. Each grid has a spot for the UAV to hover, and moreover, there is a wireless charger at the center of each grid, which can provide wireless charging to UAV when it is hovering in the grid. When the UAV lacks energy, it can be charged by the wireless charger at the spot. By taking into account the collected data amount as well as the energy consumption, we formulate the problem of data collection with UAV as a Markov decision problem, and exploit Q-learning to find the optimal policy. In particular, we design the reward function considering the energy efficiency of UAV flight and data collection, based on which Q-table is updated for guiding the route of UAV. Through extensive simulation results, we verify that our proposed reward function can achieve a better performance in terms of the average throughput, delay of data collection, as well as the energy efficiency of UAV, in comparison with the conventional capacity-based reward function.

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users' channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.

Terrestrial-satellite networks (TSNs) are envisioned to play a significant role in the sixth-generation (6G) wireless networks. In such networks, hot air balloons are useful as they can relay the signals between satellites and ground stations. Most existing works assume that the hot air balloons are deployed at the same height with the same minimum elevation angle to the satellites, which may not be practical due to possible route conflict with airplanes and other flight equipment. In this paper, we consider a TSN containing hot air balloons at different heights and with different minimum elevation angles, which creates the challenge of non-uniform available serving time for the communication between the hot air balloons and the satellites. Jointly considering the caching, computing, and communication (3C) resource management for both the ground-balloon-satellite links and inter-satellite laser links, our objective is to maximize the network energy efficiency. Firstly, by proposing a tapped water-filling algorithm, we schedule the traffic to relay among satellites according to the available serving time of satellites. Then, we generate a series of configuration matrices, based on which we formulate the relation between relay time and the power consumption involved in the relay among satellites. Finally, the collaborative resource allocation problem for TSN is modeled and solved by geometric programming with Taylor series approximation. Simulation results demonstrate the effectiveness of our proposed scheme.

In this article, we propose an energy-efficiency-aware integrated architecture of cooperative computing (CC) to support the demands of computing amount in the blockchain-based Internet of Things (IoT). Specifically, we assume that multiple computing servers are placed at each data access point (DAP). The computing servers across multiple DAPs can be virtualized to constitute a CC pool to flexibly allocate the computing resource. When the amount of received data from a DAP is accumulated to a certain length of one data block, blockchain computing will be implemented to generate a correct Nonce value meeting the threshold of hash value. After the correct Nonce has been generated, the data block will be transmitted and stored in cloud caches, where the hash value is written into blockchain to guarantee the security of data block. We maximize system energy efficiency defined by the overall power consumption per unit of throughput transmitted from DAPs to cloud caches. We formulate the system optimization model by considering the constraints of data delay to avoid data overflow in the system. In order to solve the optimization model for maximizing system energy efficiency, we employ a geometric programming method to obtain the optimal power and resource allocation in blockchain-based IoT. By extensive simulations, we verify the effectiveness of our proposed energy-efficiency-aware optimization mechanism in the blockchain-based IoT.

In this paper, we focus on the energy efficiency aware architecture of caching the necessary production messages and the transaction process to support a readable and tamper proof internet of things (IoT). To achieve this, blockchain can provide a reliable distributed storage of the messages, because any changes of the cached messages will break the structure of the blockchain. Specifically, we assume that the access points belonging to different telecom operators collect the messages in the IoT network, wherein multiple servers used for either caching or computing are placed at each access point. We define that the caching servers can be divided into the data loading caches for caching the received wireless IoT data and the data transmission caches for transmitting the IoT data into the blockchain based cloud caching servers. The blocks generated in the data loading caches at each access point will be written into the blockchain based on both the proof-of-work and the capacity of the data loading caches at each access point. Then, we formulate the optimization problem maximizing the system energy efficiency by optimizing the allocation of cache, computation and communication resources by a geometric programming model. By the CVX tool in matlab software, the geometric programming model can be solved effectively. We study the impact of different parameters involved in the blockchain on the system performance, and verify the effectiveness of our proposed energy efficiency aware optimization mechanism in the blockchain based IoT.

In the two-layer space communication network (TLSCN), communications can be performed to obtain higher throughput and lower latency by introducing various types of spatial nodes. However, the mobility of spatial nodes and the connectivity of spatial links result in the time-varying network topology, and intermittent link connection. This further leads to the lack of continuous contact, unreliable transmission, and high transmission cost. In this article, contact plan design (CPD) is employed to address the above problems by increasing the contact reliability while decreasing the contact cost. First, a directional space-time graph (DSTG) is constructed by considering the motion trajectory of spatial nodes and the time evolution nature of TLSCN. Afterwards, based on DSTG, we propose three CPD methods with greedy algorithm by considering the limited computing power of nodes. The three methods can optimize the objective functions of the total contact reliability, total contact cost, and invalid probability cost, respectively. The simulation results show that the proposed CPD methods can effectively improve the contact reliability, reduce the contact cost of TLSCN, and perform well with the increase of network density.

Nash noncooperative power game is an effective method to implement interference cooperation in downlink multiuser multiple-input multiple-output (MU-MIMO). Power equilibrium point of Nash noncooperative power game can achieve a satisfactory tradeoff between self-benefits of Internet of Things (IoT) users and interference between IoT users which largely enhance the edge IoT user throughput. However, either power strategy space, i.e., the enabled range of power allocation for IoT users, or overall BS transmit power in the existing Nash noncooperative power games is generally static. This limits the performance of systems, especially in IoT systems, etc., in 5G. As an effort to address these problems, we design a novel framework of Nash noncooperative game with iterative convergence for downlink MU-MIMO. We first decompose the MU-MIMO into multiple virtual single-antenna transmit-receive pairs with a stream analytical model. Afterwards, based on streams, we propose a noncooperative water-filling power game with pricing (WFPGP) where the power strategy space of each stream can be dynamically determined byiterative water-filling. We derive the sufficient condition for the existence and uniqueness of WFPGP game, in which the verification of the sufficient condition can be executed in a distributed manner. By simulations, we verify the performance of WFPGP compared to other Nash noncooperative games.

Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.

Due to the reconfiguration overhead of optical fabrics, batch scheduling method is generally used to schedule an optical packet switch, with a necessary speedup inside the switch to ensure 100% throughput with a bounded packet delay. Existing algorithms take each traffic matrix as a batch, and adopt traffic matrix decomposition techniques to decompose it into the sum of a set of weighted permutation matrices (which are then used as switch configurations). Nevertheless, existing algorithms adopt an equal weight for all switch configurations, meaning that each configuration should be held for the same time duration to transmit packets. We observe that this rigid strategy may limit the flexibility of the scheduling and result in a large speedup requirement due to inefficient time slot utilization. Motivated by this observation, we propose a UWS (Unequal Weight Sequence) algorithm to decompose the traffic matrix. UWS uses a different weight for each switch configuration. It first takes an arithmetic progression as the starting weight sequence, and then adjusts the weights for configurations to ensure 100% throughput with a bounded packet delay (such that QoS can be guaranteed). We theoretically prove that the worst case speedup of UWS will never be larger than that of the best existing ADAPT algorithm. Simulation results indeed demonstrate a speedup improvement of around 15%.

Caching content of small base stations (SBSs) is a promising approach in the fifth generation (5G) wireless networks for decreasing the transmission delay and energy consumption. The key issue in caching is effective cooperation among SBSs for full usage of the caching to reduce the systems cost. In this paper, we first introduce the architecture of wireless cellular networks with caching in 5G. We divide the caching space into two parts: One caches the fixed content purchased by telecom operators from the remote service providers, and the other one caches contents for wireless transmission. Then, we propose the cooperative caching scheme based on content-exchanged, where each user can get its demanded data from either local SBS or purchasing the contents stored in the neighboring cooperative SBSs. We further prove the concavity of system costs regarding to the proportion between the two parts. We also propose a mechanism to search for the minimal system costs. Finally, by simulations, we prove that the performance of our proposed cooperative caching scheme is better than that of the traditional cache schemes without purchasing the contents between SBSs.

Nash non-cooperative power game can be effectively used to find the power equilibrium point to enhance edge user throughput in downlink multi-user multiple-input multipleoutput (MU-MIMO) wireless networks. However, the power strategy space of the existing Nash non-cooperative power games are generally statically pre-determined without adapting to the changing wireless environment. To address this problem, we explore a novel framework of Nash non-cooperative power game with dynamic and environment-adaptive power strategy space. Then, we propose a non-cooperative water-filling power game with pricing (WFPGP), wherein the power strategy space is dynamically determined by iterative water- filling algorithm. We derive the sufficient condition for the existence and uniqueness of the WFPGP game which can be implemented in a distributed manner. Simulation results are used to compare the performance of WFPGP with the other Nash non- cooperative power games and confirm the stable and superior performance advantages of WFPGP.

The explosion of Internet traffic calls for quality of service (QoS)-guaranteed packet transmission in wavelength division multiplexing (WDM) networks with high energy and bandwidth efficiency. Conventional routing and wavelength assignment (RWA) algorithms focus on circuit switching, which does not well meet this requirement due to the bursty nature of IP traffic. Based on a novel traffic matrix decomposition technique, we study the joint design of traffic scheduling and routing in a reconfigurable WDM optical network to improve energy and bandwidth efficiency. Specifically, every node in the network is equipped with a set of parallel tunable lasers, each with a reconfiguration overhead. A dynamic matrix is adopted to model the traffic among the nodes and is decomposed into a set of transmission configurations (i.e., traffic scheduling). The configurations are then fulfilled by tuning the parallel lasers and routing the scheduled traffic under the topology constraint, to achieve loss-free packet transmissions with bounded delay (i.e., QoS guarantee). We reveal that a tradeoff exists between the packet delay and the required number of tunable lasers. The latter is then minimized under a given packet delay to save energy. As far as we know, this is the first work to adopt traffic matrix decomposition in WDM networks to save energy. The proposed framework is validated by extensive simulation studies.

In 3GPP LTE-A, Coordinated Multi-Point (CoMP) is adopted to enhance the transmission rates of edge users. To maximize the total downlink throughput of all edge users, it is crucial to properly determine the set of simultaneously served users in each physical resource block (PRB) and the cooperative base stations (BSs) for each scheduled user, as well as the transmit power of the BSs. Based on the reference signal receiving power (RSRP) of each edge user, we first propose two simple and integrated transmission scheduling algorithms, one distributed and the other centralized, to choose cell-edge users and cooperative BSs in each PRB. With the scheduling results, the classic Water-Filling (WF) algorithm is carried out over all PRBs at each BS to get an initial single cell power allocation. To take the interference among different cooperative BS sets into account, we further formulate a non-cooperative power allocation game to adjust the initial power allocation for interference coordination, where the initial power allocation provides the strategy space of the game for each BS. This increases the total downlink throughput of edge users over all BSs. We prove that the game has a unique Nash Equilibrium (NE), and design an algorithm to find the NE. Performance gain is then demonstrated through extensive simulation studies.

Data center networks (DCNs) generally adopt Clos network with crossbar middle switches to achieve non-blocking data switching among the servers, and the number of middle switches is proportional to the number of ports of the aggregation switches in a fixed manner. Besides, reconfiguration overhead of the switches is generally ignored, which may contradict the engineering practice. In this paper, we consider batch scheduling based packet switching in DCNs with reconfiguration overhead at each middle switch, which inevitably leads to packet delay. With existing state-of-the-art traffic matrix decomposition algorithms, we can generate a set of permutations, each of which stands for the configuration of a middle switch. By reconfiguring each middle switch to fulfill multiple configurations in parallel with others, we reveal that a tradeoff exists between packet delay and switch cost (denoted by the number of middle switches), while performance guaranteed switching with bounded packet delay can be achieved without any packet loss. Based on the tradeoff, we can minimize the number of middle switches (under a given packet delay bound) and an overall cost metric (by translating delay into a comparable cost factor), as well as formulating criterions for choosing a matrix decomposition algorithm. This provides a flexible way to reduce the number of middle switches by slightly enlarging the packet delay bound.

In LTE-A (3GPP LTE-Advance) systems, CoMP (Cooperative Multi-Point) is adopted to enhance the performance of edge users. To maximize the edge user throughput, it is very crucial to properly determine the set of simultaneously served users in the same PRB (physical resource block) and cooperating BSs (base stations) for each selected user, as well as the transmit power of the BSs. In this paper, we first propose a simple scheduling algorithm to choose cell-edge mobile stations (MSs) and cooperating BSs for each PRB according to the RSRP (reference signal receiving power) of each MS, based on which the classic Water-Filling (WF) is applied at each BS to allocate transmit power over all PRBs. However, the objective of single cell power allocation is to maximize the throughput of each individual cell without considering interference among different cooperating BS sets. Therefore, we further formulate a power reallocation mechanism using non-cooperative game theory to refine the single cell WF result for interference coordination, which maximizes the total edge user throughput over all BSs and PRBs by properly taking CCI (co-channel interference) into account. Based on proving the existence of a unique Nash Equilibrium for the formulated game, we design an algorithm to find the Nash Equilibrium and demonstrate the performance gain through extensive simulation studies.