This paper proposes a unified analytical framework for reconfigurable intelligent surface (RIS)-assisted two-way full-duplex (TW-FD) communication systems in 6G Internet of Things (IoT) scenarios. The proposed framework specifically addresses RISs with N reflective elements, facilitating efficient bidirectional communication. A novel unified moment-based analytical approach is developed, accommodating diverse fading models including Rayleigh, Nakagami-n, Weibull, Nakagami-m, and κ-μ, thereby significantly enhancing the versatility and practicality for complex 6G IoT environments. To comprehensively validate the applicability of our analysis, both independently identically distributed (i.i.d.) and independently non-identically distributed (i.n.i.d.) fading channel scenarios are investigated. By employing the Edgeworth expansion method, we derive analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-interference-plus-noise ratio (SINR). Additionally, closed-form expressions for probability, average symbol error rate (SER) for various modulation schemes, and end-to-end ergodic rate are provided. Monte Carlo simulations demonstrate the accuracy and robustness of the theoretical models proposed. The results presented in this work not only contribute substantially to the analytical methodologies for RIS-assisted communication but also offer practical guidance for the design and optimization of future 6G IoT systems.

In integrated sensing and communication (ISAC) systems, the timeliness performance is pivotal to support high-precision sensing and reliable high-rate data communication, particularly for real-time applications. However, there are two critical challenges in the research on the timeliness of ISAC systems: joint characterization of sensing-communication time-liness tradeoffs and unified resource allocation optimization. To address these challenges, a timeliness-centric analysis and optimization framework is proposed for ISAC systems. First, the age of information (AoI) of sensing tasks and the delay of communication tasks are analyzed to evaluate the timeliness performance of ISAC systems. Then, the closed-form expressions of the outage probabilities for sensing and communication tasks are derived, quantifying their reliability under dynamic channel conditions. Next, a joint sensing-and-communication time-allocation optimization problem is established to minimize the weighted sum of sensing AoI and communication delay. To tackle the non-linear integer programming problem, a dynamic programming (DP) algorithm is designed, which can be verified to converge to an optimal solution with polynomial computational complexity. Simulation results are presented to validate the accuracy of the analytical results, and demonstrate the optimality and performance gains of our proposed DP algorithm.

Edge intelligence has emerged as a promising strategy to deliver low-latency and ubiquitous services for mobile devices. Recent advances in fine-tuning mechanisms of foundation models have enabled edge intelligence by integrating low-rank adaptation (LoRA) with federated learning. However, in wireless networks, the device heterogeneity and resource constraints on edge devices pose great threats to the performance of federated fine-tuning. To tackle these issues, we propose to optimize federated fine-tuning in heterogenous wireless networks via online learning. First, the framework of switching-based federated fine-tuning in wireless networks is provided. The edge devices switches to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of device heterogeneity and transmission unreliability. Second, a tractable upper bound on the inference risk gap is derived based on theoretical analysis. To improve the generalization capability, we formulate a non-convex mixed-integer programming problem with long-term constraints, and decouple it into model switching, transmit power control, and bandwidth allocation subproblems. An online optimization algorithm is developed to solve the problems with polynomial computational complexity. Finally, the simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.

Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.

The technique of digital twin network (DTN) has been considered as a promising technique of network management and control to accommodate disruptive applications for the sixth generation communication (6G) systems. However, it is still challenging for DTN to provide ultimate experience for the emerging services and applications, due to the lack of effective DTN model management strategies. To solve this problem, the model selection and offloading for DTN is studied in this paper. First, a framework of cooperative model selection and offloading for DTN is designed, which can adapt with the limited computation capability of users, and improve the communication efficiency of model offloading. Second, the performance metrics are proposed to effectively evaluate the accuracy loss and the privacy leakage risk of model management for DTN, and tractable expressions are provided for our studied framework. Third, a joint optimization algorithm is designed for model management and transmit power allocation, which can efficiently reduce the accuracy loss and the privacy leakage risk with low processing latency. Finally, the experiment results are provided to show the effectiveness of our introduced performance metrics, and verify the performance gains of proposed optimization algorithm for our studied framework.

Integrated sensing and communications (ISAC) has been widely applied in Internet of Things (IoT) networks for its capability to simultaneously support high-performance communication and sensing, and non-orthogonal multiple access (NOMA) is introduced to further improve spectral efficiency and connection density. However, with the deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) and high-frequency (HF) technologies, the near-field (NF) paradigm replaces the conventional far-field (FF) paradigm and becomes dominant, necessitating a reassessment of NOMA-based ISAC system performance in the NF region. To address this, a novel NOMA-based ISAC scheme in NF XL-MIMO systems is proposed in this paper, wherein a multi-beam design based on subarray partitioning is employed to realize a communication-and-sensing coexistence ISAC system, while the additional distance-based degree of freedom (DoF) provided by the unique NF beamfocusing is utilized to improve NOMA performance gains. To balance the optimal performance tradeoff between communication and sensing, an optimization problem for joint device scheduling, subarray partitioning, and power allocation is formulated to maximize the ISAC joint rate under various constraints. Based on alternating optimization (AO) and fractional programming (FP) techniques, an efficient joint optimization algorithm is developed to solve the complex non-convex problem with coupled variables. In particular, the original problem is decoupled into three subproblems and solved by applying the linearization of 0-1 polynomial programming, Lagrangian dual reformulation, and quadratic transform techniques. Numerical results validate that our proposed NOMA-based ISAC scheme and joint optimization algorithm significantly enhance the ISAC joint rate performance in NF XL-MIMO systems for IoT networks.

Integrated sensing and communication (ISAC) has been proved as a promising technique to further improve the performance for both the communication and the sensing tasks in wireless networks. However, due to the complicated and dynamic interference circumstances, the performance of ISAC cannot be guaranteed. To provide some insights for keeping a sophisticated balance between communication and sensing with considering co-channel interference, the theoretical performance of ISAC is studied in this paper. First, an analytical system model is provided based on stochastic geometry. Second, the success serving probability (SSP) is defined for both the communication and the sensing tasks based on mutual information, which provided a unified analysis framework for ISAC. The tractable expressions of SSP are also derived. Finally, the simulation results are shown to verify the analytical results of SSP, which can provide some insights for the tradeoff between sensing and communication of ISAC.

Existing integrated sensing and communication (ISAC) systems face two critical challenges: a trade-off between resource allocation and interference management, and a lack of a unified performance metric that can be simultaneously applied to both sensing and communication functions. To address two aforementioned issues, a non-orthogonal multiple access-assisted ISAC (NOMA-ISAC) scheme is first proposed in this paper, which can simultaneously perform the sensing and communication tasks on shared radio resource, significantly improving spectral efficiency while mitigating mutual interference. Furthermore, in order to provide a unified metric to simplify the performance analysis in ISAC systems, we extend the definition of the outage probability from a mean square error (MSE) perspective. Then based on the built framework, the tractable expressions of the outage probability are derived for both communication and sensing tasks to evaluate the performance of our proposed NOMA-ISAC scheme and the conventional orthogonal radio resource-based ISAC (OR-ISAC) scheme. Next, the asymptotic outage probability comparisons are carried out to show that the NOMA-ISAC scheme can achieve better performance than the OR-ISAC scheme. Finally, simulation results are provided to verify the analytical derivations, and also demonstrate the robustness and performance gains of our proposed NOMA-ISAC scheme over OR-ISAC scheme.

Federated learning is a promising technique to implement network intelligence for the sixth generation (6G) communication systems. However, the collected data in wireless networks is non-independent and identically distributed (non-IID), which leads to severe deterioration of model performance. Although various enhanced schemes are proposed, it is still challenging to balance the communication cost and the model performance, due to the scarcity of radio resource for model update in wireless networks. In this paper, an ensemble federated learning paradigm is proposed for handling non-IID data, which is also optimized for its deployment in wireless networks in a cost efficient way. First, the framework of ensemble federated learning is designed. By formulating individual user clusters, intra-cluster federated learning models can be generated to reduce the impact of non-IID data, which can be integrated to adapt to various learning data via model ensemble. Second, the optimization of user cluster formation is studied to improve the performance of ensemble federated learning, which is modeled as a coalition formation game to design a Nash-stable algorithm. Finally, the simulation results on the public data sets are provided to verify the performance gains of our proposed schemes for deploying federated learning with non-IID data in wireless networks.

Federated learning is a new artificial intelligence technology with which an edge server can orchestrate with multiple end users to train a global model collaboratively. Under this setting, users only upload the locally trained parameters instead of their local data, substantially reducing communication costs and boosting data privacy. Nonetheless, federated learning mainly relies on users’ local training, overlooking the abundant computing resources owned by the edge server. To exploit the edge server’s processing power, we propose a hybrid learning paradigm that consists of centralized and federated learning components. This scheme uploads a portion of users’ data for centralized learning when the local model is trained under federated learning. We derive a theoretical upper bound for the model accuracy, which can be used to assess the performance of the proposed new learning paradigm. To balance the computation and communication resources for a good model accuracy performance, we establish a joint optimization problem of model accuracy, latency, and energy consumption. We also devise the corresponding joint optimization algorithm to solve the problem. Experiment results show that compared with centralized and federated learning, the proposed hybrid learning algorithm can effectively improve the model accuracy and significantly reduce computation and communication resources.

Driven by big data in wireless networks, the meta-learner-based scheme provides a promising paradigm to make full use of big data at the base stations to improve the per-formance of few-shot learning tasks, which plays an important role in facilitating network edge intelligence. However, it is a dilemma to balance the few-shot learning performance and the communication costs of meta-learner transmission. In this paper, we studied the fast adaptation of few-shot learning in wireless networks. First, a user grouping-based meta-learner generation scheme is designed, and a multicasting-based model transmission scheme is proposed. Second, a learning task selection scheme is designed to facilitate the fast adaptation to few-shot learning tasks at the users. Finally, the simulation results are provided to show that our proposed scheme can achieve model accuracy performance gains with low communication costs.

Analog over-the-air computing enables a swarm of end-user devices to efficiently conduct distributed learning, where the intermediate parameters of users, such as gradients, are modulated and transmitted via a group of orthogonal waveforms, and can be mixed directly at a server without individually detecting the feedback parameters of each user. Nonetheless, the scarcity of orthogonal waveforms, as well as communication resources of the end-user devices, are throttling this paradigm in adopting complex deep learning models. To balance the tradeoff between communication efficiency and accuracy performance, we study model pruning for analog over-the-air distributed learning in this paper. First, a model pruning scheme is proposed to improve the communication efficiency of analog over-the-air training. An importance measure for model parameter pruning is also designed based on the analog over-the-air aggregated gradient, which can characterize the contribution of each parameter without removing channel fading and electromagnetic interference. Second, an analytical expression of the training error upper bound is derived, which shows the proposed scheme is able to converge even when the aggregated gradient is corrupted by heavy-tailed electromagnetic interference with an infinite variance. Finally, several experimental results are provided to show the performance gains achieved by our proposed scheme, and also verify the correctness of analytical results.

Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. To solve this problem, federated learning with non-IID data in wireless networks is studied in this paper. Firstly, based on the derived upper bound of expected weight divergence, a federated averaging scheme is proposed to reduce the distribution divergence of non-IID data. Secondly, to further harmonize the distribution divergence, data sharing is associated with federated learning in wireless networks, and a joint optimization algorithm is designed to keep a sophisticated balance between the model accuracy and the cost. Finally, the simulation results based on a common-used image data set are provided to evaluate the performance of our proposed schemes, which can achieve significant performance gains with a small price of latency and energy consumption.

Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle. As a promising solution of decentralization, scalability and security, leveraging blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain like Proof of Work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in details, and then two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism. The extensive simulations show that DAG-FL can achieve the better performance in terms of training efficiency and model accuracy compared with the typical existing on-device federated learning systems as the benchmarks.

To adapt to the complicated interference and the high dynamics of wireless circumstances, deep reinforcement learning (DRL) has been considered as a potential solution for beam management in the massive multiple-input and multiple-output (MIMO) systems. However, due to the extremely high dimensions of both action and state spaces, the existing DRL-based schemes are with high computation costs, and the practical performance is still unknown. To provide some insights, DRL-based beam management in the massive MIMO systems is studied in this paper. First, a DRL-based beam management scheme has been designed for beyond the fifth generation and the sixth generation (B5G/6G) systems, which can support the collaborative beam selections of multiple panels with low complexity and fast convergence. Second, a system-level simulation platform is developed to evaluate the performance of our proposed scheme in B5G/6G systems. Finally, the system-level simulation results are provided, which show that our proposed scheme can achieve much higher spectrum efficiency than the referred evaluation results given by international telecommunication union (ITU).

The combination of artificial intelligence and mobile edge computing (MEC) is considered as a promising evolution path of the future wireless networks. As a model-level coordination learning paradigm, federated learning can make full use of the distributed computation resource in the MEC systems, which allows the users to keep their private data locally. However, due to the unreliable wireless transmission circumstances and resource constraints in the MEC systems, both the performance and training efficiency of federated learning cannot be guaranteed. To solve this problem, the optimization design of federated learning in the MEC systems is studied in this paper. First, an optimization problem is formulated to manage the tradeoff between model accuracy and training cost. Second, a joint optimization algorithm is designed to optimize the model compression, sample selection, and user selection strategies, which can approach a stationary optimal solution in a computationally efficient way. Finally, the performance of our proposed optimization scheme is evaluated by numerical simulation and experiment results, which show that both the accuracy loss and the cost of federated learning in the MEC systems can be reduced significantly by employing our proposed algorithm.

In fog-radio access networks (F-RANs), the loadings of backhaul is the bottleneck to fully explore the potential of cloud computing capability, which provide abundant computation resources to execute the computation tasks. In this paper, the performance of computation offloading F-RANs is studied to keep a balance between the tradeoff between the costs and the gains of different computation task processing modes. First, we focus on an opportunistic computation offloading strategy in F-RANs, and the computation offloading probability is analyzed based on a stochastic geometry-based model. Second, the computation offloading procedure in F-RANs can be modeled as a Jackson network of queueing system. A closed-form expression of average delay performance is derived, and the global optimal solution of the ratio of computation tasks handled by the cloud computing center is also provided to minimize the average processing delay. Finally, the simulation results are shown to verify the accuracy of analytical results and evaluate the performance gains of hybrid computation offloading in F-RANs.

In this paper, a simultaneous wireless information and power transfer-based traffic offloading scheme is proposed for cloud radio access networks with energy harvesting-aided device-to-device (D2D) communications. Wherein, the traffic offloading via D2D communications is utilized to alleviate the heavy burden on the capacity-limited fronthauls, while the energy harvesting design is adopted to stimulate offloading by compensating the energy consumption at the D2D transmitters. Different from the conventional works that harvest energy from ambient radio-frequency signals, dedicated energy signals are designed to provide a more flexible wireless power supply for the D2D transmitters with the data-energy beamforming technique. However, transmitting the dedicated energy signals impairs the performances of the wireless information transfer. To achieve a better balance between the traffic offloading gains and the wireless power transfer costs, a weighted sum-rate maximization problem is formulated. Nevertheless, the binary variables introduced by the offloading decision and the constraints on the data-energy beamforming make the optimization problem non-convex. To solve this intractable problem, a layered optimization method with the iterative optimizing and the ellipsoid method is proposed. Furthermore, an algorithm with lower complexity is proposed with a separated data-energy beamforming design. Simulation results reveal that a significant sum-rate gain can be achieved via the proposed scheme.

A dilemma in cloud radio access networks (C-RANs) is how to keep a balance between the performance and the efficiency of centralized processing. To solve this problem, the joint design of channel estimation and cluster formation are studied in this paper. In particular, a data-assisted channel estimation scheme is used to reduce the redundant cost of training sequences, and C-RAN clusters are formed by the remote radio heads (RRHs) to provide efficient cooperation strategies. To ensure the performance of channel estimation and data transmission, the cluster formation and the channel estimation are optimized jointly. An iterative channel estimation scheme is designed by using convex optimization and the Broyden-Fletcher- Goldfarb-Shanno (BFGS) algorithm jointly. Moreover, a utility function of cluster formation can be established based on the estimates and the mean squared error (MSE) of our proposed channel estimation algorithm, and the cluster formation of RRHs can be formulated as a coalitional formation game. Finally, the simulation results are shown to evaluate the performance of our proposed algorithms.

A key problem of content caching networks is that extra radio resource blocks are consumed to push content objects, which leads to a decline of spectrum efficiency. To solve this problem, a non-orthogonal multiple access-based multicast (NOMA-MC) scheme is proposed in this paper, where pushing and multicasting content objects can be accomplished simultaneously, and thus the spectrum efficiency can be improved significantly. To evaluate the performance of the NOMA-MC scheme, an explicit expression of outage probability is derived, which shows that full diversity gains can be achieved in the single-cell scenario. Moreover, the theoretical results can be extended to the multi-cell scenario by establishing a stochastic geometry-based network model, which show that the NOMA-MC scheme can achieve better performance than the conventional orthogonal multiple access-based multicast scheme. Then, the joint design of power allocation and content matching is studied to enlarge the performance gains of the NOMA-MC scheme, and two distributed optimization algorithms are proposed by solving a hospitals/residents matching problem. Finally, simulation results are provided to verify the analytical results, and also demonstrate the performance gains of the NOMA-MC scheme.

This paper considers the application of non-orthogonal multiple access (NOMA) to a multi-user network with mixed multicasting and unicasting traffic. The proposed design of beamforming and power allocation ensures that the unicasting performance is improved while maintaining the reception reliability of multicasting. Both analytical and simulation results are provided to demonstrate that the use of the NOMA assisted multicast-unicast scheme yields a significant improvement in spectral efficiency compared with orthogonal multiple access (OMA) schemes which realize multicasting and unicasting services separately. Since unicast messages are broadcast to all the users, how the use of NOMA can prevent those multicast receivers intercepting the unicasting messages is also investigated, where it is shown that the secrecy unicasting rate achieved by NOMA is always larger than or equal to that of OMA. Simulation results are provided to verify the developed analytical results and demonstrate the superior performance of the proposed NOMA scheme.

Cloud-radio access networks (C-RANs) are considered as energy efficient network architectures. Nevertheless, a dilemma in C-RAN architecture is to simultaneously optimize both spectral efficiency (SE) and energy efficiency (EE). In this paper, the tradeoff between SE and EE are studied considering a downlink multicasting transmission scenario in a C-RAN. In particular, to effectively mitigate the severe interference caused by densely deployed RRHs and improve the energy efficiency, a thinning scheme of RRHs is proposed. With a fixed diluted density of deployed active RRHs, the explicit closed-form expressions for coverage probability, SE and EE are derived by applying stochastic geometry. By using the obtained theoretical result as a utility function, a problem of optimizing the selection radius of RRHs is formulated to make a tradeoff between SE and EE for different preferences. Then, scalarization methods are proposed to give efficient solution. Numerical results validate the correctness and precision of performance analysis and show that the proposed approaches can generate an optimal selection radius of the thinning scheme.

In this paper, we focus on optimization of simultaneous wireless information and power transfer (SWIPT) in uplink cloud radio access network (C-RAN). The key idea is to design transceiver architecture and implement the SWIPT strategy at remote radio heads (RRHs). The minimum mean-square-error (MMSE) is considered as the performance metrics with transmit power constraint and energy constraint. Toward the goal of MMSE, the precoders and the detectors are iteratively updated in proposed scheme. However, because of the energy constraint, the precoding optimization problem with a specified detection matrix become non-convex. To solve this problem, a new precoding design based on the Lagrangian dual relaxation (LDR) is developed. Meanwhile, conditions to make sure the optimization problem has its physical interpretation and LDR become convex are determined. Finally, simulation results demonstrate that the proposed transceiver design can significantly improve the system performance.

To guarantee the rapidly increasing applications of electrical consumer, ultra small cells have been proposed to provide high spectral efficiency. However, which are severely constrained by the inter-cell interference (ICI) when the system load is high, and the enhanced inter-cell interference coordination (eICIC) is key to improving performance. To quantify the performance gains from eICIC to the load-varied small cells, a stochastic-geometry model is established in this paper, and the successful coverage probability with a dynamic frequency resource allocation (DFRA) scheme adaptive to the varied system load is researched, in which the small cell base station (SCBS) location is modeled as a Poisson point process. The proposed DFRA scheme can decrease ICI through the cooperation of adjacent SCBSs, which results in the correlated spectral usage in adjacent SCBSs. Several approximations are used to handle this kind of correlation, and a Bayesian theory based evaluation method is proposed to derive the asymptotic expression of successful coverage probability. Simulation results show that these approximated expressions match well with the simulated results, and the proposed DFRA can effectively decrease ICI and significantly increase the success coverage probability.

In cloud radio access networks (C-RANs), a substantial amount of data must be exchanged in both backhaul and fronthaul links, which causes high power consumption and poor quality of service (QoS) experience for real-time services. To solve this problem, a cluster content caching structure is proposed in this paper, which takes full advantages of distributed caching and centralized signal processing. In particular, redundant traffic on the backhaul can be reduced because the cluster content cache provides a part of required content objects for remote radio heads (RRHs) connected to a common edge cloud. Tractable expressions for both effective capacity and energy efficiency performance are derived, which show that the proposed structure can improve QoS guarantees with a lower cost of local storage. Furthermore, to fully explore the potential of the proposed cluster content caching structure, the joint design of resource allocation and RRH association is optimized, and two distributed algorithms are accordingly proposed. Simulation results verify the accuracy of the analytical results and show the performance gains achieved by cluster content caching in C-RANs.

A harvest-use-store power splitting (PS) relaying strategy with distributed beamforming is proposed for wireless-powered multi-relay cooperative networks in this paper. Different from the conventional battery-free PS relaying strategy, harvested energy is prioritized to power information relaying while the remainder is accumulated and stored for future usage with the help of a battery in the proposed strategy, which supports an efficient utilization of harvested energy. However, PS affects throughput at subsequent time slots due to the battery operations including the charging and discharging. To this end, PS and battery operations are coupled with distributed beamforming. A throughput optimization problem to incorporate these coupled operations is formulated though it is intractable. To address the intractability of the optimization, a layered optimization method is proposed to achieve the optimal joint PS and battery operation design with non-causal channel state information (CSI), in which the PS and the battery operation can be analyzed in a decomposed manner. Then, a general case with causal CSI is considered, where the proposed layered optimization method is extended by utilizing the statistical properties of CSI. To reach a better tradeoff between performance and complexity, a greedy method that requires no information about subsequent time slots is proposed. Simulation results reveal the upper and lower bound on performance of the proposed strategy, which are reached by the layered optimization method with non-causal CSI and the greedy method, respectively. Moreover, the proposed strategy outperforms the conventional PS-based relaying without energy accumulation and time switching-based relaying strategy.

To realize an efficient utilization of harvested energy and improve throughput with the help of a battery, a harvest-use-store power splitting (PS) relaying strategy with distributed beamforming is proposed for the wireless-powered multi-relay scenario in this paper. To this end, harvested energy via PS can be accumulated and stored for future usage, which affects throughput at subsequent time slots due to the battery operations including the charging and discharging. As a result, PS and battery operations are coupled with distributed beamforming, such that the throughput optimization problem is intractable looking. To address the intractability of the optimization, a layered optimization method with an ideal non-causal channel state information (CSI) assumption is proposed. As a result, the optimal joint PS and battery operation design in the proposed strategy is derived in a decoupled manner. Simulation results confirm the accuracy of the proposed method, and revealed that the proposed strategy has significant performance gains over the conventional designs.

A dilemma in cloud-radio access networks (C-RANs) is to balance the cluster scale and the cooperative gains. In this paper, cluster formation for downlink transmissions in C-RANs is studied. In particular, with a fixed intro-cluster cooperation strategy, an explicit expression for the successful access probability is derived by applying stochastic geometry. By using the obtained theoretical result as a utility function, the problem of grouping remote radio heads is formulated as a coalitional formation game, and then two distributed algorithms based on the merge and split approach are obtained as efficient solutions for the cases with and without cluster size constraints, respectively. Compared with grand cluster formation, which is a centralized method, simulation results show that the proposed approaches can achieve better performance with smaller cluster settings.

User-cooperation-based multihop wireless communication networks (MH-WCNs) as the key communication technological component of mobile social networks (MSNs) can be exploited to enhance data rates and extend coverage. As one of the most promising and efficient user cooperation techniques, network coding can increase the potential cooperation performance gains among selfishly driven users in MSNs. To take full advantages of network coding in MH-WCNs, a network coding transmission strategy and its corresponding channel estimation technique are studied in this paper. Particularly, a four-hop network coding transmission is presented first, followed by an extension strategy for the arbitrary 2N-hop scenario (N ≥ 2). The linear minimum mean square error (LMMSE) and maximum-likelihood (ML) channel estimation methods are designed to improve the transmission quality in MH-WCNs. Closed-form expressions in terms of the mean square error (MSE) for the LMMSE channel estimation method are derived, which allows the design of the optimal training sequence. Unlike the LMMSE method, it is difficult to obtain closed-form MSE expressions for the nonlinear ML channel estimation method. In order to accomplish optimal training sequence design for the ML method, the Cramér-Rao lower bound is employed. Numerical results are provided to corroborate the proposed analysis, and the results demonstrate that the analysis is accurate and the proposed methods are effective.

The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple antennas is considered in this letter, where a “harvest-and-forward” strategy is proposed. In particular, the relay harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting, and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the two-stage procedure, and demonstrate that the proposed “harvest-and-forward” strategy outperforms the conventional amplify-and-forward (AF) relaying and the direct transmission.

This paper proposes a robust channel estimation strategy for two-way multi-antenna relay networks, where the two sources are not perfectly synchronized with each other. The relay is allowed to first detect the signal arriving order (SAO) and then estimate the channel matrix of source-to-relay link in order to construct the relay precoding matrix. In particular, the SAO detection is formulated as a composite hypothesis testing problem, and effectively tackled by using the generalized likelihood ratio testing (GLRT) method. Moreover, a two-step estimation algorithm for composite source-to-source channels is developed aiming at reducing the error probability of data detection, and the optimal training sequences to minimize the Cramér-Rao bound of the estimation mean square error (MSE) is derived. Simulation results show that the proposed channel estimation strategy can effectively mitigate the estimation error due to the asynchronous transmission, thus significantly outperforming the existing channel estimation method.

In this paper, a network coding broadcast (NC-BC) scheme is proposed to improve the spectral efficiency for multiple-input and multiple-output (MIMO) transmissions. Particularly by network coding the base station broadcast message with each user message respectively, NC-BC can save the radio resource allocated for base station broadcasting, which is necessary in conventional time division-based scheme. Moreover, the iterative precoding design for NC-BC is studied for approaching the optimal performance, and the sub-optimal precoding design with low complexity is also provided. The simulation results show that the proposed NC-BC scheme with precoding optimization can further improve the system transmission performance.

Joint scheduling and resource allocation in uplink orthogonal frequency division multiplexing (OFDM) systems is complicated, and even gets intractable with large subcarrier and user number. This paper investigates the resource allocation for OFDM-based multi-user multiple-access relay channels (MARC) with network coding. We formulate a joint optimization problem considering source pairing, subcarrier assignment, subcarrier pairing and power allocation to maximize the sum-rate under per-user power constraint. The problem is addressed in polynomial time by optimizing three separate subproblems, and afterwards three low-complexity suboptimal algorithms are further proposed. The simulation results demonstrate performance gain of the proposed algorithms versus per-node transmit power and source nodes number, respectively.

In this paper, physical-layer network coding with spatial modulation in two-way relaying channels is studied. Two multi-antenna source nodes exchange information via a denoise-and-forward (DNF) relay node, and space shift keying modulation is employed in both source nodes and relay. The error performance in terms of average symbol error probability is evaluated. To further enhance the transmission quality, the denoise mapping operator is concerned to be optimized, and a graph-based approach is adopted to make the problem more tractable. Effective sub-optimal algorithm based on dichotomizing method is presented to reduce the solving complexity. Simulation results in terms of symbol error rate and throughput show that the DNF protocol with optimized mapper outperforms that with random or no mapper.

In this paper, we study a special case of multi-way relaying channel, to which traditional beamforming cannot achieve the best performance due to insufficient antennas. A new transmission protocol is proposed by aligning the messages from the same pair with the help of relay precoding. As a result, inter-pair interference can be avoided and intra-pair interference can be coped with by using network coding. Then analytic results, such as the ergodic sum rate and the outage probability, are developed for the proposed protocol. The numerical results are also provided to demonstrate the performance of our proposed scheme. To improve the diversity gain of the proposed scheme, an optimal scheme is also presented.