In the upcoming next-generation wireless communication systems, high-mobility support serves as an essential part. OTFS (Orthogonal Time Frequency Space) modulation is regarded as a candidate waveform for 6G systems for its robustness in high mobility scenarios. However, its performance degrades when Doppler spreads beyond the subcarrier spacing with ultra-high speed, a condition referred to as the out-of-range Doppler (OD) channel. We analyze the input-output relationship under OD channels and reveal that the Delay-Doppler (DD) domain received signal is a superposition of the transmitted signal with an additional phase term. Based on this insight, we design a cross-frame OTFS framework for channel estimation, utilizing coprime subcarrier configurations to observe varying responses across subframes, and the out-of-range parameters are resolved using the Chinese Remainder Theorem (CRT). Furthermore, an effective channel estimation method is developed to handle multipath scenarios by decomposing subframes and matching response taps using fractional and amplitude information, which transforms the OD channel estimation problem into multiple single-domain problems by processing DD domain signals in layers. Simulation results demonstrate the effectiveness of the proposed method in estimating OD channels, outperforming existing approaches that fail under such conditions.

Orthogonal time-frequency space (OTFS) is a potential waveform for integrated sensing and communications (ISAC) systems because it can manage communication and sensing metrics in one unified domain, and has better performance in high mobility scenarios. In practice, a target might come from far distance or with ultra-high speed. However, the max unambiguous range and max tolerable velocity of OTFS-ISAC system is limited by the unambiguous round-trip delay and Doppler shift, which are related to OTFS frame, i.e., time slots and subcarrier spacing, respectively. To enlarge the sensing range, a novel OTFS cross-frame ranging and velocity estimation model as well as its corresponding method based on the Chinese remainder theorem (CRT) are proposed in this paper. By designing co-prime numbers of subcarriers and time slots in different subframes, the difference in the responses of the subframes for a target can be used to estimate the distance and velocity of an out-of-range target. Several frame structures are further designed for specific sensing scenarios, such as target with ultra-high speed or at far distance. Simulation results show that the proposed method can achieve significantly better performance in NMSE compared with the classic sensing methods under the condition of same time and frequency resources.

This paper presents a novel autonomous transmissive reconfigurable intelligent surface (ATRIS) framework for weighted sum-rate maximization in millimeter wave (mmWave) high-speed railway (HSR) communications. The proposed system employs ATRIS units mounted on high-speed train (HST) windows to enable efficient signal refraction from trackside base stations (BS) to in-cabin users, creating supplementary virtual line-of-sight (LoS) paths. To address the inherent challenges of HSR communications—including severe Doppler effects, multipath degradation, reduced coherence time, and fast fading—while reducing the computational complexity of conventional optimization approaches, we develop a decentralized feedback-based optimization framework. The system autonomously optimizes ATRIS phase shifts by maximizing the fraction of power transmitted through its' surface from BS, utilizing an on-board RF-Power Detector and genetic algorithm (GA). The optimized phase shift configurations are then transmitted to the BS via backhaul link, where weighted minimum mean square error (WMMSE) algorithms optimize the beamforming vectors to maximize users weighted sum-rates. For performance benchmarking, we implement a comparative centralized optimization approach using conventional passive transmissive reconfigurable intelligent surface (PTRIS) with direct BS control signal. Extensive simulation results demonstrate that our ATRIS-based system achieves competitive weighted sum-rates within 25% of the centralized PTRIS approach while significantly reducing computational complexity from$O\left(P G N K M_{B S}\right)$to O(PGN), where$P$is population size,$G$is number of generations,$N$is number of ATRIS elements,$K$is number of users, and$M_{B S}$is number of BS antennas. These results validate the proposed framework's viability for practical HSR communication implementations operating at speeds of up to 600 km/h.

With the rapid development of rail transit, both the diversity and volume of data transmitted in Train Communication Network (TCN) have significantly increased. Conventional TCN technologies struggle to accommodate this expansion. TimeSensitive Networking (TSN), an enhanced technology built upon traditional Ethernet, not only supports periodic traffic with consistent latency but also maintains compatibility with standard Ethernet services. Its capability to handle high bandwidth, low latency and ensure reliability makes TSN a promising solution for train communications. This paper establishes a network topology and data flow model based on TSN. Building on Time-Aware Shaping (TAS), we propose the Priority-Sorted Multi-Routing No-Wait Scheduling Algorithm (PSMRS). PSMRS distinguishes between priorities and provides better service for high-priority traffic, while also enhancing bandwidth utilization, scheduling success rates, and real-time performance. Furthermore, experimental simulations are conducted using Python and OMNeT++. The results of these experiments demonstrate the feasibility of the proposed algorithm, specifically the findings verify that PSMRS ensures faster response time for high-priority traffic, boosts overall link utilization, increases data flow scheduling success rate, and delivers superior real-time performance in large-scale flow scheduling.

In this paper, we propose a blockchain-based energy trading mechanism for electric vehicle (EV) charging and discharging, which aims to provide a user-friendly, secure, and efficient energy trading platform for EV users. Firstly, EV users are required to calculate an evaluation function through the Road Side Unit (RSU) decision-making mechanism and select the RSU that maximizes their benefits to participate in vehicle-to-vehicle (V2V) energy trading. Secondly, this paper develops a mixed pricing strategy for V2V energy trading based on the Bayesian game. The strategy, combined with the real-time micro-market supply and demand status, promptly adjusts the bids of both parties of the energy transaction. After determining the transaction price, the mechanism further derives the energy trading volume that maximizes social welfare. Finally, the simulation results show that in the V2V energy transaction under the blockchain environment, adopting the RSU decision-making mechanism proposed in this paper can reduce the buyer's cost and improve the seller's revenue. The mixed pricing strategy can effectively improve the price satisfaction of energy transaction users and the success rate of transaction matching. It can protect user privacy and reduce communication loss, demonstrating blockchain's broad application prospects in the future energy transaction market.

Evolving from informatization to intelligence, railway wireless communication system (RWCS) is required to support more high quality service with strained resources. Nevertheless, due to the high mobility of trains, the perfect channel state information (CSI) of RWCS is typically hard to obtain, which may induce severe performance loss. To address these issues, we proposed an active RIS-assisted RSMA RWCS, where the CSI acquired is assumed to be imperfect. In this system, we formulated a weighted sum-rate (WSR) maximization problem to jointly optimize the beamforming, rate allocation vectors and RIS precoding matrix. To overcome the uncertainty of imperfect CSI and non-convexity of this problem, we transform orignial WSR maximization problem into a weighted average sum-rate ones, and we propose a block coordinate descent (BCD) algorithm based on weighted minimum mean square error (WMMSE) , fractional programming (FP) and sample average approximated (SAA) method to solve it. Numerical results show that active RIS-aied rate-splitting architecture has impressive advantages over conventional passive RIS-assisted space division multiple access (SDMA) under imperfect CSI.

This paper presents research on the use of multiple reconfigurable intelligent surfaces (RIS) in high-speed railway (HSR) tunnel communication systems for signal quality improvement and spectral efficiency (SE) maximization. This is done by placing multiple RISs on opposite sides of the tunnel, which then relay the signal to the onboard mobile relay (MR) of the high-speed train (HST), as it passes through the tunnel. To achieve our objective, a joint beamforming at Basestation (BS) and RISs phase shift SE optimization problem is formulated. The formulated problem is non-convex and hard to solve using existing conventional mathematical optimization techniques. We proposed a special class of deep reinforcement learning (DRL) called rank-based batch prioritization replay deep deterministic policy gradient (RBPR-DDPG) to solve the problem. The performance of the proposed solution is then compared with an adapted version of benchmark uniform random batch replay deep deterministic policy gradient (URBR-DDPG). Simulation results show that the placement of multiple RISs along the HSRtunnel line not only leads to signal quality improvement in the tunnel but also the maximization of the signal SE at the HST mobile relay.

Urban rail transit is currently moving toward automated driving and mobile occlusion, which raises higher expectations for the train positioning system. Operating in diverse environments like open spaces and tunnels, a single positioning system falls short in meeting the accuracy and consistency requirements for train positioning along the entire rail line. In response to these stringent requirements, this research proposes an Error-State Kalman Filter (ESKF) based real-time fusion train positioning system for urban rail transit, incorporating Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) technologies. Through practical measurements, an UWB ranging error model is established. Based on this model, a simulation study is conducted on the proposed real-time fusion positioning algorithm under different UWB anchor deployment methods in the context of urban rail transit scenarios. The real-time performance and accuracy of the proposed algorithm were validated through in-tunnel testing.

The vehicular networks are envisioned to support the future intelligent transportation system (ITS). Content dissemination is vital to achieving efficient data traffic management and real-time decision making. Limited resources constrain the vehicles and edge network, but the vehicle-to-vehicle (V2V) communication technology can support the conventional cellular communication link in content dissemination. To explore the full benefit of V2V, relay vehicles can be selected to support direct V2V links in situations where direct communication is not feasible due to distance or shadowing. However, not all vehicles will agree to participate in relaying without any reward. To counter this problem, we introduce an incentive mechanism to entice vehicles to assist in relaying. The trustworthiness between users is considered when establishing a V2V communication link. We jointly consider the content distribution mode and relay selection problems to maximize system utility. Moreover, improved auction algorithms were proposed to improve user utility. The proposed scheme is compared against some closely related baseline schemes.

In this paper, we develop a secure multi-layer mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) acts as an aerial edge server and assists the offloading from multiple ground users to a base station (BS), in the presence of a full-duplex active eavesdropper (AE). To enhance the computing performance, we consider a partially offloading scheme where the computational task at each user can be executed at itself and offloaded to the UAV edge server and the BS via the UAV-enabled RIS, respectively. To maximize the total number of secure computing tasks among all users, we design a low complexity iterative algorithm by jointly optimizing the RIS phase shift, UAV deployment, power and computing resource allocation subject to certain power constraints. Numerical results show that compared to benchmark offloading schemes, our proposed UAV-RIS aided multi-layer MEC design improves the computing performance by at least 12.91%. Numerical results also demonstrate the impact of the full-duplex AE and validate the robustness of our proposed solution.

Ultra-reliable low-latency communication (URLLC) and reconfigurable intelligent surface (RIS) aided communication are two key technologies in future wireless communications. However, secure URLLC over RIS-adied communication channels receives little attention in the literature. In this paper, we propose finite blocklength (FBL) coding schemes for RIS aided SISO/SIMO/MIMO systems in the presence of an eavesdropper (no direct link between the transceiver), which are based on an existing coding scheme for the point-to-point white Gaussian channel with noiseless feedback. Then, we show that the proposed schemes are self-secure when the blocklengths are larger than certain thresholds. Finally, numerical results show that for given decoding error probability and secrecy level, the required coding blocklengths of our proposed schemes are extremely short, and the secrecy capacities of the RIS-aided systems without channel feedback are significantly enhanced by our feedback schemes.

The increase of mobile applications in the internet of vehicles (IoVs), necessitates the demand for higher computation capabilities. Vehicles can transfer related applications to another nodes for processing. In this paper, an efficient task offloading scheme for cellular vehicle to everything (C-V2X) is proposed to improve offloading reliability and latency. Vehicles are grouped into clusters, where vehicles in need of assistance can transfer their task to other vehicles for processing through the vehicle to vehicle (V2V) link, or transfer their task to the mobile edge computing (MEC) server via the vehicle to network (V2N) link. Matching theory is exploited for the task assignments. Simulation results reveals that the proposed scheme performs better than the existing schemes.

Over the past few years, industry and academia have worked hard to develop and standardize vehicle-to-everything (V2X) communication, which is one of the important emerging service for next-generation wireless networks (5G). Thereby, to manage radio resource efficiently with realistic execution complexity, a vehicle density based two-stage resource management scheme for 5G-V2X network is presented in this paper, whose key targets are to reduce latency, improve throughput and guarantee reliability for V2V-UEs and V2N-UEs. Particularly, during the first level, the resource distribution strategy depend on vehicles density information (VDI), that is different compared to channel state information (CSI) and queuing state information (QSI) used during the second level by considering buffer state information. Two efficient resource management algorithms are presented that grants the optimum resource distribution for V2N-UEs and V2V-UEs. Simulation experiments reveal the promising performance of proposed scheme compared with existing scheme.

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.

Recently, power allocation has attracted more and more attention in order to optimize the performance of non-orthogonal multiple access (NOMA) systems. Different from existing works, the power allocation problems are investigated for cooperative NOMA systems with dedicated amplify-and-forward half-duplex relay (NOMA-HDR) and full-duplex relay (NOMA-FDR). From the fairness standpoint, the power allocation problems are formulated to maximize the minimum achievable user rate in the considered systems. The problems for both NOMA-HDR and NOMA-FDR systems with two-user and M-user are addressed. The closed-form power allocation policy of two-user NOMA-HDR system is obtained. Also, the optimal numerical power allocation policies for two-user NOMA-FDR and M-user NOMA-HDR systems are obtained. In addition, the problem for M-user NOMA-FDR systems is solved in noise-limited environment. Simulation results show that the proposed NOMA-HDR or NOMA-FDR scheme with power adaption clearly outperforms the NOMA-HDR or NOMA-FDR scheme with fixed power allocation. Besides, when the residual self-interference channel gain is small, the performance of NOMA-FDR system is better than the NOMA-HDR system.

In this paper, a clustering based resource management scheme for latency and sum rate optimization in V2X networks is proposed to identify distinguished demands for different kinds of vehicular links, namely cellular-vehicle-to-vehicle (C-V2V) links and cellular-vehicle-to-infrastructure (C-V2I) links, and to improve the performance of cellular user in terms of sum rate, latency and throughput for C-V2I links while guaranteeing reliability for every C-V2V connection. To address the fast channel changes due to high mobility, we present a clustering based resource management model to achieve band sharing and efficient power management that depends on large scale fading. Besides, we have also considered the resource management problem of cellular V2X and VANETs users to reduce data transmission impacts. Firstly, the total average sum rate of each C-V2I links is used as an optimization goal to increase the throughput and to reduce latency of the entire C-V2I link. Secondly, cluster based optimum algorithms are proposed which give the optimum resource management. Simulation results reveal that the proposed scheme outperforms the existing scheme.

To achieve low-latency and high-reliability (LLHR) for the vehicle-to-everything (V2X) services, the Long Term Evolution (LTE)-based solution has been considered as a promising technology. Since the existing LTE networks are based on the orthogonal multiple access (OMA), the limited spectrum resources have not been fully and efficiently utilized, so that severe data congestion and low access efficiency cannot be avoided in dense networks. Non-OMA (NOMA) provides a new solution for 5G V2X services to mitigate traffic congestion and reduce latency. Different from existing works, we propose two relay-assisted NOMA transmission schemes for 5G V2X communications, i.e., half-duplex relay-assisted NOMA (HDR-NOMA) broadcasting/multicasting and full-duplex relay-assisted NOMA (FDR-NOMA) broadcasting/multicasting, and investigate the optimal power allocation problems for them. To improve the quality of service (QoS) for the users with the poor channel conditions and to guarantee the fairness, the power allocation problems are formulated to maximize the minimum achievable rate for all users. Even though both of the formulated problems are neither concave nor convex, it is shown that the considered problems are quasi-concave. Hence, a bisection-based power allocation algorithm is proposed to obtain the optimal solutions to the problems. Numerical results demonstrate that the proposed schemes outperform the scheme with fixed power allocation obviously and achieve a significant performance improvement with respect to a suboptimal fractional transmit power allocation (FTPA) method and the optimized time division multiple access (TDMA) scheme. Besides, compared with HDR-NOMA, better performance can be achieved by FDR-NOMA scheme when the self-interference is sufficiently suppressed.

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.

Massive connectivity and safety guarantee are two main challenges in 5G vehicular communication networks. In this paper, we proposed a 5G vehicular communication network which provides active road safety application, traffic efficiency application and infotainment application at the same time. For V2V communications, we exploit SCMA(Sparse Code Multiple Access) and full-duplex to provide massive connectivity, which are also promising to reduce the transmission latency and improve the spectrum efficiency. For V2I communications, OFDMA and half- duplex technologies are adopted for traffic efficiency information transmission and entertainment content transmission. Besides, software-defined networking (SDN) and wireless network virtualization (WNV) are also exploited to slice the network into multiple independent slices, which isolates the road safety application from other applications. Then we proposed virtual resource allocation scheme, in which the absolute priority mechanism is applied to ensure the successful transmission of road safety information. The centralized scheduling and virtual resource allocation problem is formulated, and a Lagrangian dual algorithm is developed, which converges to a stable resource allocation scheme after a limited number of iterations. Simulation results show that the proposed mixed SCMA/OFDMA scheme outperforms the traditional orthogonal multiple access scheme in terms of system throughput.

Power allocation is an important issue in order to optimize the performance of non-orthogonal multiple access (NOMA) systems. However, the power allocation problem for cooperative NOMA systems has not been well investigated. In this paper, we investigate the power allocation problems for half-duplex cooperative NOMA (HD-CNOMA) and full-duplex cooperative NOMA (FD-CNOMA) systems, respectively. From the fairness standpoint, the optimization problem for each system is formulated to maximize the minimum achievable user rate in a NOMA user pair. Even though both of the formulated problems are neither concave nor quasi-concave, the optimal closed-form solutions of both cases are still obtained with the proposed two-step method. First, we transform the initial problem into a quasi-concave problem by treating the relay transmit power, namely R, as a constant, and then solve the obtained quasiconcave problem. Second, we convert the original problem into a univariate problem of R based on the results of the first step, and eventually obtain the optimal power allocation. In addition, a hybrid half/full-duplex cooperative NOMA scheme, which dynamically switches between the HD-CNOMA and FD-CNOMA mode, is proposed. After that, a relay selection scheme is also investigated to extend the hybrid scheme into general networks with multiple users. Numerical results demonstrate that the proposed hybrid relaying scheme can achieve a significant performance improvement with respect to the conventional NOMA, HD-CNOMA, and FD-CNOMA scheme.

This paper investigates the power allocation problem of full-duplex cooperative non-orthogonal multiple access (FD-CNOMA) systems, in which the strong users relay data for the weak users via a full duplex relaying mode. For the purpose of fairness, our goal is to maximize the minimum achievable user rate in a NOMA user pair. More specifically, we consider the power optimization problem for two different relaying schemes, i.e., the fixed relaying power scheme and the adaptive relaying power scheme. For the fixed relaying scheme, we demonstrate that the power allocation problem is quasi-concave and a closed-form optimal solution is obtained. Then, based on the derived results of the fixed relaying scheme, the optimal power allocation policy for the adaptive relaying scheme is also obtained by transforming the optimization objective function as a univariate function of the relay transmit power $P_R$. Simulation results show that the proposed FD- CNOMA scheme with adaptive relaying can always achieve better or at least the same performance as the conventional NOMA scheme. In addition, there exists a switching point between FD-CNOMA and half- duplex cooperative NOMA.

Cooperative non-orthogonal multiple access (NOMA) has attracted more and more attentions recently, in which NOMA-strong users play as relays to help the data transmission of NOMA-weak users. Different from existing works, we study the problem of power allocation for cooperative NOMA systems with half-duplex relaying mode. From a fairness standpoint, our proposed scheme aims at maximizing the minimum achievable user rate in a paired user group. More specifically, we divide the cooperative NOMA systems into two categories, i.e., fixed relaying scheme and adaptive relaying scheme. Fixed relaying scheme means the transmit power at the relay node, namely , is a given fixed constant while adaptive relaying scheme implies that can adapt to channel conditions according to our strategy. It is shown that the formulated power allocation problem for fixed relaying scheme is quasi-concave while the problem for adaptive relaying scheme is not. Hence, we firstly solve the former problem using a bisection algorithm by transforming it into a sequence of convex feasibility problems. Then, relying on the derived results of fixed relaying scheme, we find that the problem for adaptive relaying scheme can be converted into a univariate function about , in which the optimum can be also obtained by a similar bisection procedure. Numerical results reveal that the proposed adaptive relaying scheme always outperforms the proposed fixed relaying scheme. In addition, we also show that the cooperative NOMA systems are especially appropriate for systems under low SNR environments or having significantly different fading coefficients between NOMA users.

Power allocation plays an important role in cooperative non-orthogonal multiple access (NOMA) systems. To guarantee the quality of service (QoS) requirements of users, we present the cross layer power allocation algorithms for half-duplex cooperative NOMA (HD-CNOMA) and full-duplex cooperative NOMA (FD-CNOMA) systems respectively. From fairness standpoint, the optimization problem for each scheme aims at maximizing the minimum user effective capacity in a NOMA user pair under different delay QoS constraints. Due to the quasi-concavity of both problems, we propose a bisection-based cross layer power allocation algorithm for both cases to obtain the optimal solution. Numerical results show that proposed schemes significantly outperforms existing fixed cooperative NOMA schemes. Moreover, we also illustrate that proposed cooperative NOMA schemes are more suitable for low SNR environments compared to the optimized conventional NOMA scheme.

The convergence of communications, caching and computing (i.e., 3C) has attracted a lot of interests recently. To save communication resources, caching has been introduced into information-centric communication networks. Meanwhile, computing has also been exploited to improve the efficiency of communication networks. However, the combination of both communication, caching and computing is still at its infancy and significant research challenges remain to be addressed. Different from the previous works, we consider a virtualized 3C network, in which multiple service providers (SPs) are able to share the physical resources (i.e., bandwidth, storage and computation resources) from the same infrastructure provider (InP), and the users can dynamically choose the services from different SPs to maximize their utility. The dynamics of resource sharing between multiple SPs and users are modeled via a game theoretical approach. More specifically, the evolution and the dynamic behaviors of users are modeled as an evolutionary game. The competition among the SPs is formulated as a non-cooperative game, and an iterative algorithm is proposed to obtain its Nash equilibrium. Simulation results are presented to show the dynamics and equilibrium of the proposed game model.

Multimedia broadcast/multicast service (MBMS) transmission, which distributes the media content to multiple users on the same radio resources by using point-to-multipoint communications, is a highly spectrum efficient mechanism for multimedia communications. In this paper, we study the application of power domain non-orthogonal transmission to MBMS enhancements in a K-tier heterogeneous network, in order to satisfy the ever-increasing demands for emerging applications and performance requirements. Then, we present non-orthogonal multi-rate MBMS transmission (NOMRMT) and non-orthogonal multi-service MBMS transmission schemes and investigate their performance by using stochastic geometry. A tractable mode is developed to analyze the performance of asynchronous and synchronous non-orthogonal MBMS transmission. Based on this model, analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, average number of served users, and sum rate are derived. The results demonstrate that non-orthogonal MBMS transmission can achieve better performance than the orthogonal one, while synchronous non-orthogonal MBMS transmission is superior to the asynchronous one.

Wireless network virtualization has attracted great attentions from both academia and industry. Another emerging technology for next generation wireless networks is in-band full duplex (FD) communications. Due to its promising performance, FD communication has been considered as an effective way to achieve self- backhauls for small cells. In this paper, we introduce wireless virtualization into small cell networks, and propose a virtualized small cell network architecture with FD self-backhauls. We formulate the virtual resource allocation problem in virtualized small cell networks with FD self-backhauls as an optimization problem. Since the formulated problem is a mixed combinatorial and non-convex optimization problem, its computational complexity is high. Moreover, the centralized scheme may suffer from signaling overhead, outdated dynamics information, and scalability issues. To solve it efficiently, we divide the original problem into two subproblems. For the first subproblem, we transfer it to a convex optimization problem, and then solve it by an efficient alternating direction method of multipliers (ADMM)-based distributed algorithm. The second subproblem is a convex problem, which can be solved by each infrastructure provider. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

Recent advances of self-interference cancellation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where a FDR relay is deployed at the intersection of three sectors in a cell. Firstly, a simple but practical transmission strategy is proposed to deal with the involved interferences, i.e., multi-access interference, multi-user interference and self-interference. Then, the joint resource allocation problem is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and non-convex problem, we use Dinkelbach and discrete stochastic optimization methods to solve it efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

Wireless network virtualization has attracted a lot of interests recently. Furthermore, recent advances of self-interference cancellation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. In this paper, we propose a resource allocation scheme for virtualized FDR networks. The resource allocation problem is formulated as a mixed combinatorial and non-convex optimization problem, which involves high computational complexity. Therefore, we transfer the original problem to a convex optimization problem, which can be solved efficiently. In addition, with recent advances in alternating direction method of multipliers (ADMM), we develop an efficient ADMM-based distributed resource allocation algorithm. Extensive simulations are presented to verify the effectiveness of the proposed scheme.

Recent advances of loop interference cancellation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Unlike the existing works, in this paper, we study the energy efficiency aspect of resource allocation in FDR systems. We consider a OFDMA cellular network, where a shared FDR is deployed at the intersection of three sectors in a cell. Firstly, the problem of energy-efficient joint bandwidth sharing and power allocation is formulated as a three-stage Stackelberg game. Secondly, the subgame perfect equilibrium for each stage is analyzed. Then, the interplays of the three-stage game are discussed and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. At last, simulation results are presented to show the effectiveness of the proposed game.

Recently, the optimal transceiver design in multi-user multiple-input-multiple-output (MIMO) relay systems has attracted great attention. Unlike most existing works, we take the channel correlation and estimation errors into account, and jointly optimize the relay and receiver matrix in multiuser MIMO relay systems. First, the mean-square error (MSE) expression at receiver is derived, and a minimum mean-square error (MMSE)-based optimization problem is formulated. Then, a Bi-step iterative algorithm is developed to search for the optimal numerical solution. Besides, in order to solve the original problem more efficiently, we further transform it to a sub-optimal problem that minimizes the upper bound of the MSE. Then, we propose a structurized algorithm, where the closed-form expressions of the sub-optimal relay matrix and receiver matrix are given. Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithms.

Recently, the MIMO two-way AF relaying, a promising spectral efficient transmission technique, has attracted a great deal of attention. Meanwhile, it has been shown that it is possible to improve the performance of MEMO systems by employing antenna selection technology. Existing antenna selection algorithms in MEMO two-way AF relay assume perfect channel state information(CSI). However, as a matter of fact, it is difficult to obtain perfect CSI. In this paper, considering the antenna correlation and channel estimation error, we derive the lower bound on sum rate utilizing worst case uncorrelated additive noise theorem in MIMO two-way AF relaying system, and analyze the effect of antenna correlation and channel estimation error on the sum rate. Then, an optimal relay antenna selection algorithm based on discrete stochastic optimization is proposed to maximize the sum rate under the assumption of imperfect CSI. We also prove the convergence of the proposed algorithm through theoretical analysis. Through extensive numerical simulation, we observe that the proposed relay antenna selection algorithm converges to the best antenna set obtained by exhaustive search when only imperfect CSI is available.