Reconfigurable intelligent surface-assisted non-orthogonal multiple access (RIS-NOMA) transmission is a promising enabler for concurrent multicast and unicast services. Since RIS alters the wireless channel, conventional NOMA user pairing based on channel gain, as well as successive interference cancellation (SIC) decoding ordering, are no longer applicable. To address this challenge, we propose a joint user pairing and decoding ordering scheme for multicast-unicast transmission in RIS-NOMA systems. To this end, we formulate a max-min multicast rate problem under the constraints of unicast quality of service and SIC decoding rates. To tackle the problem with complex non-convex structure, we propose a low-complexity sequential rank-one constraint relaxation-based reformulation-linearization technique successive convex approximation (SROCR-based RLT-SCA) algorithm, which transforms quadratic constraints into first-order formulations. We prove that multi-variable SROCR beamforming design satisfies rank-one constraints. Simulation results demonstrate that the proposed scheme ensures both user fairness and rate fairness, while validating the necessity of jointly optimizing user pairing, decoding ordering and beamforming. Furthermore, multicast rate is up to unicast density and RIS scale: decoding ordering prevails with sparse unicast users or a large RIS, whereas user pairing dominates in densetive unicast-user scenarios with a limited RIS, where the proposed scheme still attains an improvement of up to 35.1% over the scheme of optimal pairing and fixed ordering.

Integrated sensing and communications (ISAC) is a key enabler for 6G, yet it introduces considerable interference that limits its performance. To minimize interference and enhance spectrum efficiency of the ISAC systems, we propose a user-grouping and full non-orthogonal multiple access (GF-NOMA) approach. Specifically, we first group communication users and sensing targets based on their spatial distribution, so that spatial beam orthogonality can be employed to reduce interference between different groups. Then, by taking sensing targets as virtual communication users, each group is fully multiplexed by NOMA in the power domain for interference cancellation. To optimize the performance of the proposed approach, we formulate a beamformer design problem to maximize the weighted sum of the communication throughput and sensing power. Meanwhile, we propose a double-penalty successive convex approximation (DP-SCA) algorithm to solve this problem by iteratively searching for the optimal solution. The simulation results demonstrate that the proposed approach improves the system performance by about 10% on average compared to the non-grouping approach. When compared to Partial-NOMA, the proposed Full-NOMA approach improves the communication users’ sum rate by about 20%.

Multi-beamforming is an effective technique to mitigate interference in the integrated sensing and communication (ISAC) system. This paper proposes an adaptive multi-beamforming approach, in which a demand factor that optimizes the phase weighting factor is developed for synthesizing sensing and communication beams. Based on this demand factor, a novel osprey sparrow optimization (OSO) algorithm is proposed to efficiently calculate the optimal sensing and communication beamforming vectors. Simulation results demonstrate that the proposed approach achieves the desired multi-beamforming, catering to the requirements of sensing and communication. OSO suppresses side lobes with a reduction of 5–10 dB and enjoys more accurate main lobe pointing for linear antenna arrays (LAAs), compared to either sparrow search algorithm (SSA) or particle swarm optimization (PSO). When the size of antenna arrays becomes larger, OSO achieves at least 3-fold convergence speed improvement for planar antenna arrays (PAAs).

To enhance spectral efficiency, massive MIMO, hybrid precoding (HP) and non-orthogonal multiple access (NOMA), etc. are emerged. However, these enablers need to match with their appropriate application scenarios to achieve expected performances. To this end, this paper proposes an adaptive precoding mode selection (APMS) approach for millimeter wave (mmWave) systems. Firstly, we explore the criteria for precoding mode selection and adaptively group users based on proposed triple-channel state information (triple-CSI) that consists of three parameters. Then, phase-minimum-mean-square error (PMMSE) algorithm is proposed to optimize the sum-rate of HP, and equal-gain-transmission (EGT) method is used for NOMA. Simulation results show that APMS significantly outperforms HP and NOMA. At 26 dBm transmission power and 64 physical antennas, the sum-rate of AMPS is improved by 28.2% and 38.0% compared to HP and NOMA, respectively.

Mobile edge computing (MEC) is a promising enabler for computing-intensive or latency-sensitive services. To enhance the quality of experience (QoE), this paper proposes an adaptive computation offloading approach that minimizes the normalized sum of latency and energy consumption of all computing-intensive users based on MEC offloading, device-to-device offloading (D2D offloading), and local computing strategies. An attention-enhanced multi-agent deep deterministic policy gradient (AT-MADDPG) algorithm is proposed to figure out the optimal collaborative users, MEC servers, and their corresponding offloading ratios. Simulation results show that AT-MADDPG achieves convergence before the 80th training episode, with a 68% improvement compared to MADDPG's convergence at the 250th training episode. As the number of users increases, AT-MADDPG performs even more prominently and outperforms MADDPG by 12% and 38% in terms of latency and energy consumption when the number of users reaches 40. Compared with simply offloading to D2D or MEC, the adaptive offloading approach improves the weighted performance of latency and energy consumption by 33% and 42%, respectively.

Affine frequency division multiplexing (AFDM) is a promising waveform due to its excellent performance in doubly dispersive channels. Compared with OFDM, AFDM has two more adjustable parameters with better flexibility. By analyzing the diversity gain of AFDM system and deducing the Cramér-Rao lower bounds (CRLB) of unknown parameters, this paper studies the influence of the parameter $c_{1}$ of AFDM on the bit error rate (BER) and parameter estimation. In addition, an adaptive waveform approach is proposed to adapt to different communication and sensing requirements based on AFDM parameter $c_{1}$ and a demand factor. The simulation results confirm the validity of our analysis, which shows that $c_{1}$ can be adjusted according to the demand factor to achieve the optimal performance compromise.

Aiming at the problem of weak coverage resulting from interference and obstructions in wireless networks, we propose a gridding based Reconfigurable Intelligent Surface (RIS)-assisted optimization scheme that divides problem area and RIS deployment area into several grids for better performance and lower complexity. The objective is to maximize the signal strength received in weak coverage area, with optimization variables comprising location, angle of incidence and angle of reflection of RIS. Then Particle Swarm Optimization (PSO) and Alternating Optimization (AO) algorithms are used to solve this non-convex problem. Simulation results show that our proposed scheme can achieve more than 25% improvement in terms of average SNR than conventional ones when the number of reflection elements of RIS is more than 100. Moreover, we analyze the impact of different sizes of RIS on its optimal location and the average SNR received in the weak coverage area, finding out when RIS is closer to the weak coverage area, the performance is not always better, which is different to existed observations.

Traditional device-to-device (D2D) communication improves the power consumption, communication delay and resource efficiency, but limited to communication range of devices. To this end, this paper proposes a novel UE-specific reconfigurable intelligent surface (RIS) aided D2D relay communication approach to enhance the network capacity by exploring the degrees of freedom of time, space and reuse mode. The capacity enhancement problem is formulated to maximize the overall spectral efficiency of cellular users (CUs) and D2D users (DUs) to find out the optimal reuse partners i.e., CUs and power control with a decoupling algorithm. Simulation results show that the proposed approach significantly improves the access rate and increases the total spectrum efficiency by 45.6% compared with the direct D2D communication, and outperforms traditional D2D relay scheme in terms of higher 35.2% total spectrum efficiency and less 29.3% power consumption.

In Mobile AdHoc Networks (MANETs), the dynamic of network topology and connectivity poses challenges to routing protocols. Traditional single routing protocols typically fail to meet the performance requirements of such networks. To address this issue, this paper proposes an adaptive routing framework based on real-time network topology and node change perception. Within this framework, firstly a routing protocol switching scheme is proposed to adapt to the network topology, and then a parameter adjustment scheme is used to capture node change. The simulation results show that the proposed approach outperforms the traditional single routing protocols AODV and OLSR, as well as the traditional network-aware routing protocol TARCS, in terms of 13.0%, 17.5% and 8.8% higher packet delivery ratio, and 13.1%, 21.8%, and 11.6% higher throughput, respectively. And the proposed approach reduces average end to end delay by 52.4% and 37.5%, and average jitter by 58.5% and $\mathbf{1 0. 0 \%}$, respectively, compared to AODV and TARCS.

Self-Organizing Network (SON) is a promising and enabling technology for network automation that is an important component of 6G native-intelligence. Coverage and Capacity Optimization (CCO) is one of key use cases to self-optimize users’ experience. However, the temporal granularity of CCO as a network-level optimization is comparatively larger when contrasted with that of Radio Resource Management (RRM). Moreover, the Key Performance Indicators (KPIs) and Network Configuration Parameters (NCPs) to be adjusted may overlap between both optimization techniques. To further mitigate Operation, Administration, and Maintenance (OAM) cost and address conflicts arising from different optimization techniques, this study initially presents a framework for SON-RRM intelligent optimization. Subsequently, a cross-layer Deep Reinforcement Learning approach (cl-DQN) is proposed to address the challenge of delineating the optimization tasks of SON-RRM with different temporal granularities, taking CCO as an use case. The simulation results demonstrate that the proposed cl-DQN based CCO-RRM intelligent optimization approach outperforms traditional CCO, with 6% capacity as well as 4% coverage & capacity improvements respectively. Additionally, it reduces the OAM cost stemming from antenna parameter adjustments, with a maximum reduction of 93%.

In order to meet increasing demands of higher energy efficiency and better reliability performance for the multiuser Internet of things (IoT) secure communications, in this paper, we design a reliable and energy efficient superposition modulation (SM) and singular value decomposition (SVD) detection based multi-user orthogonal frequency division modulation-based differential chaos shift keying (SM-MU-OFDM-DCSK) transceiver for IoT devices. At the transmitter, we propose to superimpose and overlap the information-bearing chaotic signals for higher energy efficiency. Since the chaotic modulated signals of each user share identical reference chaotic signals, the transmitted superimposed signal matrices have the low-rank property. Then at the receiver, we apply the SVD-aided detection to recover these low-rank signals, which can suppress the noise to attain better reliability performance. Moreover, we prove that the proposed design can achieve the maximum likelihood detection performances. Furthermore, we derive the theoretical bit rate, energy efficiency and bit error rate expressions over additive white Gaussian noise (AWGN) channel. Simulation results are then provided to validate the theoretical derivations. Subsequently, the simulated performances over AWGN and fading channels are investigated to show that higher energy efficiency and better reliability performances than benchmark schemes can be achieved in industrial IoT scenarios.

In this paper, we design a novel orthogonal time frequency space modulation based differential chaos shift keying (OTFS-DCSK) transceiver for reliable information transmissions. In this design, we exploit the orthogonal time frequency space (OTFS) modulation which is a two-dimensional modulation designed in the delay-Doppler domain, to combat multiplicative channel fading. Besides, since the OTFS-DCSK signal matrix is rank-1, we propose to utilize a singular vector decomposition (SVD) aided detection at the receiver to suppress the additive white Gaussian noise (AWGN) to further enhance the reliability. Thanks to the diversity obtained from OTFS modulation and the noise-suppression achieved by the SVD aided detection, the proposed OTFS-DCSK transceiver significantly improves the reliability performance, particular in the high-mobility wireless communications over the dual-dispersive channels caused by multipath propagation effects and Doppler shift effects. Simulation results validate the proposed OTFS-DCSK system outperforms the benchmark systems over AWGN, multipath and dual-dispersive fading channels.

To fulfill the high capacity requirements of 5G and B5G, ultra-dense network (UDN) is a promising technology. As the network becomes denser, energy consumption becomes one of the primary concerns of the operators. To reduce the power consumption of UDN, in this paper, we propose a hierarchical energy optimization approach with two energy-efficient strategies: one is dynamic coverage for large time-scale scenario; the other is individual power adjustment based on weakly standard interference mapping for small time-scale scenario. Considering a more realistic power consumption model, the optimization problem is formulated as adapting network capacity to users' demands, so as to improve energy consumption and other network performances e.g., load balancing. The proposed approach owns the following advantages: First, it can obtain the optimal solution with low computational complexity; second, they are distributed and thus the considered problem can be solved in a parallel manner; all of these make the proposed methods suitable for UDN. Simulation results verify that the proposed approach can obtain the optimal solution in few iterations and the complexity of computation is low.

With the prevalence of smart mobile devices and surveillance cameras, the traffic load within the Internet of Things (IoT) has shifted away from nonmultimedia data to multimedia traffics, particularly, the video content. However, the explosive demand for real-time video communication over wireless networks in IoT is constantly challenging both video coding and communication research communities. The state-of-the-art answer to this challenge is sliding-window-based delay-aware fountain (DAF) codes, which combine the channel-adaptive feature in rateless coding and the delay-aware feature in video coding. However, the high computational cost and large delay make it impractical for real-time streaming. To address this issue, we integrate the model predictive control (MPC) technique into DAF codes, so the complexity is lowered to an affordable level so that real-time video encoding is supported. Two schemes are developed in this paper: 1) DAF-S, the smallhorizon DAF codes and 2) DAF-O, the MPC-based DAF using video bit rate prediction. The advantages of both designs are validated through theoretical analysis and comprehensive experiments. The results of simulation experiments show that the decoding ratio of DAF-S is close to the global optimum in DAF codes, and higher than the other existing schemes; DAF-O outperforms the state-of-the-art real-time video communication algorithms.

Wireless backhauling is a promising technology for Ultra-Dense Networks (UDN). Resource management for access and backhaul is a key issue. Different from conventional resource allocation approaches, we partition radio resources for access and backhaul, taking into account the impacts of cooperative transmission modes. This problem is formulated to maximize the area throughput of UDN to obtain the optimal resource partition and power allocation. It includes two coupling problems and thus is difficult to solve. Firstly we prove the power allocation problem i.e. beamforming problem can be equally decoupled from original problem. And then, the resource partition problem can be converted to a convex optimization problem. Finally, we can get the Pareto optimal solution to the original problem. Simulation results show the gains of the proposed approach.

Network densification is a promising technology to increase 5G system capacity, including large scale multipleinput multiple-output (MIMO) and ultra-dense network. Energy saving and resource utility are the key issues for the densified OFDM system. However, most of previous studies do not taking account of the effect of resource utility in the SINR model, e.g. the load factor. In this paper, we firstly formulate the SINR using more realistic model considering load factor in OFDM systems. Based on this model the energy saving problem is formulated as a joint optimization of beamforming and power subject to users' QoS and load constraint of each cell. To sovle this problem, we propose a two-stage scheme to decouple this problem into the beamforming and power control. In the beamforming stage, we resort to a suboptimal beamformer using the idea of best linear unbiased estimator (BLUE); For the power control stage, we try to minimize the transmit sum-power while fulfilling the user's QoS and load constraints based on theory of standard interference mapping. The convergence behavior of the proposed scheme is rigorously proved. Simulation results show that the proposed scheme converges faster in multiple-antenna cases comparing with the single-antenna counterpart. Moreover the proposed beamformer owns advantage against the traditional ones in term of power saving.

This paper investigates dynamic spectrum resource utilization in dense stadium. The proposed scheme flexibly aggregates intra-site and inter-site resources for UEs taking into account various factors such as traffic demand, network load condition, channel quality. Extensive simulation results are presented to validate its performance. The results may shed some light on the design and deployment of future dense stadium networks.

Next generation cellular networks will provide users better experiences by densely deploying smaller cells, which results in more complicated interferences environment. In order to coordinate interference, power control for uplink is particularly challenging due to random locations of uplink transmitter and dense deployment. In this paper, we address the uplink fractional power control (FPC) optimization problem from network optimization perspective. The relations between FPC parameters and network KPIs (Key Performance Indicators) are investigated. Rather than considering any single KPI in conventional approaches, multi-KPI optimization problem is formulated and solved. By relaxing the discrete optimization problem to a continuous one, the gradients of multiple KPIs with respect to FPC parameters are derived. The gradient enables efficiently searching for optimized FPC parameters which is particularly desirable for dense deployment of large number of cells. Simulation results show that the proposed scheme greatly outperforms the traditional one, in terms of network mean load, call drop & block ratio, and convergence speed.

Frequency optimization has a great impact on lots of network Key Performance Indicators (KPIs). Conventional approaches focus on direct mitigating contention or interference among Access Points (APs), ignoring the network KPIs and their conflicts. In this paper, we propose a novel Frequency Self-Optimization (FSO) scheme to improve network KPIs in dense Wireless Local Area Networks (WLANs) where network environment is highly dynamic. By modeling the network load and related KPIs, we can derive an optimal frequency assignment solution for all APs. We formulate this problem as a multi-objective optimization problem and propose an Evolutionary Particle Swarm Optimization (EPSO) algorithm to reduce the user dissatisfaction degree and service interruption ratio, and improve the network throughput as well. Simulation results show that the proposed scheme greatly improves network KPIs and outperforms the traditional ones e.g. Dynamic access point Load-based Plan (DYLD) scheme.

Rapid increase of large-scale high-rise structures results in three-dimensional distribution of wireless traffic, and thus raises a new demand of three-dimensional coverage of the common control signals in cellular mobile communication networks. The control approach of three-dimensional coverage is investigated in this paper, based on planar array of macro basestation and three-dimensional beamforming technology. A novel algorithm is proposed for obtaining the excitation weight matrix of planar array. Coverage requirement is extracted according to the target coverage distances and the actual landforms in different directions, and then mapped into an anisotropic desired array pattern. The optimization model is established as a joint minimization of array pattern design error and transmission power. An approximately optimal tradeoff coefficient and the corresponding weight matrix are solved through iteration. Numerical results validate that the proposed algorithm has the advantages of excellent coverage performance, considerable saving of transmission power and effective suppression of inter-cell interference.

In this paper, we address the capacity and coverage optimization (CCO) problem for LTE networks by means of self-organizing network (SON) techniques. A novel hybrid two-layer optimization framework is proposed to enhance the network capacity and coverage, where on the top layer a network entity of eCoordinator is implemented to ensure overall network coverage by optimizing the antenna tilt and capacity-coverage weight of each cell in a centralized manner, and on the bottom layer individual eNB optimizes cell-specific capacity and coverage by tuning its pilot power in a distributed manner. A heuristic algorithm is developed for the eCoordinator operation at large time granularity and the Genetic Programming (GP) approach is exploited for the eNB operation at small time granularity, for the purpose of tracking overall network performance as well as adapting to network dynamics. Our simulation results have demonstrated the usefulness of the proposed algorithms by enhancing network capacity and coverage performance under various system requirements.

Coverage optimization is an important task, which directly affects the performance of cellular networks. It is difficult to correct the weak coverage and the over coverage simultaneously using existing methods such as adjusting antenna downtilts and/or transmit powers of sectors. In this paper, a novel coverage optimization method based on multi-sector joint beamforming is proposed, so that weak coverage area and over coverage area can be decreased simultaneously. A heuristic algorithm based on the semidefinite relaxation is then developed to obtain antenna array excitation weights with low computational complexity. Simulation results show that the performance of our algorithm is superior to that of antenna downtilt optimization algorithm.

Next generation cellular networks will provide users better experiences with smaller cells, which results in high dynamics and interferences. Conventional approaches for inter-cell interference coordination (ICIC) are based on fixed frequency reuse, which wastes frequency resources. As a result, the benefit brought by ICIC is suspect in practical networks. In this paper, we tackle this problem by optimizing fractional frequency reuse (FFR) and adapting to dynamic traffic maps. We formulate this problem as a multi-KPIs (Key Performance Indicators) optimization problem and propose a traffic-based dynamic spectrum management (DSM) algorithm to reduce the call drop and block ratio (CDBR), and improve the network throughput as well. Simulation results show that the proposed scheme greatly outperforms the traditional ones.

Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB) are two important functions of Self-Optimized Networks (SON). To improve the Key Performance Indicators (KPIs) of handover and load-balancing, conventional MRO & MLB approaches adjust different handover parameters respectively, based on cell-individual or cell-pairs. However, it is hard for these approaches to control the conflict between MRO and MLB, which results in unnecessary handovers for some users. In this work, a novel parameter configuration method for MRO and MLB is proposed, which jointly optimizes the performances of MRO&MLB by the adjustment of the same handover parameters for individual users. This method enables specific users to be correctly handed over to the neighboring cells. Simulation results show the proposed schemes can achieve better performances than the conventional scheme.

Relay technology is one of the technologies that will enhance user experience in the next generation cellular networks. One of the goals (and characteristics) of successful cellular network design is availability of services to all subscribed users irrespective of their location. Designing a good Radio Resource Management (RRM) is essential in achieving that goal. In this paper we propose a dynamic resource partitioning in the OFDMA-based Relay Enhanced Networks. We study performance of the proposed RRM scheme with fairness and maximum throughput objectives and show that it has superior performance in comparison with conventional RRM schemes.

Among many requirement to the next generation cellular networks are availability of services to all subscribed users irrespective of their location as well as controllable (e.g., low) variability of resource allocation. Achieving these tasks is possible by designing a good Radio Resource Management (RRM) mechanism. In this paper we propose a new design of dynamic RRM for OFDMA-based Relay Enhanced Cellular Networks. We study performance of the proposed RRM and demonstrate that it allows controlling variability of resource assignment at the expense of increased convergence time to a steady state. We also show that the proposed RRM can optimize fairness or maximum throughput objectives and demonstrates superior performance in comparison with conventional RRM schemes.

Technologies such as cognitive radio, interference coordination and spectrum sharing are promising to provide higher spectrum efficiency and throughput, and spectrum sensing is usually the first step. Generally, an intentionally emitted signal shows directionality at receiver in many cases. Intuitively, if we find the feature that characterizes the directionality of received signal, we can use this feature to identify the presence of a signal. In this paper, we propose a new detection method by exploiting directionality of a signal with only one snapshot, and the performances of the directionality detection in various conditions are also examined. Simulation shows that the proposed directionality detection achieves better performance than conventional energy detection by carefully designing the beams of receiver antenna system in some scenarios.

Intermittently Connected Wireless Networks (ICWN) or Delay/Disruption Tolerant Networks (DTN), have attracted attention from researchers because of their inherent characteristics, including long latency, low data rate, and intermittent connectivity. Extensive research has been conducted on ICWN, including the architecture, and routing. However, few researchers have investigated the performance of TCP in ICWN, especially when Epidemic Routing is used. In this paper, we first evaluate the performance of TCP in ICWN with Epidemic Routing. Our results show that the Epidemic Routing in ICWN degrades the performance of TCP because multicopy data packets cause duplicate ACKs, and in turn reduce the transmitting rate of TCP. Then an enhanced algorithm for TCP, named A-TCP/Reno is proposed to solve the above problem. A-TCP/Reno can avoid the duplicate ACK problem which is caused by Epidemic Routing. The simulation results show that A-TCP/Reno outperforms the TCP/Reno in ICWN with Epidemic Routing protocol.

IEEE 802.16 supports both single-hop and multi-hop mesh modes. It defines several traffic and service categories and offers differentiated quality of service (QoS) through scheduling algorithms. In IEEE 802.16, a three-way handshaking mechanism is given to reserve the slots hop-by-hop for mesh networks, which is called Hop-by-hop Bandwidth Reservation Protocol (HBRP). However, HBRP can not assure the End-to-End QoS, and can not optimally schedule the idle slots. In this paper, we provide a QoS assurance method in IEEE 802.16 Mesh networks, named End-to-End Bandwidth Reservation Protocol (EBRP). EBRP includes End-to-End optimal bandwidth calculation and End-to-End resource reservation. The performance analysis shows that the EBRP is more effective than HBRP in the existing Mesh networks.

Relay Stations (RSs) selection and spectrum allocation is a very important problem in cognitive Multi-hop Cellular Networks (MCN). In this paper, we propose a practical joint RS selection and spectrum allocation scheme to maximize the system capacity while fairness is guaranteed. In the proposed scheme, Mobile Stations (MS) are adaptively and optimally selected to be RS based on spatiality of spectrum sets and multi-hop links. Further, we simplify the optimal scheme and propose the RS non-deterministic heuristic algorithms and then compare with conventional RS predetermined algorithm in which RS are predefined before joint RS selection and resource allocation. Simulation results show that the RS non-deterministic algorithm outperforms the conventional RS predetermined algorithm in terms of spectrum efficiency.

In a spectrum overlay system, the secondary users (SUs) with cognitive radio capability need to detect the presence of primary users promptly and reliably in order to prevent excessive interference. Likewise, to make full use of the available spectra, such systems have to attain low false alarm probability. Having a high detection probability while maintaining a low false alarm probability is challenging as these are conflicting goals; therefore, an appropriate tradeoff needs to be determined. In this paper we propose a "Hierarchical and Adaptive Spectrum Sensing (HASS)" solution to multi-hop cellular networks, which is based on cooperative sensing and soften hard detection fusion mechanisms with one-bit overhead. SUs that are able to make local detection decisions either directly report their one-bit decisions to the cognitive radio base station or they relay their decisions to other favorable SUs based on the states of their reporting channels. If SUs can not make detection decisions, they relay the observed signals to other favorable SUs for further processing. Simulation results show HASS solution improves spectrum sensing performance in multi-hop cellular networks.

Cooperative communication can obtain spatial diversity without using multiple antennas, and thus achieve more reliable transmission or consume less power. Accordingly, a new cooperative MAC mechanism in wireless networks, the DCMAC, is proposed in this paper. The DCMAC makes full use of the broadcasting characteristics of wireless channel to obtain channel information, chooses the most suitable cooperative nodes, and reserves wireless channel efficiently. Evaluation results show that DCMAC can choose the most suitable cooperative nodes to improve system performance.

Relay selection is a challenging issue in cooperative communication networks. In particular, in a multi-user multi- concurrent-transmission network, effects of the resource sharing of a relay by its served users on the relay selection strategy need to be taken into account. There is little research on this investigation in literature. In this paper, we propose a novel efficient and fair relay selection strategy, in which individual user's QoS requirement is inherently taken into account. In terms of efficiency, the proposed strategy is of low computational complexity. In terms of fairness, the system resource is utilized fairly among all relaying nodes so that the system has the best potential to accommodate as many users as possible. Simulation results indicate that the proposed selection strategy outperforms many existing ones, therefore justifying our arguments.