Dynamic spectrum allocation in heterogeneous wireless networks under interference constraints across the cell coverage

At present, the fixed spectrum allocation is the accepted strategy employed by the wireless industry and regulators and a large number of spectrum resources have been wasted because of the traffic load variation in different heterogeneous wireless networks. Through the analysis of recent research about dynamic spectrum allocation (DSA), vertical handoff and multi-mode base stations, the authors have proposed the shared carrier vertical network transformation (SCVNT) algorithm, which is based on minimum total blocking probability (MTBP) or minimum redundant channel difference (MRCD). In order to evaluate the performance of SCVNT, the cellular system model and user mobility model are established, and using fixed channel allocation and dynamic channel allocation in each network, the theoretical analysis and simulation experiments are made in different user mobility conditions, different SCVNT judgment periods and different thresholds of the redundant channel difference. The results indicate that SCVNT can effectively reduce total blocking probability and enhance fairness in heterogeneous wireless networks with good compatibility and applicability.

This paper presents and evaluates the performance of a temporal dynamic spectrum allocation (DSA) scheme based on the CDMA networks as an alternative to the fixed spectrum allocation (FSA) methods. The paper considers the spectrum allocation between two code division multiple access (CDMA) systems with different time-varying traffic pattern. The traffic pattern is based on the real CDMA traffic data from Daegu urban area in Korea. A proposed algorithm is investigated by the simulation based on CDMA call processing and shows that can produce significant gains for the radio access network (RAN) in DSA.

QoS-aware distributed spectrum sharing for heterogeneous wireless cognitive networks

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Dynamic transmission power based dynamic spectrum allocation (DSA) schemes are extensively adopted in cognitive ratio (CR) networks due to their flexible spectrum access strategies to improve spectrum utility. However such DSAs suffer from high sensitivity to the environment change. Based on the protocol model of wireless communications, a notion of interference threshold (IT) is introduced into the DSA model such that the intricate interactions among CRs can be decoupled and CRs' adaptations only affect the local area. With IT modeling, CRs' strategy profile number turns into finite such that an exhaustive search based IT DSA (ES-IT DSA) scheme is feasible to enumerate all strategy profiles for finding the optimal one. To address the scalability issue in ES-IT-DSA, a suboptimal RGF-IT DSA is developed in virtue of restricted growth function theory. Simulation results concur that the IT based DSA algorithms can keep high spectrum utility while achieving simplicity.

Connected and automated vehicles (CAVs) are expected to revolutionize future transportation systems, and so, active research and development are in progress. In particular, connection functions have the potential to drive automated vehicles into the new era with cooperation and networking. Flexible wireless connection with reliability in vehicle-to-everything communication (V2X) is important for supporting various future connected applications having different demands. The reliability controlled connected function opens up new possibilities of future CAV with sharing vehicle status information, sensor information, traffic signal information under various surrounding environment. In this paper, a new smart spectrum management concept of V2X is proposed for supporting future CAV applications by using hierarchical spectrum managers with spectrum databases. In this concept, dynamic spectrum allocation and reliability prediction are realized by smartly combining spectrum measurement, modeling, database, and management. By using this concept, spectrum management can be realized by considering the surrounding radio environment and predicting the communication performance. A common V2X packet exchange process is used to derive the spectrum information by collecting the measurement data from the on-board unit installed on vehicles. A radio environment map of the exact radio propagation can be created by utilizing the measurement data collected from vehicles. A field test is performed for obtaining the measurement data over vehicle-to-vehicle communication using three vehicles. It is confirmed that the radio environment map can assist in understanding the effect of the surrounding buildings and be used for predicting the performance prior to communication. Finally, we present the challenges and future direction of the spectrum management of V2X in the future CAV era.

Mobile communication plays an important role in the future of communication systems. To meet personalized and intelligent requirements, mobile communication has evolved from single wireless cellular network to heterogeneous mobile communication network, including wireless cellular network, wireless local network, and wireless personal network. In the heterogeneous communication system, to entirely fulfill the spectrum resource complementary advantages of such a heterogeneous wireless network, the spectrum resource trade algorithm has attracted tremendous research efforts. In this paper, the advantages and disadvantages of dynamic spectrum allocation and load balancing are discussed and spectrum auction takes the place of spectrum allocation to maximize the operation revenue. The authors design a joint spectrum auction and load balancing algorithm (SALB) based on heterogeneous network environment to develop a more flexible and efficient spectrum management. The simulation result shows that SALB increases the operation profit significantly.

Dynamic spectrum allocation can improve spectrum efficiency by exploiting the traffic loads variations of multiple wireless network systems, when they share neighboring band. In this study, we use a centralized approach in which a spectrum manager periodically re-allocates spectrum among two networks in a single cell. We propose a two levels model (a spectrum level model and a call level model) to characterize the dynamic spectrum allocation. We have further validated the approximated numerical results by simulations. Numerical and simulation results show that the performances of networks with the dynamic spectrum allocation are better than those with fixed allocation and complete its performance analysis via approximation and iterative approach. Hence, we conclude that our model and scheme can improve Quality of Service (QoS) of wireless networks when spectrum is limited.

In order to fully utilize scarce spectrum resources, dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. However, the collusion among selfish network users may seriously deteriorate the efficiency of dynamic spectrum sharing. The network users' behaviors and dynamics need to be taken into consideration for efficient and robust spectrum allocation. In this paper, we model the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. In order to combat user collusion, we propose a pricing-based collusion-resistant dynamic spectrum allocation approach to optimize overall spectrum efficiency, while not only keeping the participating incentives of the selfish users but also combating possible user collusion. The simulation results show that the proposed scheme achieves high efficiency of spectrum usage even with the presence of severe user collusion.

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks.

Allowing Secondary Users (SUs) to access the licensed spectrum without causing harmful interference to the Primary Users (PUs) is crucial in enabling the Cognitive Radio (CR) technology. In order to increase the utilization of the spectrum bands, we propose a Dynamic Spectrum Allocation (DSA) algorithm that integrates both interweave and underlay spectrum access schemes. The proposed algorithm will jointly take into account the geographical locations of the nodes, the correlated shadow fading, the interference between the primary and the secondary networks, the interference between SUs that are transmitting on the same channel, and the communications activity of the users. Moreover, a suboptimal heuristic DSA algorithm that jointly takes into consideration all of the aforementioned issues, while requiring low computational- and time-costs, is developed. Simulation results show that the proposed algorithm provides a good success rate and outperforms classical spectrum allocation algorithms.

For wideband Cognitive Radio (CR) devices, the availability of primary user (PU) activity prediction model can save sensing energy and time, track spectrum variations, reduce switching overhead, improve spectrum usage and lower interference to PU. Using Universal Software Radio Peripheral (USRP) generated data, this paper has verified the superiority of Hidden Semi-Markov model (HSMM) over the conventional HMM in learning and predicting temporal correlations in PU spectral activity under non-geometric distribution of state durations. Numerical simulations further show that in a coordinated multi-SU/PU scenario, compared to the traditional random Carrier Sense Multiple Access (CSMA), the proposed HSMM-dynamic spectrum allocation (DSA) protocol significantly reduces aggregate interference to PU. Depending on the number of SU, there exists a trade-off between “interference-free coexistence with PU” and “self-existence of a large number of SU”. This problem is addressed further by statistically modeling probability of free channels and using it to optimize the number of SU that can be allocated. The proposed scheme outperforms the conventional sensing based dynamic spectrum allocation (DSA) in terms of reduced PU interference, lower spectrum handoff requirements and higher spectrum utilization efficiency. The predictive optimized HSMM-DSA can be used as an additional layer of intelligence for any CR device for efficient spectrum sharing.

A highly available spectrum allocation service model in dynamic spectrum market

Fixed Spectrum Allocation (FSA) results in suboptimal spectrum utilization, unbalanced system loading and the inability to adapt to system traffic variations when employed in multi-Radio Access Technology (RAT) systems. This paper proposes the use of flexible spectrum management techniques in multi-RAT systems. A Dynamic Spectrum Management (DSM) framework that addresses the shortcomings of FSA, while ensuring the reliable operation of all co-deployed RATs, is presented. Simulations for a system co-deploying HSPA and LTE show that the proposed framework outperforms FSA schemes and is capable of adapting to system traffic variations.

Presently a new technology which is highly preferred is wireless ADHOC networks. The traffic of the wireless ADHOC networks day by day is growing up due to this the scarcity problem arises in the available spectrum. The channel resources are allocated in Cognitive Radio Network (CRN) with a Dynamic Access methods of available sensed radio environment. The Dynamic Spectrum Allocation (DSA) is a suitable technique which solves the issue of spectrum scarcity in the CRNs. This DSA will coordinate with the channel access for schedule sensing , release connection etc. when two networks are active at the same moment. Here this research paper provides an analysis of reliable channels exclusively used for CRNs with respect to distribution of license-exempt bands and also determining the potential and limitations of every methods.