In the last few years, the use of MANETs has increased significantly due to its adaptability towards mobile-communication, low-latency routing decision ability
However, it has always been a challenge to enable energy efficiency when sensing idle spectrum across available licensed channels and transmit certain fixed amount of data over them, which can even become more difficult for MANETs which are highly dynamic in topology. Though, a few efforts have been made to enhance QoS in CRN based WSNs, where authors have either focused on minimizing transmission delay
Though, a number of researches have been done towards energy-efficiency MANETs; however majority of the existing approaches either focus on reducing active sensing nodes to reduce energy exhaustion or improve routing paradigm. Under dynamic network condition, the lack of active sensing nodes might force network to undergo delayed Channel State Information (CSI) estimation or even outdated CSI estimation. The improper functional characteristics or greedy resource access nature which is common in CRNs, it might cause significantly high interference to the PUs impacting their QoS provision. A few researches have tried to enhance resource access and transmission efficiency by means of a power and channel scheduling, it could not address interference and resulting QoS violation introduced by SUs on PUs. Though, a number of researches have been done towards energy-efficiency MANETs; however majority of the existing approaches either focus on reducing active sensing nodes to reduce energy exhaustion or improve routing paradigm or could be considered as another major research gap which is imbibed as one of our objectives of the research work proposed.
Considering such limitations and gaps which are mentioned in the previous paragraph, in this study, a robust and efficient stochastic resource allocation model is developed for MANETs that intends maintaining optimal resource access to SU, while maintaining interference (caused by SU) below a specified threshold. The main advantage of the proposed work being filling up of the gaps mentioned in the previous paragraphs and proposition of new model to produce efficient results in comparison with the work done by the earlier researchers. The proposed resource allocation model is designed as a resource allocation problem as controlled Markov Decision Process using Hidden Markov Model (HMM) and Lagrange relaxation; our proposed ISP-DRACM model achieves better resource allocation under limited noise or interference condition and hence achieves both cost-effectiveness as well as QoS provision. Realizing dynamic nature of MANET and different operating environment, the proposed resource allocation has been assessed for Interweave (also called Overlay) and Underlay setup, where resource allocation has been performed under instantaneous as well as averaged interference conditions. The overall proposed model has been developed and simulated using MATLAB 2019b tool and the performance has been assessed in terms of resource allocation and power transmission scheduling.
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Energy Awareness Optimal Relay Selection (EAORS) was proposed by Yang et al.
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This is the matter of fact that energy-efficiency and QoS provision (reliability, timely transmission, resource efficiency, etc) have always been the predominant need of any wireless communication systems. However, high pace rising demands of the QoS/QoE communication under diverse network condition possessing high mobility, heterogeneous network conditions and greedy resource access nature limit majority of the conventional routing approaches or protocols. On the other hand, bandwidth or resource being a constrained factor requires optimal resource-access, control and allocation strategy to meet user demands so as to continue communication. Noticeably, radio allocation has always been the challenge for industries due to non-linear demand patterns, energy-exhaustive scenario, competitive transmission nature and therefore it requires an optimal resource-allocation strategy. Undeniably, the resource allocation model must ensure both energy-efficiency as well as QoS provision to the users. Towards optimal resource utilization or allocation, in the last few years Cognitive Radio (CR) technology has gained widespread attention that intends to maximize resource utilization across the network amongst licensed users (say, PUs) and unlicensed users (say, SUs). As application specific purposes CR has given rise to a new networking paradigm where it comprises cooperatively functional PUs and SUs and SU intends to use unused resources to ensure optimal utilization.
Majority of existing works focus mainly on CR based sensor networks, which is hypothesized to be low in topological changes and network dynamism. On contrary, MANET as name reveals undergoes significantly high mobility and hence has exceedingly high non-linearity in resource utilization or demand patterns. In such case CR requires to be well versed to deal with dynamic topology and resource demands. On the other hand, CRN based MANETs requires maintaining optimal balance between sensing, resource access, transmission scheduling and allocation efficiency. Merely, reducing active nodes or MAC scheduling cannot yield optimal performance until strengthening the resource allocation policy with interference resilient-transmission control. In other words, scheduling resource allocation can be done better provided it allocates or schedules transmission while ensuring that the noise or interference caused by SUs on PU(s) will be significantly low so that QoS or QoE could not be affected. CRN based MANETs require maintaining energy-efficient and interference resilience to both PUs as well as SUs for QoS communication. Additionally, CR-based MANET (here onwards we call it as CRN) might function as both interweave as well as underlay which has different flexibility and operating principles towards opportunistic sensing and resource allocation to the SUs. Noticeably, in underlay CRN environment SUs are allowed to access and transmit their data opportunistically as long as it maintains interference to a defined level. On the other hand, in interweave CRN states a coexisting communication culture where SUs are required to maintain their interference level to a predefined threshold. In addition, in interweave SUs can access the spectrum which is left unused or under-utilized. However, in both these CRN environment, the transmission by SUs might impose interference to the PUs and hence can degrade QoS provision.
Considering above stated issues, in this paper we emphasized on developing a robust stochastic prediction assisted resource allocation strategy for CR based MANET or CRN under different network setup like interweave and underlay. Unlike classical researches, in the proposed resource allocation method the emphasis is made on enhancing power transmission scheduling and resource allocation while capping interference below a level. This approach is hypothesized to enable optimal resource allocation with negligible interference and hence better QoS delivery. In this paper a novel and robust Interference and Noise Resilient Stochastic Prediction based Dynamic Resource Allocation model for Cognitive MANET (ISP-DRACM) is developed to ensure optimal resource allocation under underlay as well as interweave network setup. ISP-DRACM intends to enable optimal resource allocation under interweave and underlay network setup with instantaneous as well as average interference conditions. ISP-DRACM employs a joint power management and resource allocation strategy where it intends to maximize weighted sum-rate of the SUs under certain defined conditions like average power and stochastic interference level. As probabilistic interference condition, ISP-DRACM intends to perform resource allocation under both instantaneous as well as averaged interference conditions. Inculcating resource allocation problem as controlled Markov Decision Process using HMM and Lagrange relaxation, our proposed ISP-DRACM model achieves better resource allocation under limited noise or interference condition. The proposed model has been designed for both interweave as well as underlay CRN setups. Considering working culture of both interweave and underlay CRNs, the resource allocation strategies have been examined under short-term as well as long-term interference constraints. In CRN there can be dynamic resource demands and opportunistic resource access activity and hence the assessment of both short-term (say, instantaneous) interference as well as long-term (say, average) interference can make proposed system robust to handle or deal with any dynamic conditions.
This section primarily discusses the proposed ISP-DRACM model for dynamic resource allocation in CR based MANETs. Before discussing the proposed model, a brief of CRN applied is given as follows.
Consider that the CRN under study be the MANET with unlicensed secondary user (SUs) who intends to transmit its data using available spectrum opportunistically over K spectrum or channels. We hypothesize that each spectrum or channel has the similar bandwidth and belongs to or connected to the different licensed PUs. Let the considered CR based MANET be possessing a CRN network controller (NC) especially designed to operate for gathering CSI and associated dynamic parameters to make resource and interference adaptive spectrum allocation decision, while ensuring minimum packet loss, retransmission probability, energy exhaustion and QoS violation. Functionally, NC intends to collect dynamic CSI information to make adaptive resource allocation scheduling. A snippet of the CSI model considered in ISP-DRACM is given in subsequent section.
The CSI incorporated in our dynamic spectrum sensing and allocation strategy contains the details pertaining to the channel statistics. In ISP-DRACM, CSI is accessible to each node or users, where the CSI can be heterogeneous in nature, which is different for PUs and requesting SUs. It makes CSI estimation highly intricate for NC. We considered CSI heterogeneity due to two key reasons, first that the CSI accessibility for the links pertaining to PUs or SUs is different and second that the CSI can have decisive impact on resource allocation strategies. CSI for SUs (say, CR-to-CR) data transmission link can be called as static and often known (say, perfectly known). In other words, in case of CR-to-CR communication (to be noted, here CR says a participating MANET’s node or sensor node requesting resource access from the PU), at each time slot, the instantaneous gain of the SUs can be obtained deterministically. Consider that at certain instant
In other words,
In interweave setup NC needs the information whether each of the radio-band is occupied or is being used. In addition, it estimates up to what extent it is being used. To achieve it, we introduced a Boolean constructs
The derived belief factor (1) can be hypothesized to be existing when the probability mass of
To enable optimal and energy efficient resource allocation, it is must to avoid outdated CSI information and “gather and exploit” dynamic CSI information of
where i, j = 0, 1.
Majority of the classical radio sensing methods have not considered any error probability, which can’t be generalized under dynamic conditions of CR based MANETs or CRN. Hence, to alleviate the issue of the sensing error, we considered a factor called the likelihood of miss detection
The CSI values obtained signifies the estimated states of a HMM where we have applied a Recursive Bayesian Estimation Model (RBEM) to estimate the instantaneous belief factor (1). Here, we update
If
If
If
To be noted, the above derived model is similar to the RBEMs, like Kalman filter based prediction-correction. However, considering dynamic resource allocation scenario in mobile network like MANET, the conventional approaches seems confined. It becomes even more significant when the radio spectrum sensing error varies, and transition matrix
In case of Underlay setup, NC needs knowing the channel gains of SU to the PU channels, where it is expected that the SU might use available resource at PU, provided its interference level remains lower than a defined threshold to preserve QoS for PUs. Here, the CSI involved encompasses the values of the instant squared fading coefficient between the
Let,
Considering sensing error issue we hypothesized to have memory less additive noise model given as (7).
where, the second term
For
Since, the number of unobserved HMM can be indefinite the denominator of (8) can be presented as an integral. To obtain CSI under different CRN conditions, we applied different NCs, especially for PUs and SUs. Noticeably, the secondary CSI states inter-SUs or CR-to-CR (inter-node) link gains, that states the primary interference. Primary CSI is obtained either by the PUs activity vector alone in interweave network. On contrary, for underlay CRN, it is obtained by CR-to-PU a channel gain that doesn’t consider secondary interference. Here, the secondary CSI is hypothesized to be perfectly known and hence the information pertaining to the instantaneous realization is deterministic in nature. Here, Primary CSI is stated to be uncertain, so that the belief state for the instantaneous realization could be probabilistic in nature.
With the resource allocation model and allied variables be the function of
Unlike classical resource allocation models, in ISP-DRACM we intended to apply different constructs like network dynamism, network parameters under different interference conditions, CSI information etc. that eventually strengthens it to enable QoS centric and power efficient resource allocation strategy. To achieve it, we formulated overall resource allocation problem by identifying optimal variables, optimization metrics and operations conditions or constrains to be meet. We considered
In (9),
A SU fulfilling the condition
Under such circumstances the optimal resource allocation can be performed by achieving the solution for the problem mentioned in (12).
Before discussing the resource allocation under interference a brief of the optimal resource allocation without interference is given in the sub-sequent section.
Before discussing resource allocation problem under interference, a brief of the resource allocation model without interference is discussed in this section. As depicted in (12), even if it depicts a non-convexity problem, it can be solved or relaxed to an equivalent convex problem using Karush-Kuhn-Tucker (KKT) conditions. The derived model (12) signifies a weighted sum-rate optimization problem for a channel, where
Observing above expressions, it can be found that
Observing literatures, it can be found that though numerous efforts have been made towards resource allocation; however majority of the researches focus on CRN without interference and noise condition, which cannot be optimal in practical MANET scenario. Considering this fact, we have focused on performing resource allocation under different noise and interference conditions. Here, we assume that limiting the average interference power and noise can help making optimal (interference-resilient) resource allocation. To ensure QoS to the licensed users or PUs in CR based MANET, we focused on confining the interference caused by SUs. Towards this motive, identifying and suppressing the probabilistic constraints can help reducing CSI imperfections that eventually will make resource allocation efficient. Interference cancellation and allied resource allocation can be achieved under two distinct methods, short term interference or long term interference. These interference models are also called as instantaneous and average interference, respectively. Here, instantaneous constraints require maintaining a defined interference probability at each instant. On contrary, the average interference constraints enable PUs to be interfered up to a tolerable level over certain duration. Functionally, instantaneous constraints are more restrictive than the average interference constraint. Thus, for CRN a SU can expect transmitting higher data rate under average interference constraints. Interestingly, resource allocation optimization is relatively easier in case of instantaneous constraints, while the same can’t be easy for average interference constraints and hence it requires dual (optimization) scheme to solve it. In addition, there are different interference conditions such as underlay and interweave that introduce interference distinctly in CRN settings. Here, we have defined the duality problem for resource allocation under underlay CRN setting. Unlike existing methods, in ISP-DRACM we considered different interference conditions such as interweave and underlay under instantaneous and average constraints condition to perform resource allocation. Here, our prime motive is to design a robust dynamic resource allocation model which can be applied in any operating MANET conditions without imposing computational overheads and energy exhaustion. The details of the resource allocation for the instantaneous and average constraints is given in the sub-sequent sections.
Considering QoS provision to the PUs in CRN, it is inevitable to maintain and control the interference caused by SUs. To achieve it under instantaneous interference condition (also called short-term interference), we applied a threshold level called the maximum Interference probability
For interview CRN, interference might come into existence when
In (11), at time slot
Hypothesizing
Being related to the belief factor
To meet the interference condition under interweave set up the optimal resource scheduling can be achieved as per (21).
Summarily, a SU can access the channel provided it maintains the likelihood of the spectrum accessed or to be used lower than
For underlay CRN, interference comes into existence when a PU finds received interference power caused due to SU’s transmission higher than a threshold
At
And hence,
Applying the derived belief factor for primary CSI at
With
Noticeably, with the perfect CSI, as there is no uncertainty in
So far we discussed the interference-resilient resource allocation in CR-based MANET under short-term interference conditions or instantaneous interference conditions. However, realizing the long-term interference which can be possible due to pre-established CRN encompassing multiple cooperatively functional cognitive (MANET) nodes, we have developed resource scheduling strategy to handle aforesaid issue. The detailed discussion of the proposed resource allocation model under long-term interference constraint is given in the sub-sequent section.
In practice, for interweave CRN the main problem is not in satisfying the interference constraint, but in retrieving the likelihood of a PU to be active. Though, it is possible by applying efficient sensing approaches. In case of long-term interference PUs are expected to be in under interweave setup. We used a dual relaxation method to perform interference-resilient resource allocation. For instantaneous interference condition interweave setting needs fulfilling the condition (27).
We, intend to enable long-term constraint where at each time instant the interference is maintained below a threshold while enabling resource allocation to an optimal level. Here, we consider being the upper bound for those time spans for which interference takes place. To achieve it, we apply the following condition.
The expectation function in (28), considers all CSI realizations. Additionally, the model developed above signifies the joint probability of the PU being active and NC can schedule one SU to transmit over channel. In ISP-DRACM model, to confine the likelihood of one SU to be active while satisfying PU uninterrupted active state, we multiply
In case of CSI as imperfect,
Knowing the status that
To enable optimal resource allocation in long-term constraints, we implement the concept of dualization that at first obtains the likelihood of interference to the PU and allied resource allocation. This process, being independent of CSI imperfections makes resource allocation optimization as non-convex. In ISP-DRACM to enable interference resilient resource allocation we formulated our model by confining instantaneous interference probability under underlay setup that gives rise to the following condition (31).
In other way, resource scheduling requires fulfilling (32).
For long-term interference which can be common in case of CRNs, with all channels while hypothesizing SUs to be causing interference, we derive a constraint to be followed (33).
Thus, performing averaged CSI estimation over all
Consider that the Lagrange relaxation or multiplier allied with certain
Now considering (29) and (34) the mathematical model derived in (34) use primary as well as secondary CSI in addition to the trades off rate reward with cost of interference and associated transmission power. Replacing (13) with (34), it can be found that the optimal power in (14) (i.e., without interference) and allied transmission (and/or resource) scheduling (16) remain same; however, (26) losses relation with (15) because the third entity in (34) depends on the transmission power, while the optimal power relies merely on initial two terms (15). Practically, due to non-convexity of
Consider that the maximum power to be transmitted (while ensuring interference resilient transmission and resource access) be
In ISP-DRACM, in case of high interference cost, the power transmission is confined to a level
In our proposed resource allocation strategy we intend to estimate the optimal values for
Considering the optimization objectives, the above derived model (36-38) facilitates a fair stochastic sub-gradient of the dual function for the optimal resource allocation (12). In ISP-DRACM resource allocation is scheduled under interference constrained scenario without causing any performance degradation. Thus, obtaining the following values
This research focused on ensuring resource allocation while maintaining low interference or noise onto the PUs. In our proposed model and allied simulation, realizing network dynamism we introduced varying channel condition at the different instants, where the channel condition and corresponding allocation scheduling was performed over continuous time-series over a definite span. Noticeably, being stochastic prediction based resource allocation and/or power transmission strategy over mobile topology of MANET, performing power transmission control over each time instant n is must and therefore we performed scheduling over n. ∆T time span, which was considered as 10000. n.∆T can also be stated as the total simulated time instant over which the resource scheduling was performed. In our proposed ISP-DRACM model SUs were assigned with the initial transmission power, and the total number of SUs considered was 10, while only two nodes were assigned as PU. Some of the key simulation parameters used in ISP-DRACM is given in
Parameter |
Value |
Network type |
CRN |
Number of users |
8 |
Number of frequency bands |
10 |
Average transmit power constraint per CR |
|
Maximum Interference probability from SU onto PU |
.04*ones(No. of frequency bands,1) |
SNR for which interference occurs |
.5*ones(No. of frequency bands,1) |
Average Power allowed at Prim. Rx Side (for comp. purposes) |
.5*ones(No. of frequency bands,1) |
User priority coefficient |
ones(No. of users,1) |
Average SNR of the secondary channel |
9*ones(No. of users, No. of frequency bands) |
SNR gap of the modulation w.r.t. Shannon’s limit |
1*ones(No. of users, No. of frequency bands) |
Activity Sensing duration |
3 sec |
Time correlation coefficient |
0.95 |
Inverse noise |
(1- Time correlation coefficient) |
Error in the analog measurement |
0.01 |
Estimation interval of the primary channel |
6 sec |
Number of simulated time instants |
10000 sec |
To assess robustness of the proposed ISP-DRACM resource allocation model, we simulated it for the different interference conditions or constraints such as interweave setup, underlay setup with both short-term as well as long-term interference constraints. We simulated ISP-DRACM under both known CSI as well as unknown CSI conditions; though in this simulation results we have discussed the outcome for known CSI only. To adopt realistic noise conditions in MANET systems, we considered the amplitudes of the SU channels as Rayleigh and distributed for which we maintained real and imaginary components independent. On the other hand, the primary CSI was considered to be Gaussian distributed with mean as zero and unit variance. We considered time correlated model as
To examine performance of the proposed ISP-DRACM resource allocation model, we assessed its efficacy in terms of power transmission, interference, channel utilization etc. As stated, to achieve optimal resource allocation we tried to maintain high weighted sum rate of the secondary users by maintaining interference below a defined threshold. To achieve it, we solved (6) by applying RBEM-HMM with Lagrange relaxation, where it predicted interference stochastically with reference to which the Lagrange multipliers were obtained to reduce interference. The simulations were performed for instantaneous as well as average interference conditions over underlay and interweave (overlay) CRN setup (for CR-based MANET). To illustrate dynamic performance by ISP-DRACM, we have plotted evolution of the noise, interference, Lagrange relaxation variables etc. over simulation period. Some of the key performance assessment variables are discussed as follows. Noticeably, being based on duality solving problem, we have obtained simulation results in the form of trajectory evolution for the different primal and dual variables which are plotted against the standard performance with known optimal values. Due to the space constraints in this manuscript, we have examined parametric evolution and respective resource allocation performances under known CSI condition for both interweave as well as underlay network.
The parametric evolution and respective interference condition over interweave CR setup is given in
In
Observing overall performance it can be visualized that the proposed model achieves convergence just after a few hundreds of the iterations and achieves stated constraints successfully to enable dynamic resource allocation to the users without imposing interference to the PUs in CRN. The resource allocation performance too affirms that the proposed model achieves optimal resource allocation while maintaining efficient transmission scheduling for SUs. The proposed model and allied simulation outputs substantiate the efficacy of the proposed resource allocation model under different CRN conditions. The proposed model fulfils expected constraints while indicating that avoiding CSI perfection might cause sub-optimal solution for resource allocation. Our proposed model affirms that exploiting statistical information and allied CSI/Interference over SU-to-PU channels can help making optimal resource allocation decision. This study confirmed that the resource allocation average or the long-term interference constraints yields slightly better resource allocation, as compared to the instantaneous one; however yields satisfactory performance in terms of noise resilience. Additionally, the interference probability estimation by our proposed stochastic prediction model helped updating belief states as per the real channel and node condition that avoided further collision and QoS violation. The resource allocation performance by the proposed ISP-DRACM model with known CSI information is depicted in
In this study, the predominant emphasis was made on designing a robust interference resilient dynamic resource allocation strategy for CR-based MANETs. Realizing the dynamic topology and opportunistic resource demand nature of CR-based MANET which can be of both interweave and underlay types, the proposed model intended to maintain high weight sum rate for SUs while maintaining interference and noise lower than a defined threshold. Here, the prime objective was to maintain interference and noise component caused due to SUs lower than an acceptable level while ensuring optimal source allocation to the users, under interweave and underlay conditions. As interference constraints, the proposed model considered both instantaneous as well as average interference constraints for which the optimal resource allocation was performed. In the proposed model, the dynamic resource allocation was scheduled as a Markov Decision Problem (MDP), where it intended to maintain or provide maximum resource utilization while limiting interference caused by SUs onto the PUs. As stated, to achieve stochastic resource allocation the proposed method intended to achieve sum-rate maximization while constraining maximum average power and interference probability. Considering network dynamism the probabilistic interference was modeled in such manner that it depicted errors and imperfections in spectrum sensing and CSI estimation. The consideration of both short-term as well as long term interference and allied dynamic resource allocation exhibits robustness of the proposed model. For short term interference the proposed model considered CSI imperfections to ensure that the interference probability at any instant remains lower than a defined level. On the other hand, the long term interference exploited the differences of the interference over different time period to ensure that the time-span during which interference occurs doesn’t exceed a defined level or threshold. The proposed model considered above stated problem as non-convex problem which was solved as zero-duality gap problem. Thus, maximizing the rate (signifying the quality of secondary links), transmission power and optimizing interference with reference to the PUs, the proposed model achieved optimal resource allocation. To achieve swift convergence, Lagrange multiplier or relaxation parameters were multiplied with above stated parameters, where Lagrange multiplier value itself was obtained with reference to the demands of the PUs and SUs in CR-based MANETs. Summarily, the use of stochastic model helped achieving probability of interference and optimal values of the Lagrange multipliers which helped enabling optimal resource allocation to the users across CRNs.