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We consider the problem of joint resource allocation and admission control in a secondary code-division network coexisting with a narrowband primary system. Our objective is to find the maximum number of admitted secondary links and then find the optimal transmitting powers and code sequences of those secondary links such that the total energy consumption of the secondary network is minimized subject to the conditions that primary interference temperature constraints, secondary signal-to-interference-plus-noise ratio (SINR) constraints and secondary peak power constraints are all satisfied. This is an NP-hard optimization problem which motivates the development of suboptimal algorithms. We propose a novel iterative algorithm to solve this problem in a computationally efficient manner. Numerical results demonstrate that the proposed algorithm provides excellent solutions that result in high energy efficiency and large admitted percentage of secondary links.

With the explosive demand in wireless service in recent years, radio spectrum has become the scarcest resource for the modern wireless communication industry. Therefore, both spectrum regulation makers and wireless technology specialists endeavor to seek solutions that would increase the amount of available frequency spectrum. With the FCC's report that much of the licensed radio spectrum is highly underutilized [

Admission control and resource allocation problem can be traced back to the 90’s. In [

To the best of our knowledge, this paper presents the first research work on the problem of establishing secondary code-division links coexisting with a primary narrowband system. Since the wideband transmissions of secondary users generally experience multipath fading, the RAKE matched filter is utilized at the secondary receivers (SRs). Our objective is to maximize the number of admitted secondary links and minimize the total power consumption of active secondary links under the following constraints: (1) SINR constraints at secondary RAKE receivers, (2) interference constraints at the primary receivers (PRs), and (3) peak transmission power constraints at secondary transmitters (STs). This optimization problem is an NP-hard problem, which motivates the development of efficient suboptimal algorithms that provide good solutions. We propose an iterative algorithm to solve this problem which produces a suboptimal solution with excellent cognitive networking performance characteristics in terms of the number of admitted SUs and network energy consumption. We provide numerical results to demonstrate that our proposed algorithm outperforms existing signature design and admission control algorithms previously proposed in the literature.

Within this context, our main contributions in this paper can be outlined as the following.

The rest of the paper is organized as follows. We present the cognitive code-division network model and formulate the admission control and resource allocation problem in Section

We consider a primary narrowband system where each primary user holds the license for one of

After carrier demodulation, chip-matched filtering and sampling at the chip rate over the duration of a multipath extended symbol (bit) period of

At the primary narrowband receivers, secondary spread spectrum signals are seen as white noise at each licensed narrowband with constant spectral densities [

Here, we consider joint maximization of the number of admitted SUs and minimization of total energy consumption in the secondary cognitive radio network (CRN). The objective is to find a joint admission control and resource (i.e., power and signature) allocation scheme that maximizes the number of SUs accessing the spectrum and minimizes the total energy consumption. Minimizing total network energy is an important problem especially when the network has a constraint on the interference it can cause to other neighboring networks. Since our goal is to admit as many users as possible and minimize the energy consumption of the network, we initially formulate the problem as a multiobjective optimization problem with three sets of constraints: (1) SINR constraints at the SRs, (2) interference temperature constraints at PRs, and (3) power budget constraints at the STs. We can now formulate problem, referred to as P, as follows:

The problem

Since the objective corresponding to the number of admitted users has higher priority, we start the problem by assuming that all

Under the assumption that there are

After computing the secondary signature set, we consider the constraints in (

We should note here that there is a possibility of not having a feasible solution for P1, since it may not be feasible for all

When the network is dense and the number of requesting secondary links is high, it becomes difficult to support all requesting secondary links simultaneously, that is, the constraints in (

The basic idea behind our admission control algorithm is that we iteratively remove the link which violates the constraints in (

The stationary bit-energy vector

The stationary bit-energy vector

The stationary bit-energy vector

In Scenario

For Scenarios

After removing one link at a time, we return to P1 again and attempt to solve P1 and repeat the procedure in an alternating manner. We note that removing one link at a time does not necessarily solve P2, but the whole iterative algorithm solves P1 and P2 jointly as explained in the next section.

The steps of our iterative joint admission control and resource allocation algorithm are summarized as follows.

At iteration

Solve P1 using the proposed method in Section

If the constraints of P (or P2) are violated, remove one link using the proposed method in Section

1: Select an arbitrary initial signature set for

2: Calculate

3: Obtain

4: Update

5: Repeat Step 2–4 until the secondary signature set converges.

6: Calculate the matrix

7:

8:

9:

10: Update

11:

12: Repeat Step 6–11 until

13: Calculate

14:

15:

16:

17:

18: Remove the secondary link

19:

20:

21:

22: Remove the secondary link

23:

24: Remove the secondary link

25:

26:

27: Repeat Step 1–26 until

The computational cost is evaluated in terms of “FLOP”, which is defined by an additive or a multiplicative operation. The computational complexity of the signature set optimization is dominated by the complexity of the eigen-decomposition for

In this section, we provide some simulation results to evaluate the performance of the proposed algorithm. We consider a primary narrowband system with

We compare the performance of the proposed joint resource allocation and admission control algorithm (Section

In Figure

Admitted percentage of secondary links as a function of the SINR threshold for secondary links.

Average bit energy per secondary link as a function of the SINR threshold for secondary links.

Figures

Average number of admitted secondary links versus the number of requesting secondary links averaged over 1000 random channel realizations.

Average bit energy per secondary link versus the number of requesting secondary links.

We studied the problem of cognitive code-division networking where secondary users coexist with a narrowband primary system. We formulated the problem as the search for the powers and signatures of secondary links to maximize the number of admitted secondary links and minimize the total power consumption of secondary links under primary interference temperature constraints, secondary SINR constraints, and maximum peak power constraints. We proposed an iterative joint admission control and resource allocation algorithm for this NP-hard optimization problem which provides excellent results by allowing a large number of active secondary links while improving the energy efficiency of the network as shown in the simulation results.

Depending on the operation mode of the primary system (narrowband or wideband), the proposed solution in this paper can be combined with a primary system identification technique followed by an adaptive selection of secondary operation mode between the proposed solution herein and the solution proposed in [

This material is based on research sponsored by the Air Force Research Laboratory, under Agreement no. FA8750-11-C-0124. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the USA Government.