This paper deals with radio resource allocation in fourth generation (4G) wireless mobile networks based on Orthogonal Frequency Division Multiple Access (OFDMA) as an access method. In IEEE 802.16 m standard, a contiguous method for subchannel construction is adopted in order to reduce OFDMA system complexity. In this context, we propose a new subchannel gain computation method depending on frequency responses dispersion. This method has a crucial role in the resource management and optimization. In a single service access, we propose a dynamic resource allocation algorithm at the physical layer aiming to maximize the cell data rate while ensuring fairness among users. In heterogeneous data traffics, we study scheduling in order to provide delay guaranties to real-time services, maximize throughput of non-real-time services while ensuring fairness to users. We compare performances to recent existing algorithms in OFDMA systems showing that proposed schemes provide lower complexity, higher total system capacity, and fairness among users.

In fourth Generation (4G) wireless cellular networks, increasing demands for higher speed data rates transmission, mobility, and multiservice access, have imposed staggering challenges. Therefore, IEEE 802.16 standards propose the use of Orthogonal Frequency Division Multiple Access (OFDMA) among multiple alternatives. OFDMA has become one of the most interesting developments in the area of new broadband wireless networks due to its powerful capability to mitigate Inter-Symbol Interference (ISI), provide high spectral efficiency and immunity of multipath fading.

Looking at wireless networks literature, several researches focus on adaptive resource allocation algorithms for single service required by users, in order to achieve some objectives aimed either to minimize total power under data rate constraint, called Margin Adaptive (MA) problem [

The major characteristics of resource allocation algorithms consist of their running time, computational complexity, and efficiency. Generally, optimal resource allocation algorithms are classified as Nondeterministic Polynomial-time Hard (NP-Hard) problems, making them unsuitable for real-time applications such as video call. Therefore, literature tackles such problems by proposing suboptimal algorithms and heuristic methods in order to close optimal solution with low complexity and face real-time and channel variations constraints.

In this work, we propose two complementary methods in order to guarantee an efficient resource allocation policy. The first method gives new approach, in context of contiguous subchannel method, for subchannel gain computation using frequency responses dispersion. Our goal in this study is to increase the number of bit per symbol subject to maintain lower BLoc Error Rate (BLER) in mobility and high-mobility context. The main objective in this second contribution is to resolve resource assignment to mobile users' problem, in order to take into account the trade-off between maximizing resources' use and fairness. In this context, a new dynamic heuristic algorithm is proposed. After that, we bring out multiservice feature of fourth generation wireless networks by proposing an adaptive resource allocation algorithm in OFDMA systems to support a variety of Quality-of-Service-(QoS-) sensitive applications such as streaming multimedia.

The remainder of this paper is organized as follows, In Section

In this work, we consider an OFDMA system for mobile wireless networks, based on IEEE 802.16 m standard. The system consists of a single Base Station (BS) that servers

The system capacity,

Thus, the total system capacity is obtained as follows:

Approximate BER for coherent modulations [

Modulation | Formula |
---|---|

QPSK | |

M-QAM |

In this work, equal power is allocated to subchannels in downlink sense in order to reduce computational complexity. Having the target to maximize the system capacity, the objective function is formulated as follows:

The two constraints C1 and C2 are on subchannel allocation to ensure that each subchannel is assigned to only one user where

In IEEE 802.16 m systems, the total sub-carriers of one bandwidth are grouped into subchannels in order to reduce the computational complexity and the signalling overhead [

In order to compute the global subchannel gain, different methods are described in [

In this section, we propose a new method to compute the subchannel's gain depending on frequency responses. The idea here is to close the channel quality based on dispersion probability and average channel gain. We can define the sub-carrier gain array as

The number of sub-carriers with high channel gain is greater than that with bad channel gain:

The number of sub-carriers with bad channel gain is greater than that with high channel gain:

The number of sub-carriers with bad and high gain is almost the same:

Our proposed method is described as in Algorithm

(i)

(ii)

Calculate the average channel gain

the sub-carriers frequency responses where

Calculate the probability

After computing the available subchannels gain, let us move to the subchannel allocation scheme in a single-service context.

In a loaded system, the number of users

(i)

Equal power is allocated to groups

(ii)

Sort

Order user

(iii)

{% the subchannel

{ % the subchannel n is allocated}

less priority than the actual one and

{% two or more users have the same order}.

{% users with the same order do not require the

{% these users require the same subchannel

second best sub-channe

chance to get a good subchannel}.

Jump to Sub-step

second best subchannel; Jump to Sub-step (

Let us recall that

We consider three Classes of Service (CoS) which are real-time Polling Service (rtPS), non-real-time Polling Service (nrtPS), and Best Effort (BE). For each CoS, the QoS satisfaction has a distinct definition [

We assume that

For each user, a queue is used for buffering arrival packets in the proposed BS scheduler. We design the scheduling priority of each connection based on its channel quality, QoS satisfaction and service type priority.

For rtPS traffic of user

We should notice that in this rtPS-class, the packet should be immediately sent if its deadline expires before the next frame is totally served. The ratio

For the nrtPS connection of user

For the BE service of user

We should notice that the role of

After calculating the priority of each user

We should notice that in this study, each user requires a single service at the same time and packets buffered in the same queue follow a First In First Out (FIFO) scheduler. Let

The number of slots required to carry

In this work, the channel is modelled as a Rayleigh Channel with four multipaths. The simulated system consists of a single cell that uses 1024 sub-carriers for communications. In order to consider the mobility, the channel state changes every subframe delay and the simulation window is equal to 10000 subframes. Simulation parameters are described in Table

OFDMA parameters for IEEE 802.16 M.

Parameter | Symbol | Value |
---|---|---|

Subchannels number | 48 | |

Subcarriers number per subchannel | 18 | |

Number of subframes per frame | 7 | |

Sub-carriers spacing | 7.813 KHz | |

Super frame delay | 20 ms | |

Frame delay | 5 ms | |

Subframe delay | 714,286 |

We show the performance of our sliding window method compared to the minimum [

Figure

Total spectral efficiency versus the number of users in loaded systems.

Table

Variation intervals in terms of total spectral efficiency.

0.197 | 0.193 | 0.250 | 0.246 | |

1.823 | 1.883 | 1.899 | 1.923 |

BLoc Error Rate (BLER) versus the number of users.

Table

Variation intervals in terms of BLER.

−5.718 | −5.320 | −4.724 | −4.535 | |

−1.371 | −3.870 | −2.233 | −0.977 |

Our proposed resource allocation algorithm is compared with the suboptimal existing solutions [

Figure

Total spectral efficiency in loaded systems.

Table

Variation intervals in terms of total spectral efficiency.

1.31 | 1.38 | 1.51 | 1.60 | 1.70 | |

−0.11 | 0.26 | 0.72 | 1.19 | 1.55 |

To better examine the fairness of these algorithms for different number of users, their performance is shown in Figure

Jain Fairness Index versus the number of users in loaded systems.

Table

Variation intervals in terms of Jain Fairness Index.

0.183 | 0.420 | 0.549 | 0.639 | |

0.057 | 0.103 | 0.128 | 0.145 |

Figure

Outage probability versus the number of users in loaded systems.

Table

Variation intervals in terms of outage probability.

−0.943 | −2.156 | −2.809 | −3.247 | |

−3.772 | −8.626 | −11.236 | −12.991 |

For unloaded system case, the performance of our proposed algorithm is compared with the algorithms proposed in [

Figure

Total spectral efficiency versus the number of users in unloaded systems.

Table

Variation intervals in terms of total spectral efficiency in unloaded systems.

−0.231 | 0.237 | 0.687 | 1.123 | 1.458 | |

−0.087 | 0.265 | 0.709 | 1.132 | 1.466 |

The performance of the Multi-QoS-based adaptive resource allocation proposed algorithm is compared to two existing algorithms that are proposed in [

Figure

Average rtPS packet loss ratio versus the number of users.

Table

Variation intervals in terms of rTPS packet loss ratio.

−20.88 | −13.78 | −9.204 | −8.351 | |

−24.84 | −16.72 | −14.21 | −10.33 |

In Figure

Variation intervals in terms of nrTPS packet satisfaction ratio.

9.078 | 5.994 | 5.740 | 3.631 | |

27.21 | 18.32 | 15.57 | 11.32 |

Average nrtPS packet satisfaction ratio versus the number of users.

Simulation results demonstrate that our sliding window method increases the system capacity and decreases the BLER effectively compared the minimum and the average channel gain methods. In loaded systems, simulation results show that the proposed algorithm permits to achieve a better trade-off between fairness and efficiency use of resources compared to other recent methods [

In order to reduce the OFDMA system complexity, available sub-carriers are grouped into equal groups of contiguous sub-carriers, where each group is called a subchannel. The adaptive modulation and coding scheme, AMC, is used in order to maximize the number of bit per symbol. In this paper, we have firstly proposed a new method for subchannel gain computation based on the frequency responses dispersion. Secondly, we have proposed a new heuristic method for subchannels allocation problem in the context of WiMAX release 2.0, IEEE 802.16 m. An adaptive method for subchannels allocation was necessary in order to exploit the multi-user diversity, to respect real-time constraints and to maximize the system capacity. The idea of this method was based on the statistic parameters, mean, variance, root mean square, or RMS of the frequency response channel gain for every mobile station. Finally, we proposed a multi-QoS-based resource allocation algorithm for OFDMA systems. We defined a priority function for each user according to the QoS satisfaction degree and its corresponding subchannel qualities. Simulation results showed that proposed algorithms provide a better trade-off between total system capacity, fairness, and complexity compared to other existing methods. For future work, we are interested to validate the present proposition in a multiservice context in order to develop an efficient radio resources management policy for Long-Term Evolution (LTE) network.