NeighborsAware Proportional Fair scheduling for future wireless networks with mixed MAC protocols
 Charles Jumaa Katila^{1}Email authorView ORCID ID profile,
 Chiara Buratti^{1},
 Melchiorre Danilo Abrignani^{1} and
 Roberto Verdone^{1}
https://doi.org/10.1186/s1363801708756
© The Author(s) 2017
Received: 28 October 2016
Accepted: 5 May 2017
Published: 22 May 2017
Abstract
In this paper, we consider a beyond5G scenario, where two types of users, denoted as scheduled and uncoordinated nodes, coexist on the same set of radio resources for sending data to a base station. Scheduled nodes rely solely on a centralized scheduler within the base station for the assignment of resources, while uncoordinated nodes use an unslotted Carrier Sense Multiple Access (CSMA) protocol for channel access. We propose and evaluate through simulations: (a) a novel centralized resource scheduling algorithm, called NeighborsAware Proportional Fair (NPF) and (b) a novel packet length adaptation algorithm, called ChannelAware (CA) Packet Length Adaptation algorithm for the scheduled nodes. The NPF algorithm considers the uplink channel state conditions and the number of uncoordinated nodes neighboring each scheduled node in the aggregate scheduling metric, in order to maximize packet transmission success probability. The CA algorithm provides an additional degree of freedom for improving the performance, thanks to the fact that scheduled nodes with lower number of hidden terminals, i.e., having higher packet capture probability, are assigned longer packet transmission opportunities. We consider two benchmark schemes: Proportional Fair (PF) algorithm, as a resource scheduling algorithm, and a discrete uniform distribution (DUD) scheme for packet lengths distribution. Simulation results show that the proposed schemes can result in significant gain in terms of network goodput, without compromising fairness, with respect to two benchmark solutions taken from the literature.
Keywords
1 Background
In future wireless systems, such as 5G and beyond, the current dominating humancentric communication systems will be complemented by a tremendous increase in the number of smart devices, i.e., things, equipped with radio devices, possibly sensors, and uniquely addressable [1]. This will result in explosion of wireless traffic volume [2] and consequently exponential growth in demand of radio spectrum. However, the radio spectrum resource will remain limited, and thus, efficient radio resource utilization techniques, such as advanced medium access control (MAC) schemes, will be of paramount importance.
In wireless networks, MAC protocols are classified into two main groups: contentionbased and contentionfree. Contentionbased protocols are distributed in nature and suffer from packet collisions. Nodes whose packets collide could perform a random backoff before attempting to access the channel again for retransmission of lost packets. Such protocols include ALOHA [4], slotted ALOHA [5], and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) family of protocols [6]. On the other hand, contentionfree protocols are mainly coordinated in nature involving a centralized master entity which allocates orthogonal or nonorthogonal resources to network users, using some policies defined by the scheduling algorithm. Radio resources assigned to users can either be in time, frequency, space, code, or combination of more than one resource dimension. The conventional scheduling algorithms include RoundRobin (RR), Earliest Deadline First (EDF) [7], Maximum Throughput (MT), and Proportional Fair (PF) [8]. Each scheduling algorithm aims at maximizing/minimizing some network performance metrics such as fairness, sum throughput, power consumption, latency, etc., subject to some constraints.
Furthermore, we propose a novel ChannelAware (CA) Packet Length Adaptation algorithm for the scheduled nodes, which serves as an additional degree of freedom for improving network performance. The CA algorithm is based on the following observation: there exists a circular area centered at the BS and having radius equal to the half of the sensing range (see the gray area in Fig. 2), where the uncoordinated nodes can sense any scheduled transmission from the area and refrain from accessing the channel. This area is denoted hereafter as Hidden Neighbor Free (HNF), and nodes within it are assigned an opportunity to transmit longer packets, because they can experience lower interference. Therefore, CA algorithm logically partitions the scheduled nodes into two sets: those which suffer from the hidden terminal problem (i.e., nodes outside the HNF region), using a discrete uniform distribution (DUD) scheme for packets lengths and nodes not having hidden terminals (i.e., nodes in the HNF region) allowed to transmit longer packets.
This paper is an extension of our previous work reported in [9]. As main differences w.r.t. [9], we underline the following: (1) we propose here a novel packet length adaptation algorithm, i.e., CA algorithm not included in [9]; (2) we made some modifications to the resource scheduling algorithm, NPF; and (3) we consider here more different performance metrics, evaluating in details NPF with different packet length adaptation schemes, and different system parameters.

We study a new problem where scheduled nodes coexist on the same pool of radio resources with uncoordinated nodes.

We propose a novel centralized resource scheduling algorithm, called NPF, which takes into account the relative channel quality metric, and the relative neighborhood metric accounting for the presence of uncoordinated nodes in the cell.

We propose a novel packet length adaptation algorithm, called CA algorithm.
Performance of NPF and CA are evaluated via simulations and compared with benchmark solutions, based on Proportional Fair and discrete uniform distribution scheme for packets lengths. The impact of CSMA parameters (e.g., Clear Channel Assessment (CCA) threshold and backoff exponent (BE)) on the protocols, is also evaluated.
The rest of the paper is organized as follows: Section 2 discusses related literature, Section 3 describes the system model, Section 4 describes the benchmark and the proposed scheduling algorithms, Section 5 describes the packet length adaptation schemes, Section 6 reports numerical results and, finally, Section 7 provides the conclusion.
2 Related works
2.1 Coexistence of heterogeneous MAC schemes
In the past, a lot of research studies have been done on the coexistence of heterogeneous access schemes. However, this topic continues to attract increasing interests from the scientific and industrial communities because of its promising potential in IoT and beyond5G networks. Authors in [10] classify the coexistence schemes into two main classes: mediated and autonomous coexistence. In mediated class, there is a network entity which serves as mediator between the coexisting networks to facilitate fair coexistence and mitigate collisions. The mediator also helps to ensure tight synchronization and coordination of the involved networks. This type of coexistence is applicable when two or more timedivision multiplexing (TDM) systems share a common channel. On the other hand, autonomous coexistence does not need any coordination of the involved networks. In this class, we have TDM vs. CSMA and CSMA vs. CSMA networks. In our paper, we are concerned with TDM vs. CSMA coexistence.
The 3rd Generation Partnership Project (3GPP) proposes two main mechanisms for ensuring fair coexistence of LTE with WiFi and other technologies in the unlicensed spectrum: Listen Before Talk (LBT) and Carrier Sensing and Adaptive Transmission (CSAT) [11]. LBT is a random access scheme based on carrier sensing and backoff rules similar to WiFi. On the other hand, CSAT is a TDMbased scheduled scheme which is used by LTEU small cell when no clear channel is found after longterm carrier sensing. The scheme defines a time (duty) cycle where the BS transmits in a fraction of the cycle and remains silent in the remaining duration. Therefore, when CSAT is used, TDMbased nodes (LTE network) coexist with random accessbased nodes (WiFi or other technologies) on the same channel. The scope of these works is different from our work because (1) CSAT addresses coexistence in downlink LTEU small cell, while our work is focused on an uplink scenario which is more challenging due to effects of hidden terminal problem and especially when the network has to cope with high traffic demands and (2) our work focuses on the design of a scheduling algorithm for the scheduled nodes, while in 3GPP standards, details of scheduling algorithms are not provided because it is a vendorspecific problem [12]. Furthermore, the existing traditional scheduling algorithms for radio networks may not be appropriate to be applied directly in the scenario, since they were never designed with coexistence problem in mind.
Another approach widely studied in literature to address spectrum sharing and coexistence is the cognitive radio [13]. With cognitive radio network (CRN), nodes are classified into two categories: primary (licensed) users and secondary (cognitive) users. The secondary users opportunistically access underutilized spectrum licensed to incumbent systems [14]. Authors in [10] study the problem of heterogeneous coexistence between TDM and CSMA networks in TV White Space (TVWS) spectrum and address hidden terminal problem with a beacon transmission mechanism. Centralized scheduling in cognitive radio networks is studied in [15, 16]. Our work is different because in our scenario, neither the scheduled nor the uncoordinated nodes rely on cognitive radio principles to enhance coexistence.
The IEEE 802.15.4 MAC provides an option for a TDMA mode which operates without carrier sensing. Authors in [17] study the problem of coexistence between ZigBee with TDMA MAC and WiFi. The work proposes a new paradigm which relies on busy tone signals to enhance the mutual observability between ZigBee and WiFi in order to improve on coexistence. The TimeSlotted Channel Hopping (TSCH) protocol proposed in IEEE802.15.4e standard is expected to coexist with random access schemes used by other technologies in the unlicensed bands [18]. TSCHbased devices can mitigate interference and fading through channel hopping technique. Traffic Aware Scheduling algorithm for reliable lowpower multihop IEEE 802.15.4e networks is studied in [19]. The authors propose a new scheme based on graph theory method of matching and coloring. The probabilistic TDMA (PTDMA) [20] and ZMAC [21] protocols are hybrid access schemes which combine features of both TDMA and CSMA. They are designed with the flexibility to switch between CSMA and TDMA based on the state of contention in the network. According to these schemes, each node in the network is assigned a time slot to transmit but it could capture the channel on any other slot after performing CSMA procedure. Similarly, authors in [22] propose a hybrid MAC protocol for heterogeneous MachinetoMachine (M2M) networks which combine features of contentionbased and TDMA schemes. A spectrumaware clusterbased energyefficient routing hybrid scheme is discussed in [23]. In this scheme, the TDMA and CSMA operate on different channels.
In summary, to the best knowledge of the authors, the NPF is the first uplink centralised algorithm to address the problem of scheduling competing scheduled users accounting for the effects of the uncoordinated users in the aggregate scheduling metric.
2.2 Packet length adaptation
The literature on packet length adaptation schemes is extensive, especially in 802.11 WLANs [24, 25]. However, very few works exploit packet size adaptation in a heterogeneous coexistence of scheduled and uncoordinated users on the same channel to enhance the performance of one or both of the user groups involved. Furthermore, most of the existing works account only for channel errors which occur due to the timevarying SNR on links and neglect the effects of hidden terminals. In [26, 27], authors propose a lossbased packet length adaptation algorithm for IEEE.802.11 WLANs with hidden terminals operating in a timevarying wireless channel. The authors have shown that accounting for the effect of hidden terminals in the packet loss models can improve the throughput significantly. In [28], authors consider IEEE.802.11b WLAN under interference from IEEE 802.15.4 network. The authors demonstrate that packet length optimization can result in improved throughput in the presence of interference. Dynamic packet size optimization and channel selection for cognitive radio sensor networks is studied in [29]. Finally, [30] studies frame aggregation schemes in 802.11n WLAN with channel errors and proposes optimal frame size adaptation algorithm.
In our work, we propose a new packet length adaptation scheme, denoted as CA, for the scheduled nodes which takes advantage of the capture effect phenomenon to improve on goodput of the scheduled nodes. According to the operation of the algorithm, scheduled nodes within the HNF region are assigned the maximum possible packet size, while those outside the HNF region are assigned packet lengths according to discrete uniform distribution of packet sizes with a goal of maximizing goodput by minimizing collision losses. Therefore, with this scheme, in each slot, the fraction of time for exclusive transmission by uncoordinated nodes is affected and, in fact, it varies depending on which scheduled node is transmitting.
3 The system model
3.1 Reference scenario and radio resources
We consider an uplink scenario in a single square cell of side S, consisting of K scheduled nodes, M uncoordinated nodes, and a single BS placed in the center of the cell. All nodes are randomly and uniformly distributed within the cell as shown in Fig. 2.
3.2 Traffic model
All nodes generate packets according to a Poisson arrival process with arrival rate λ [bytes/frame]. Regarding the packet length, uncoordinated nodes transmit equal length packets of L bytes, while scheduled nodes are allowed to transmit packet sizes determined according to the packet length adaptation schemes described in Section 5.
3.3 Channel and packet capture models
where k _{0} is the pathloss at 1 m given by \(20\log _{10}\frac {4\pi }{\lambda }\), where λ is the wavelength and k _{1}=10·η, being η the propagation pathloss exponent dependent on the environment, and d(i,BS) is the distance between user i and the base station. In linear scale, γ _{ i } is an exponentially distributed component with unit mean, accounting for Rayleigh fading effect on the link.
where P _{n} is the noise power. The PHY layer issues happens with probability p _{e}.
2) If no PHY layer issues are present, then we check if SIR≥α for the entire duration of packet transmission, where α is the protection ratio (also denoted as capture threshold) and SIR is the signaltointerference ratio metric.
Finally, we assume that an uncoordinated node i is a neighbor of a scheduled node j if i can “hear” transmissions of j, that is, P _{R}≥CCA_{thr}, where CCA_{thr} is the Clear Channel Assessment (CCA) threshold. Let \(\mathcal {N}_{j_{n}}=\{1,2,...,n_{j}\}\) denote the subset of all uncoordinated nodes neighboring j. The properties of \(\mathcal {N}_{j_{n}}\), i.e., cardinality of the subset and its elements change according to the coherence time of the channel because of Rayleigh fading effect on links. Therefore, \(\mathcal {N}_{j_{n}}\) has a minimum and a maximum cardinality of 0 and M, respectively.
3.4 The CSMA/CA protocol
Default simulation parameters
Parameter  Value 

β  0.1 
M  50 nodes 
K  100 nodes 
Packet length  50 subslots 
L _{max}  200 subslots 
L _{min}  10 subslots 
S  1000 m 
Fade margin (γ _{ f })  5 dB 
Bit rate  1 Mbps 
SIR threshold (α)  3 dB 
BS height  20 m 
NB _{max}  10 
CCA threshold (CCA _{ thr })  −85 dBm 
CCA duration  8 subslots 
Contention window (CW)  31 subslots 
1 subslot  80μ s 
BE  5 
b  0.1/M 
Channel coherence time  10 slots 
4 Scheduling algorithms
We first describe the benchmark protocol considered in this paper, that is Proportional Fair, and then we report our proposed solution.
4.1 Benchmark algorithm: Proportional Fair
where 0≤β≤1 and 1/β is the averaging time window [31]. By changing, β the scheduler can trade off between the throughput of the system and temporal fairness among the users. In this paper, R _{ j } is computed according to the normalized Shannon capacity formula as log2(1+SNR).
4.2 Proposed algorithm: NeighborsAware Proportional Fair
where M is the total number of uncoordinated nodes deployed in the cell and b is an arbitrarily small positive constant.
5 Packet length distribution schemes
We first describe the benchmark packet length distribution considered in this paper, and then we report our proposed solution.
5.1 Benchmark scheme: discrete uniform distribution
where L _{min} and L _{max} are the minimum and maximum possible packet sizes in bytes supported, respectively. The difference between two consecutive packet sizes in the ordered set is a fixed constant Δ L which is set to L _{min} in the rest of this paper.
5.2 Proposed scheme: ChannelAware Packet Length Adaptation
In collisionprone CSMAbased wireless networks with hidden terminals, packet length adaptation schemes can play an important role in mitigating the effects of collisions. Losses of long packets due to collisions can result in significant waste of network radio spectrum and energy [32]. The probability of packet collisions and hence losses due to the presence of hidden terminals increases with packet size. This can be attributed to the fact that when the packet length increases, the set of hidden terminals for a given node has to remain silent for a longer time in order to avoid collisions. Similarly, as the number of hidden terminals increases, the chance of collision and losses increases due to the increased average number of transmissions from the set of hidden terminals. In such a condition, small packets transmission are favorable, but if not carefully optimized based on the wireless channel conditions, it can result in low network throughput and channel under utilization.
where k _{0} and k _{1} assume the same meaning as in Eq. (2).
and P _{n} is the noise power in dBm. Therefore, if the link quality of a given node j is high such that SNR_{ j }≥ξ, the node is considered to belong to the HNF area regardless of its physical location in the cell. Within the HNF area, almost all uncoordinated nodes in the region can sense any ongoing scheduled transmission within the region and refrain from accessing the channel. Moreover, the packet capture success probability of the scheduled nodes in the region is very high even in the presence of concurrent uncoordinated transmission(s) from outside the HNF area. The CA algorithm runs within the BS to determine appropriate packet lengths for the scheduled nodes: nodes in the HNF area are assigned maximum allowable packet length, L _{max}, while those outside the HNF area are assigned packet lengths randomly and uniformly distributed between L _{min} and L _{max}, according to the DUD algorithm.
6 Results and discussion
6.1 Simulator and parameters
A C++ simulator implementing the algorithms and the system model described above has been used. We simulate a square cell of length 1 km, a single BS placed at the center of the cell, and variable number of scheduled and uncoordinated nodes which are randomly and uniformly distributed in the cell. We assume that all nodes have omnidirectional antennas. We consider a single frequency channel partitioned into time frames with each frame consisting of 10 equal slots. Each slot is further subdivided into 200 equal subslots of 80 μ s duration. Within each subslot, only 10 B of traffic can be transmitted. The pathloss is computed as given by the pathloss model in Eq. (2) with k _{0} and k _{1} set to 40.7 dB and 30, respectively. For both types of nodes, the traffic arrival rate is set to 500 B per frame. Uncoordinated nodes always transmit packets of fixed length set to 50 subslots (i.e., 500 B), while scheduled nodes transmit either fixedlength or variablelength packets depending on whether the packet adaptation algorithm is used or not. The resource scheduling algorithm runs at the beginning of each new frame. Each scheduled node requesting for resources is assigned a maximum of one slot per frame. The parameters of the CSMA/CA protocol and other default parameters used in this simulation are summarized in Table 1. A single simulation consists of 1000 frames. Results are averaged over 10 different simulation scenarios, characterized by different nodes’ positions in the area.
6.2 Performance metrics
 1.Jain Index (JI), given as$$ \mathrm{Jain\: Index} =\bigg(\sum_{j=1}^{K} x_{j}\bigg)^{2} / \bigg(K\sum_{j=1}^{K} x_{j}^{2}\bigg) $$(15)
where x _{ j } is the average number of radio resource units, i.e., slots, allocated to user j within an interval of 1000 frames.
 2.Packet delivery rate (PDR) is given by:$$ \text{PDR}=\mathrm{\frac{n{o}\: of\: successful\: packets}{no\: of\: transmitted\: packets}}*100 $$(16)
 3.Blocking rate (BR): if we let U _{ A } be the number of unsuccessful channel access attempts and T _{ A } be the total number of channel access attempts, where a channel access attempt is unsuccessful if a node fails to capture the channel after reaching maximum allowable retries, then BR is then given by:$$ \text{BR}=\frac{U_{A}}{T_{A}}*100 $$(17)
BR estimates the level of inhibition in the access to the channel suffered by CSMAbased nodes.
 4.Network goodput (NG) is given by:$$ \text{NG}=\frac{\mathrm{correctly\: received\: bits\: in} \: N\: \text{frames}}{\mathrm{time\: duration\: for}\: N\: \text{frames}} $$
where at the numerator, we have the sum of the number of bits correctly received at the BS when transmitted by the K scheduled nodes (NG for scheduled nodes) or by the M uncoordinated nodes (NG for uncoordinated nodes).
 5.Channel Utilization Index (CUI) is given by:$$ \text{CUI}=\frac{\mathrm{Aggregate \:Goodput}}{\mathrm{Bit\: Rate}} $$
where aggregate goodput is the total network goodput (i.e., sum of the network goodput of scheduled nodes and uncoordinated nodes).
6.3 Results with fixedlength packets
In this subsection, we compare results obtained for the NPF algorithm and the benchmark algorithm, Proportional Fair, obtained by setting ρ=0 in the NPF algorithm. Packet length adaptation scheme is not applied; therefore, both the scheduled and the uncoordinated nodes transmit packets of fixed length.
6.4 Results with CA and DUD schemes
In this subsection, we provide results obtained when applying CA and DUD packet length adaptation schemes together with the NPF resource allocation algorithm.
With reference to the comparison between CA and DUD packet length algorithms, in Fig. 9, we can see that CA always improves the goodput for scheduled nodes because of the effect of transmitting longer packets. On the other hand, CA worsens performance for uncoordinated nodes, since it gives more priority in the access to the channel (i.e., longer packets) to scheduled nodes. As shown in Fig. 10, in fact, the blocking rate for uncoordinated nodes increases when using CA.
Regarding the impact of the CCA_{thr}, in Fig. 12, it is shown that the channel utilization increase by decreasing the CCA threshold, since scheduled nodes may “hear” more uncoordinated nodes. This results again in increasing the blocking rate for uncoordinated node, shown in Fig. 10.
Finally, note that by properly setting the value of ρ, NPF allows the improvement also of fairness w.r.t. the PF algorithm.
7 Conclusions
This paper presents a novel centralized scheduling algorithm for resource assignment for a scenario where scheduled nodes coexist on the same pool of radio resources with uncoordinated nodes. Through simulations, we have demonstrated that the NPF algorithm outperforms the benchmark algorithm, that is proportional fair, in terms of network goodput, packet delivery rate, and channel utilization, without compromising fairness. Moreover, we have proposed a ChannelAware Packet Length Adaptation algorithm, which allows to further improve the network goodput when compared to the discrete uniform distribution packet length selection scheme. Finally, we have shown the effect of different CSMA parameters, as the backoff exponent and the CCA threshold. Results show that the performance improvement of NPF algorithm in terms of Jain Index and channel utilization, compared to PF algorithm, increases with decreasing CCA_{thr}. In conclusion, by properly setting ρ, NPF with CA can achieve, with respect to PF with DUD, a gain of 800% in terms of network goodput for scheduled nodes, 133% in terms of channel utilization, and 50% in terms of Jain Index.
Declarations
Funding
This work was funded by the Department of Electrical, Electronics and Information Engineering, University of Bologna in Italy.
Authors’ contributions
All authors contributed to the work. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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