Performance analysis of multihop framed ALOHA systems with virtual antenna arrays
 Chiara Buratti^{1}Email author,
 Alberto Zanella^{2} and
 Roberto Verdone^{1}
https://doi.org/10.1186/168714992013118
© Buratti et al.; licensee Springer. 2013
Received: 19 October 2012
Accepted: 4 April 2013
Published: 1 May 2013
Abstract
We consider a multihop virtual multipleinputmultipleoutput system, which uses the framed ALOHA technique to select the radio resource at each hop. In this scenario, the source, destination and relaying nodes cooperate with neighboring devices to exploit spatial diversity by means of the concept of virtual antenna array. We investigate both the optimum number of slots per frame in the slotted structure and once the sourcedestination distance is fixed, the impact of the number of hops on the system performance. A comparison with deterministic, centralized reuse strategies is also presented. Outage probability, average throughput, and energy efficiency are the metrics used to evaluate the performance. Two approximated mathematical expressions are given for the outage probability, which represent lower bounds for the exact metric derived in the paper.
Keywords
1 Introduction
Multipleinput multipleoutput (MIMO) systems are well known for their capability to obtain high spectral efficiency in the presence of fading channels [1, 2]. On the other hand, the need to install multiple antennas in portable devices can be problematic for economic and practical reasons. To extend the advantages of MIMO systems to devices characterized by a reduced number of antennas, the idea of deploying a virtual (or distributed) (VMIMO) architecture appears to be very promising [3–5]. Cooperation among nodes is a fundamental aspect of VMIMO systems, since nodes cooperate to create virtual antenna arrays (VAAs) [4]. VMIMO systems take also advantage from the use of nodes equipped with two radio transceivers: a longrange wireless system, which can be used for interVAA communication, and a shortrange radio interface, which can be used for intraVAA communication [3, 4, 6].
Virtual MIMO systems can be applied in the context of both centralized and decentralized networks. The performance of virtual MIMO system applied to a cellularbased architecture in the presence of a limited amount of feedback information is evaluated in terms of throughputfairness tradeoff [7]. The impact of channel estimation errors on the performance of relayassisted virtual MIMO systems is discussed in [8].
The case of multihop systems is of particular interest, as VMIMO techniques can improve significantly the performance in terms of throughput compared to conventional singleinput, singleoutput or SISO schemes [9–19]. In this contest, distributed radio resource assignment techniques, being much simpler, are to be preferred to centralized schemes. The topic of resource allocation, when deterministic (and centralized) multiple access control (MAC) techniques are used for interVAA communication, is studied in [3, 4, 6, 18]. The overall resource pool, available for interVAA communication, is assumed to be fractionated into orthogonal radio resources for interVAA links in [3, 4, 18], and the performance of the considered VMIMO scheme is investigated. As far as distributed radio resource assignment techniques are considered, the impact of nonorthogonal reuse of radio resources in a multihop VMIMO system with a fixed number of relays has been investigated in [6]. In that paper, the presence of interference caused by nonorthogonal reuse of resources has been quantified, and its effect on the sourcedestination performance has been studied.
It is well known that MIMO technology can improve the performance of conventional randomaccess schemes in terms of reliability and throughput. In particular, the use of MIMO detection techniques to combine signals sampled at successive time slots can reduce (or eliminate) the effect of collisions. This important result has motivated the research of network diversity multipleaccess (NDMA) schemes that can obtain a good performance when the channel coherent time is in the order of the packet slot duration [20, 21]. Among the random access schemes, ALOHA has been recently proposed for MIMO systems in [22–24]. More precisely, a scenario composed of an access point (AP), and multiple transmitting nodes is considered in [22]. The key idea of [22] is that nodes use framed ALOHA (FA), but the possible multiple transmissions over the same slot are not treated as collisions. In the event of collision, some nodes are selected to act as relays and retransmit the signal they received during the collision in a specific time slot. The different signals received by the AP are then processed using standard detection strategies (i.e., using maximum likelihood techniques). To alleviate the performance degradation which occurs in NDMA schemes when coherent time is larger than the packet slot duration, an adaptive spacetime diversity framed ALOHA scheme, which works with MIMO systems with two transmit and an arbitrary number of receive antennas, is proposed in [23]. Multiplexing/diversity MIMO tradeoff is considered in the context of wireless local area networks performance optimization with MIMO channels [24]. The paper proposes a joint optimization of MAC layer parameters and the multiplexingdiversity tradeoff at the physical layer. Two different MAC protocols are studied: carrier sense multiple access with collision avoidance (CSMA/CA) and slotted ALOHA. CSMA is also used in [25] for distributed scheduling in multihop networks with MIMO links. An adaptive CSMA protocol, which adjusts the link rate based on local queue information, is also proposed in [25].
To the authors’ knowledge, there are no papers in the literature addressing the performance of multihop VMIMO systems where random accessbased MAC schemes are used for interVAA communication. Motivated by this consideration, a decentralized MAC scheme is considered in this paper. In particular, the resources are assigned to each hop independently, through a framed ALOHA procedure: the time axis is split into frames, each composed of a given number of slots. Assuming perfect synchronization among nodes, each VAA in the multihop chain selects one slot randomly, resulting in possible interference among VAAs selecting the same slot. It is well known that this approach provides worse performance than the case of a centralized procedure implementing a perfect deterministic reuse of the time slots. On the other hand, implementation of framed ALOHA is much simpler than that of a centralized algorithm since resources are independently selected by nodes at each hop. Centralized assignment of the resources, in fact, requires the exchange of many control packets among nodes, and it is not applicable in many cases, as for example wireless sensor networks [26, 27].
Network performance is evaluated in terms of ergodic capacity, outage probability, average throughput, and energy efficiency. The model presented in this paper is based on the works presented in [28, 29] and it represents a generalization and integration of those contained in the two conference papers.
The contribution of this paper can be summarized as follows:

We showed the behavior of the outage probability by varying different system parameters, and compare framed ALOHA with a deterministic centralized assignment of radio resources.

We identified the optimum number of slots per frame in the slotted structure of ALOHA;

We evaluated the impact of the number of hops on the performance when the sourcedestination distance and the other system parameters are set.

We discussed the tradeoff between sourcedestination throughput and overall energy spent by nodes.

We provided some upper bounds on the outage probability. Numerical results will show that these bounds are rather tight.
The remainder of the paper is organized as follows. Section 2 describes the channel model considered, the reference scenario, the framed ALOHA protocol, and the communication within the multihop VAAbased network. Section 3 provides the mathematical model to derive the ergodic capacity of the multihop VMIMO system, while Section 4 reports the derivation of the performance metrics: outage probability, average throughput, and energy efficiency. Finally, Sections 5 and 6 report the numerical results and conclusions, respectively.
2 System description and scenario
2.1 Notations
Throughout the paper, vectors and matrices are indicated in bold, I is the identity matrix, [A]^{T} denotes the transpose of A. {a_{i,j}}_{i,j=1,…,M} is an M ×M matrix with elements a_{i,j}={A}_{i,j}. $\mathbf{A}\oplus \mathbf{B}\triangleq \text{diag}(\mathbf{A},\mathbf{B})$ is the direct sum of matrices [30]. $\mathbb{E}\left\{\xb7\right\}$ denotes expectation, and $\mathbb{P}\left\{\mathcal{E}\right\}$ denotes the probability of the event $\mathcal{E}$. u(x) indicates the unit step function, equal to one for x>0 and zero otherwise.
2.2 Propagation and connectivity models
where P_{T} is the transmitted power, k is the average power loss at x=1 m, being x the link distance, β is the attenuation coefficient which commonly ranges from 2 to 5; finally, f is a shortterm random fading component. Rayleigh fading is considered for the interVAA links, so that f is exponentially distributed with unit mean.
where k_{0}=ζ lnk and k_{1}=ζ·β, where $\zeta \triangleq 10/\text{ln}10$.
Note that, for each air interface (intra and interVAA), we could have different values of the transmitted power (P_{T}) and propagation parameters (k_{0}, k_{1}).
The channel is assumed to be ergodic. Under such assumption, the sourcedestination ergodic capacity can be considered as one of the main performance metrics [31].
2.3 Reference scenario and assumptions
In the following, source, relays and destination nodes will be denoted as main nodes. The distance between the source main node and the destination main node will be denoted as D_{SD}, while d will be the distance between the main nodes of two subsequent VAAs in the multihop chain. We assume that adjacent VAAs are equally separated in distance (i.e., d=D_{SD}/Z).
A random number of ancillary nodes is distributed in nonoverlapped arbitrary areas, $\mathcal{A}$, around the main nodes, according to a homogeneous Poisson point process (PPP) [32]. According to such distribution, the probability of having one node in the infinitesimal area $\delta \mathcal{A}$ is $\mathrm{\rho \prime \delta}\mathcal{A}$, where ρ′ denotes the nodes’ density [32]. Owing to the possible presence of mechanisms such as sleeping schedules implemented at nodes, only a subset of the ancillary nodes are simultaneously active; we denote ρ as the density of the active nodes.
As largely adopted in the literature, we assume that nodes are equipped with two air interfaces: a shortrange air interface, which is characterized by a high data rate and low energy consumption and used for the communication within VAA (intraVAA communication), and a longrange air interface, which is characterized by a lower data rate and used for the interVAA communication [3, 4, 6].
The main and ancillary nodes may cooperate to create VAAs at the source (sVAA), at the Z−1 relays (r_{ i }VAA, with i∈{1,..,Z−1}) and at the destination (dVAA). In particular, we assume that an ancillary node could be part of the VAA only if the main nodeancillary node path loss, L, is lower than a given threshold, L_{th}^{a}, representing the maximum loss tolerable by the shortrange communication system. VAAs are formed before each data transmission. We assume that the path loss between main and ancillary node, L, is constant during each interVAA transmission phase. Shortrange wireless interface is used to track possible changes in the propagation characteristics and identify the new set of collaborating nodes. The new set of cooperating nodes will be active during the subsequent transmission phase. Using the propagation model given in (1), the results that ancillary nodes may communicate with the main node only if their distance is lower than ${d}_{\text{th}}={e}^{({L}_{\text{th}}{k}_{0})/{k}_{1}}$ and the mean number of candidate cooperating nodes, denoted as N hereafter, can be written as $\rho \phantom{\rule{0.3em}{0ex}}\pi {d}_{\text{th}}^{2}$. We assume that the distances sourcerelay, relayrelay, and relaydestination are much larger than the distance between a main node and its cooperating nodes. With this assumption, the VAAs do not overlap and each ancillary node can communicate only with a unique main node. Moreover, depending on the shortrange air interface used, there could exists a maximum number of cooperating nodes a main node can actually handle; this maximum number will be denoted as M in the following. Finally, we assume that a decode and forward (DF) strategy is implemented at the relays [4].
2.4 Framed ALOHA
We assume that a framed ALOHA protocol is used by the different VAAs to access the channel [33]; the whole amount of radio resources available for interVAA communication is divided among R unitary resources. In other words, each frame has R time slots. Therefore, once a VAA has data to be transmitted, the main node of the VAA will select randomly one out of the R available time slots in the next frame and will communicate the choice to its cooperating nodes. All nodes of a given VAA (cooperating nodes plus the main node) will simultaneously access the channel by using the same unitary resource, selected with probability 1/R. We also assume an outofband signalling mechanism is used for synchronization between the transmitter VAA and the receiver VAA.
Since time slot selection is made independently at VAA level, there is a nonzero probability that other links use the same time slot, causing interference.
2.5 The communication in the multihop chain
We assume that all the VAAs (sVAA, r_{ i }VAAs (with i∈{1,..,Z−1}) and dVAA) are formed before data transmission. During the phase of VAA creation, each main node selects the slot to the used for the interVAA communication. Large scale effects (i.e., pathloss) are supposed to be slowly varying compared to the data packet length. Under such assumption, the set of cooperating nodes remains constant during the packet transmission.
Using the shortrange radio interface, the main nodes transmit a query to ancillary nodes, and those nodes that receive the query reply with an acknowledgement, becoming candidate cooperating nodes. Then, the main nodes randomly select M nodes, among all the candidates, to cooperate with. Being ancillary nodes distributed according to a PPP, the number of candidate cooperating devices at a given node follows a Poisson distribution. The number of nodes actually cooperating at the source, the i th relay and at the destination, will be denoted as n_{S}, ${n}_{{\mathrm{R}}_{\mathrm{i}}}$, and n_{D}, respectively.
 1.
The source main node will transmit the data, together with the slot to be used, to the n _{S} cooperating nodes, using the shortrange air interface (intraVAA communication).
 2.
The n _{S}+1 nodes of the sVAA transmit data towards the first relay through the VMIMO channel, using the longrange interface (interVAA communication).
 3.
The n _{R} _{1}+1 nodes of the r _{1}VAA cooperate to decode the received data, meaning that the data is send (through the shortrange interface) from the n _{R} _{1} nodes to the main node, which will decode it.
 4.
The main node at the r _{1}VAA sends the data to be forwarded, together with the slot to be used, to the n _{R} _{1} cooperating nodes, which will forward it towards the second relay.
 5.
The previous two steps are repeated until the data reach the last relay.
 6.
The n _{D}+1 nodes of the dVAA receive data from the last relay and cooperate to decode it.
In this paper, we focus on interVAA performance, which uses longrange air interface. Data transmission between main and cooperating nodes can be performed either via broadcast transmission (all nodes receive all data), or using a given multiple access scheme; this aspect, which is related to intraVAA communication, is out of the scope of this paper.
More specifically, the exchange of information between main node and ancillary node for signaling, synchronization and data transmission (intraVAA transmission) uses the shortrange radio interface. This solution also minimizes possible delays since the two transmission phases (intra and interVAA communications) can occur in parallel. For this reason, delays or other possible overhead due to cooperation aspects have not been considered.
3 Analysis of the ergodic capacity for a VMIMO system
3.1 Capacity of a generic link
where ${\sigma}_{\mathrm{N}}^{2}$ is the thermal noise power. Note that the eigenvalues of the matrices Ψ^{(ℓ)} and ${\stackrel{~}{\Psi}}^{\left(\ell \right)}$ have multiplicities larger than one.
We refer to [6] for the derivation of $\mathcal{I}({n}_{\mathrm{T}},{n}_{\mathrm{R}},\mathit{\Theta})$ for the sake of brevity.
3.2 Ergodic capacity between source and destination
where ${\stackrel{\u0304}{C}}_{{n}_{{\text{T}}_{0}}^{\left(\ell \right)},{n}_{{\text{T}}_{I}}^{\left(\ell \right)},{n}_{\mathrm{R}}^{\left(\ell \right)},{\mathbf{P}}^{\left(\ell \right)}}^{\left(\ell \right)}$ is given by (3).
From (6) it can be noted that the parameter R impacts on ${\stackrel{\u0304}{C}}_{\text{S,D}}$ as follows: on one hand the term 1/R increases by getting R smaller, on the other hand a decreasing of R causes more interference (on average), therefore the capacity of the generic link gets smaller. This leads to the existence of an optimum value of R, as shown in the numerical results.
We assume that the contribution of an interfering VAA is significant only if its distance from the receiver VAA is lower than p·d, where p is a suitable integer, which depends on the propagation environment. Therefore, when p=1, only onehop distant interferers are accounted for, whereas in the case p=2, onehop and twohop distant interferers are considered by the model. The latter assumption implies that there exists a maximum number of interfering VAAs on link ℓ, denoted as ${I}_{\text{max}}^{\left(\ell \right)}$.
3.3 Outage probability definition
4 Performance analysis
We now derive the metrics used to assess the performance of our VAAbased Framed ALOHA system: (a) the outage probability, as defined in (7); (b) the average throughput, and (c) the energy efficiency.
4.1 Outage probability
where ${\stackrel{\u0304}{C}}_{\text{S,D}}$ is given by (6) and ${I}_{\text{max}}^{\left(\ell \right)}$ is the maximum number of interfering VAAs at the ℓ th hop.
where $P(i,N)\triangleq \frac{{N}^{i}}{i!}{e}^{N}$ is the Poisson distribution with parameter N.
We define a= [a_{1},a_{2},..,a_{ m }]^{ T }, ${\mathbf{P}}_{\text{I}}^{\left(\ell \right)}=\phantom{\rule{0.3em}{0ex}}{[{P}_{1}^{\left(\ell \right)},{P}_{2}^{\left(\ell \right)},\mathrm{..},{P}_{m}^{\left(\ell \right)}]}^{T}$, and F_{(t)}, with an m ×m matrix having t elements per column equal to ‘1’ and the remaining elements equal to ‘0’, such that the different columns contain all the possible, non repeated, combinations of the positions of the ‘1’. We also denote as f_{j,(t)} the j th column of F_{(t)}, that is, F_{(t)}= [f_{1,(t)}f_{2,(t)}..f_{m,(t)}].
In the case of i=0 (i.e., no interferers), the sum in (15) is equal to $\mathrm{u}\left({C}_{0}\frac{1}{R}{\stackrel{\u0304}{C}}_{{a}_{0},r,\left[{P}_{0}^{\left(\ell \right)}\right]}^{\left(\ell \right)}\right)$, with $\stackrel{~}{\Psi}=\frac{{P}_{0}}{{\sigma}_{\mathrm{N}}^{2}}\phantom{\rule{0.3em}{0ex}}{\mathbf{I}}_{{n}_{{\mathrm{T}}_{0}}}$. In the case of i=m, the sum (15) is equal to $\mathrm{u}\left({C}_{0}\frac{1}{R}{\stackrel{\u0304}{C}}_{{a}_{0},\sum _{i=1}^{\mathrm{m}}{a}_{i},r,[{P}_{0}^{\left(\ell \right)},\mathrm{..},{P}_{\mathrm{m}}^{\left(\ell \right)}]}^{\left(\ell \right)}\right)$.
where ${\widehat{P}}_{\text{out}}$ represents the outage probability of each link. ${\widehat{P}}_{\text{out}}$ can be evaluated using (13) with m=2p−1. The impact of approximations A and B will be evaluated through the numerical results.
4.2 Average throughput
where, obviously, for the outage probability we can use the exact formula, P_{out} (given by (8)), or one of its approximations, ${P}_{\text{out,A}}^{\left(\text{app}\right)}$ or ${P}_{\text{out,B}}^{\left(\text{app}\right)}$. Finally, ${c}_{{n}_{\text{max}}}$ is the maximum value the average capacity may assume.
4.3 Energy efficiency
where $\overline{E}$ is the (average) energy required to transmit a packet from source to destination; T_{ p } is the transmission time of the packet, N is the mean number of cooperating nodes in a VAA (therefore, N+1 is the average size of a VAA).
ξ is related to the energy efficiency of the multihop VMIMO system and represents the ratio between the average throughput obtainable and the energy spent for the transmission of a packet from source to destination through the Z hops. To evaluate $\overline{E}$, we have assumed that the same amount of energy is necessary to transmit or receive a packet (this justifies the term 2 at the denominator of (19)); this assumption appears to be reasonable for many hardware platforms.^{c}
5 Numerical results
Results are obtained by considering, if not otherwise specified, ρ=2·10^{−2}m^{−2}, ${\sigma}_{\mathrm{N}}^{2}=8\xb71{0}^{15}$ W, and M=4. We consider two different channel models for intraVAA and interVAAs communication. In the former case, we set k_{0}=41 dB, k_{1}=13.03 (β=3), and L_{th}=70 dB; while in the case of the interVAA communication channel, we set k_{0}=15 dB and we vary β (k_{1}) for the interVAAs transmission (L_{th} is not fixed in this case, since we assume that the different VAAs are always connected). The signaltonoise ratio (SNR) γ is defined as ${P}_{0}/{\sigma}_{\mathrm{N}}^{2}$, being P_{0} the averaged (over fading) useful receive power, which is given by P_{T}d^{−β}/k.
5.1 Performance of the ℓ th hop
In this section we evaluate the performance of the generic ℓ th hop, in terms of the complementary outage probability, ${P}_{\text{in}}^{\left(\ell \right)}=1{P}_{\text{out}}^{\left(\ell \right)}$, where ${P}_{\text{out}}^{\left(\ell \right)}$ is given by (13), when considering the worst case that is a central link affected by the maximum number of interferers.
5.2 Performance of the whole transmission chain
This section considers the whole transmission chain by investigating the behavior of the following metrics: (a) the exact overall complementary outage probability, P_{in}=1−P_{out}; (b) the approximated overall complementary outage probability,${P}_{\text{in,A}}^{\left(\text{app}\right)}=1{P}_{\text{out,A}}^{\left(\text{app}\right)}$; and (c) the lower bound for the overall complementary outage probability,${P}_{\text{in,B}}^{\left(\text{app}\right)}=1{P}_{\text{out,B}}^{\left(\text{app}\right)}$.
In the following figures, we set β=4, p=2, and${c}_{{n}_{\text{max}}}=10$bit/s/Hz, and we use the approximated formula${P}_{\text{in,A}}^{\left(\text{app}\right)}$to compute T.
6 Conclusions
A framed ALOHA multihop VMIMO system is investigated. Random location of cooperating nodes as well as propagation environment are considered and their effects on the overall performance are evaluated. Several performance metrics are considered to assess the sourcedestination performance: outage probability, which is the probability that the capacity between source and destination is smaller than a given threshold, and energy efficiency. The proposed framework allows us to optimize different system parameters, such as the number of relays to be distributed between source and destination nodes, the number of radio resources, R, and the transmit power. Finally, we would underline that the presented framework could be easily extended to the case of other possible contentionbased protocols, as carrier sense multiple accessbased protocols by properly changing (9) based on the protocol considered.
Endnotes
^{a} As discussed in [6], the use of other models is straightforward.
^{b} Under such assumption, we neglect the correlation existing between the size of the receive VAA at the ℓ th link and the size of the transmit VAA at the (ℓ+1)^{th} link. More specifically, two uncorrelated sets of cooperative nodes are chosen by each VAA for receive (from the previous VAA) and transmit (to the subsequent VAA) purposes.
^{c} Through this definition, we do not account for the energy which is not radiated. Though this component is normally relevant, it would only affect ξ through a proper downscaling. For the purpose of this paper, such downscaling of the absolute value of ξ is not conceptually relevant.
Declarations
Authors’ Affiliations
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