- Open Access
Interference alignment for spectral coexistence of heterogeneous networks
© Sharma et al.; licensee Springer. 2013
- Received: 29 June 2012
- Accepted: 31 January 2013
- Published: 22 February 2013
The coexistence of heterogeneous networks within the same spectrum for enhancing the spectrum efficiency has attracted large interest lately in the research community. Furthermore, the research interest towards the deployment of small cells and multibeam satellites is increasing due to high capacity, easier deployment and higher energy efficiency. However, due to the scarcity of available spectrum and the requirement of additional spectrum for these systems, small cells need to coexist with macrocells and multibeam satellites need to coexist with monobeam satellites within the same spectrum. In this context, this contribution investigates an underlay spectral coexistence mechanism which exploits an interference alignment (IA) technique in order to mitigate the interference of cognitive transmitters towards the primary receivers in a normal uplink mode. More specifically, three types of IA techniques, namely static, uncoordinated and coordinated are investigated. The performance of the IA technique is evaluated and compared with primary only, resource division and no-mitigation techniques in terms of sum-rate capacity, primary to secondary rate ratio and primary rate protection ratio. It is shown that the coordinated IA technique perfectly protects the primary rate in both terrestrial and satellite coexistence scenarios.
- Channel State Information
- Multiple Input Multiple Output
- Primary System
- Primary Receiver
- Secondary System
Due to the limited and expensive spectrum resource, cognitive radio communication can be an efficient technique to enhance the spectrum efficiency in the context of coexistence of heterogeneous networks. Heterogeneous networks may exist within the same spectrum band in different ways such as two terrestrial networks or two satellite networks. In the terrestrial paradigm, the coexistence of small cells and macrocells can be considered within the same spectrum while in the satellite paradigm the focus is on the coexistence of monobeam and multibeam satellite systems. Heterogeneous networks in this article refer to small/macro cell terrestrial and mono/multibeam satellite systems. In the context of terrestrial paradigm, the macrocell system can be considered as primary and a small cell system as secondary system. Similarly, in the context of dual satellite coexistence scenarios, a monobeam system can be considered as primary and a multibeam system as secondary. In both scenarios, the interference from the secondary system to the primary system should be suppressed while the secondary system must tolerate the interference from the primary system.
Due to the advancements in terrestrial cellular technology and satellite multibeam technology, denser deployments of cells/beams has become possible for providing higher capacity and network availability. Small cell systems provide higher cellular capacity and a large number of small cells is in general more energy efficient than macrocells since there is more flexibility of operating unused small cells in sleep mode due to their smaller coverage area . Similarly, in satellite systems, multiple beams can be employed instead of a single global beam in order to enhance the capacity . A geostationary satellite can be equipped with multibeam antennas to cover the multiple spots over the surface of the Earth. However, current network configurations use macrocell/monobeam systems and the deployment of new small cells/multibeam systems need additional bandwidth which is scarce and expensive to acquire. In this context, dense cellular networks (small cells) have to coexist with traditional macrocells and multibeam satellites have to coexist with the traditional monobeam satellites to utilize the existing spectrum optimally. This need has led to the concept of cognitive radio communications which allows for the coexistence of two systems, primary and secondary, over the same spectrum. The most common cognitive techniques in the literature can be categorized into spectrum sensing (SS) or interweave, underlay, overlay and database techniques . In SS only techniques [4, 5], secondary users (SUs) are allowed to transmit whenever primary users (PUs) do not use that specific band, whereas in underlay techniques, SUs are allowed to transmit as long as they meet the interference constraint of the PUs.
The coexistence of heterogeneous networks in the same spectrum band can be modeled as cognitive radio networks with interference channels between primary and secondary systems. The operation of the primary network usually follows a well established standard and should not be degraded while the secondary network should employ advanced communication techniques to exploit the underutilized dimensions in the signal space. When the strength of secondary interference to the primary is comparable to the desired signal, treating as noise is not an option because of interference constraints while decoding and canceling requires complex primary receivers. In this context, interference alignment (IA) as an interference mitigation tool has received important attention recently in the cognitive radio research community [6, 7]. The concept behind IA is that signals can be designed in such a way that they cast overlapping shadows at the receivers where they constitute interference and remain distinguishable at the receivers where they are desired. In this direction, this study investigates an underlay spectral coexistence mechanism which exploits uplink interference alignment in order to mitigate the interference of small cell user terminals (UTs) towards the macrocell base station (BS) or the interference of multibeam satellite terminals towards the monobeam satellite. The proposed IA technique is compared to a passive transmission technique which allows for cochannel interference, as well as to a resource splitting approach which would require altering the spectrum regulations. Furthermore, the performance of different IA techniques is evaluated in terms of ergodic sum-rate capacity, primary to secondary rate ratio and primary rate protection ratio.
The remainder of this article is structured as follows: Section 2 reviews in detail prior study in the areas of multicell/multibeam joint decoding and the IA technique. Section 3 describes the considered system models for terrestrial and satellite paradigms. Section 4 describes the considered signal model and channel model. Section 5 provides the capacity expressions and presents the proposed IA technique. Section 6 provides the considered performance metrics and evaluates the effect of various parameters on the system performance. Section 7 concludes the article.
Throughout this article, denotes the expectation, (·) † denotes the conjugate transpose matrix, (·) T denotes the transpose matrix, ⊙ denotes the Hadamard product and ⊗ denotes the Kronecker product, I n denotes a n × n identity matrix, denotes a n × m matrix of ones, and 0 represents a zero matrix.
We consider two different system models in terrestrial and satellite paradigms. Although these two systems have different characteristics and channel models, they can be studied using the same input-output equations as described in the signal model section. Furthermore, both systems operate in a normal uplink mode with the primary system as a single-user uplink and the secondary system as a multiuser uplink.
3.1 Macrocell and small cells
3.2 Monobeam and multibeam satellites
We consider a single ST1, N number of ST2s served by N beams of SAT2. Multibeam joint processing is considered at the gateway of SAT2 to decode the received signals from ST2s jointly. Since a single gateway is responsible for processing the transmitted and received signals corresponding to a large geographic area, the application of joint processing techniques in the satellite context is centralized. After scheduling, we consider that one ST1 and N number of ST2s are transmitting simultaneously in a single slot over a common spectrum band. In this context, we consider spatial multiplexing for the primary monobeam system and we employ multiple dimensions (carriers) in the secondary multibeam system to align interference with the reference vector.
Furthermore, we consider that all the satellite terminals use multicarrier transmission scheme and the IA is employed at the ST2s over L = M+1 carriers, affected by Adjacent Carrier Interference (ACI). In this context, we consider a narrowband frequency division multiple access (FDMA) system which can be applicable for L/S band mobile satellite systems. We consider that M number of symbols are transmitted by ST1 and 1 symbol per ST2 is transmitted by spreading across all the carriers. Furthermore, it should be noted that ST1 sends M symbols over M subcarriers whereas each ST2 sends 1 symbol over L subcarriers. To suppress the interference caused by ST2s using IA technique, CSI towards the SAT1 is required and we assume that this CSI can be acquired at the ST2s by listening to the pilot signals broadcasted from the gateway. In this context, we assume time division duplex (TDD) mode of operation and for a satellite system with frequency division duplex (FDD) mode of operation, an alternative way of acquiring CSI should be investigated since uplink CSI can not be derived from the downlink pilots in FDD mode. As an example, for satellite scenarios where FDD is used, the uplink CSI can be derived from uplink pilots and then can be fed back with the help of a gateway.
In the considered terrestrial system model, small cell UTs are secondary transmitters (STs), small cell APs are Secondary Receivers (SRs), a macro UT is a primary transmitter (PT) and a macro BS is a primary receiver (PR). Similarly, in the satellite system model, the monobeam satellite SAT1 is the PR, the feeders of multibeam satellite SAT2 are the SRs, the multibeam satellite terminals ST2s are the STs and the monobeam satellite terminal ST1 is the PT.
In addition to CSI, STs and the PR should be aware of predefined IA vector v to perform IA. Depending on how v is calculated, we consider three different IA techniques: static, coordinated, and uncoordinated in our analysis. These techniques depend on the level of coordination between primary and secondary systems. The concept behind cognitive interference alignment is to employ precoding at the STs so that the received secondary signals at the PR are all aligned across the alignment vector v. In this way, interference can be filtered out by sacrificing one degree of freedom and some part of the desired received energy. For this purpose, the PT utilizes only M out of L dofs and reserves one dof which is devoted to IA filtering. However, after filtering the signal is interference free and can be easily decoded using conventional detection techniques. The term cognitive comes from the fact that the STs have to be aware of the CSI and the vector v to perform the precoding. On the other hand, the PR needs only to perform filtering adapted to vector v and no additional awareness or intelligence is required. The only difference between considered satellite and terrestrial models is that in the terrestrial scenario, IA is over the spatial dimensions and in the satellite scenario, IA is over the subcarriers. A common signal model can be used for both cases with different channel models as described in the following section.
where y1 is the L × 1 received symbol vector, x is the M × 1 transmitted symbol vector from the PT, x i is the L × 1 transmitted symbol vector from the i th ST and z1 is the receiver noise. All inputs x,x i are assumed to be Gaussian and obey the following sum power constraints: and , γ p s being the transmit SNRa of the PT and γ] s s being the transmit SNR of the ST. The L × M matrix H represents the channel gains between the PR and the PT while the L × L matrix F i represents the channel gains between the PR and i th ST.
where y2 is the N L × 1 received symbol vector and z2 is the receiver noise. The N L × M channel matrix represents the channel gains between all SRs and the PT while the N L × L channel matrix represents the channel gains between all SRs and the i th ST. To simplify notations, we group all into a single N L × N L matrix .
4.1 Channel model for terrestrial coexistence
with B = [β1 … β N ] and is a N L × N L random matrix with i.i.d. c.c.s. elements.
4.2 Channel model for satellite coexistence
In this scenario, we consider a spectral coexistence network of multibeam and monobeam satellite systems with interference channels between them. Each transmitter/receiver node consists of a single antenna and uses multicarrier transmission so that the channels can be represented as diagonal matrices, where the diagonal entries correspond to the different sub-channels. The multicarrier model considered in this scenario differs from MIMO (spatial) channel matrix with full entries as considered in the terrestrial scenario.
where each block S i j follows the similar structure as H.
where B(m,k) represents the beam gain of k th beam for m th terminal position, u(m,k) = 2.01723 sin(θ(m,k))/ sin(θ3dB), J i is the first kind of Bessel’s function of order i, G T is the terminal antenna gain, FL is the free space path loss for the satellite link, Gmax is the maximum satellite antenna gain, θ3dB is the 3 dB angle and θ (m,k) represents the nadir angle to m th terminal position from k th beam center position with respect to the satellite position.
5.1 Capacity expressions
5.1.1 Primary only
where I L is the identity matrix of dimension L and γ p s represents the SNR at the transmit antenna of the primary system.
In both cases, the second term represents the cochannel interference.
5.1.3 Interference alignment
where is the equivalent channel matrix after IA filtering.
where is the equivalent channel matrix including precoding.
5.2 Interference alignment and filtering
where are the equivalent N L × 1 channel matrices including precoding. To simplify notations we group all into a single N L × N matrix .
5.3 Alignment direction selection and filter design
In this section, we investigate various approaches for selecting the alignment direction v and designing the corresponding filter Q. Since these two operations are interdependent, they have to be jointly studied taking into account the level of coordination between the primary and secondary systems.
5.3.1 Static approach
In this approach, the alignment direction is predefined and does not depend on the channel state. It can be seen that this is quite static but also simple solution which assumes no coordination in the network. The disadvantage lies in the fact the IA direction may be aligned with one of the strong eigenvectors of the random PR-PT channel and in this case a large amount of received power will be filtered out.
5.3.2 Coordinated approach
Now let H H † = U ∧ U † be the eigenvalue decomposition of H H † and λ (H H † ) = [0 λ1 … λ M ] are the M ordered eigenvalues. The eigenvectors define an orthonormal space of the MIMO/multicarrier sub-channels. In this direction, the optimal strategy is to select the eigenvector which corresponds to the zero eigenvalue as the alignment direction.
From Equations ( 23) and ( 28), it can be observed that the throughput for primary only technique is a function of eigenvalues of H H ‡ and the throughput for coordinated IA technique is a function of eigenvalues of. The objective here is to show that both H H † andhave the same non-zero eigenvalues.
wherecontains the non-zero eigenvalues of H H † . Hence,and H H † have identical eigenvalues in this approach and this completes the proof.
Due to the fact that we reserve one degree of freedom for interference alignment, the coordinated IA technique perfectly preserves the primary rate. Optimally there can be L data streams at primary transmitter and if we use all L degree of freedoms for signal transmission, there exists no zero eigenvalue and in that case, even the coordinated approach will have small gap as compared to the primary only technique.
5.3.3 Uncoordinated approach
The solution of (43) is hard to tackle analytically. A heuristic solution for this problem would be to select the eigenvector corresponding to the largest eigenvalue of the equivalent channel covariance matrix . The matrix T can be decomposed using the eigenvalue decomposition as: T = U † ∧ U with U = [u1, u2, …, u M ] and ∧ being a diagonal matrix with the eigenvalues in descending order. Therefore, one simple heuristic solution is to choose the eigenvector corresponding to the largest eigenvalue, i.e., v = u1 and to design a truncated unitary matrix Q so that the condition Q v = 0 is satisfied.
In this section, we present a number of numerical results in order to provide a comparative evaluation of the proposed technique. We consider two different simulation environments in satellite and terrestrial coexistence scenarios.
6.1 Performance metrics
6.2 Results and discussion
6.2.1 Terrestrial coexistence
Parameters for capacity results in terrestrial paradigm
Number of small cells
Macro UT antennas
Small UT, BS, AP antennas
R p s
Small cell radius
R s s
Macro UT transmit power
P p s
Small UT transmit power
P s s
Path loss exponent
Number of Monte Carlo iterations
6.2.2 Satellite coexistence
Parameters for capacity results in satellite paradigm
Number of ST2s
Number of carriers used by ST1
Number of symbols transmitted by ST1
Number of carriers used by ST2
Number of symbols transmitted by each ST2
R p s
R s s
ST1 transmit power
P p s
ST2 transmit power
P s s
Receiver noise power @ 5 MHz
Monobeam 3 dB beamwidth
B W p s
Multibeam 3 dB beamwidth
B W s s
Intercarrier interference component
Free space path loss
Max satellite antenna gain
Terminal antenna gain
Number of Monte Carlo iterations
The spectral coexistence of heterogenous networks in terrestrial and satellite paradigms has been investigated. The coexistence of small cells and macrocells equipped with MIMO transceivers in terrestrial paradigm and the coexistence of multibeam and monobeam satellites with multicarrier transceivers in satellite paradigm have been considered. The primary only case has been compared to the coexistence scenarios with no mitigation, resource division and different IA techniques. More specifically, three types of IA have been considered depending on the level of network coordination and on whether it adapts to channel conditions. The different techniques have been compared in terms of system rate, primary to secondary rate ratio and primary rate protection rate. From the results, it can be deduced that the coordinated IA perfectly protects the primary rate. Although no mitigation achieves the highest sum-rate in terrestrial coexistence scenario, the primary protection rate degrades with the number of small cells. From the viewpoint of protecting primary rate as well as achieving the highest rate, the coordinated IA technique is the best among all other techniques in satellite coexistence scenario.
aIn our signal model, we consider transmit SNR as the ratio of transmitted power to the noise variance.
bThe main reason for ACI is due to imperfect bandpass filters since we consider a narrowband FDMA system in this study.
cSince we consider transmission using M out of L carriers, excluding the last column from H provides similar structure as P i .
dIt is also possible to have resource division in time domain in this context.
This study was supported by the National Research Fund, Luxembourg under Aids Training-Research (AFR) grant for PhD project (Reference 3069102) on “Spectrum Sensing, Resource Allocation and Resource Management Strategies for Satellite Cognitive Communications”, under the CORE project “CO2SAT: Cooperative and Cognitive Architectures for Satellite Networks” and by COST Action IC0902: “Cognitive Radio and Networking for Cooperative Coexistence of Heterogeneous Wireless Networks”.
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