- Research Article
- Open Access
Joint Power Allocation for Multicast Systems with Physical-Layer Network Coding
© Chunguo Li et al. 2010
- Received: 31 December 2009
- Accepted: 7 June 2010
- Published: 27 June 2010
This paper addresses the joint power allocation issue in physical-layer network coding (PLNC) of multicast systems with two sources and two destinations communicating via a large number of distributed relays. By maximizing the achievable system rate, a constrained optimization problem is first formulated to jointly allocate powers for the source and relay terminals. Due to the nonconvex nature of the cost function, an iterative algorithm with guaranteed convergence is developed to solve the joint power allocation problem. As an alternative, an upper bound of the achievable rate is also derived to modify the original cost function in order to obtain a convex optimization solution. This approximation is shown to be asymptotically optimal in the sense of maximizing the achievable rate. It is confirmed through Monte Carlo simulations that the proposed joint power allocation schemes are superior to the existing schemes in terms of achievable rate and cumulative distribution function (CDF).
- Time Slot
- Power Allocation
- Network Code
- Achievable Rate
- Power Coefficient
Network coding (NC) was first proposed for wireline systems to improve the network's achievable rate about ten years ago . It was then shown in [2, 3] that by coding the data stream, rather than separated symbols, the NC can provide a higher achievable rate than the traditional Shannon information theory does. In order to apply the network coding in wireless communication, a lot of investigations have been conducted to improve the network's achievable rate by making wireless channel coefficients similar to the wireline channel ones [4–8]. In [4–6], the idea of network coding is applied in the physical-layer of wireless networks, denoted by physical-layer network coding (PLNC), in order to enlarge the achievable rate. The PLNC is to jointly process multiple symbols without decoding them considering that the aim of the transmission is to tell the symbols to the destination rather than the relay [7–9]. Here, it is the data stream, instead of any separate symbol, that is tackled by NC. As such, NC improves the achievable rate from the data stream perspective.
For the physical-layer network coding, there are basically two types of NC, namely, the amplify-and-forward (AF) NC  and the decode-and-forward (DF) NC . For the AF-NC, the mixed messages received by the relay are first amplified properly without requiring decoding, and then forwarded to the destinations. For the DF-NC, the relay first decodes these mixed messages to obtain all of the individual messages, and then mixes the separated messages in order to forward them to the destinations. In , the AF-NC and DF-NC are compared by both theoretical analysis and simulations, drawing a conclusion that the AF-NC is superior to DF-NC in both transmission rate and link error probability. It is interesting to note that similar results are reported in .
The power allocation issue in physical-layer network coding system attracts a lot of attention since a superior performance could be realized by allocating the power properly. In , a power allocation scheme has been proposed to maximize the capacity of systems with only one single relay. In , an optimization framework has been formulated to allocate the powers for the DF-NC without consideration of AF-NC. A similar power allocation scheme is also proposed for the XOR-based NC . However, the focus of the power allocation schemes is only on the single-cast instead of the multi-cast transmission model. In fact, the multi-cast system is widely used in wireless communications, where the physical-layer network coding can be applied since the nature of broadcast is available for PLNC in the multi-cast model.
As mentioned earlier in this paper, the core of the network coding is to properly combine or mix multiple messages without decoding them separately. Although the performances of network coding are very attractive from the information theory perspective, implementation of these performances incurs very high complexity . In order to decrease the complexity of network coding in practical wireless communication systems, the lattice is introduced to realize the network coding .
The lattice is intrinsically a codebook similar to the traditional constellation modulation. There are basically five aspects to judge whether a lattice is good or not, including the packing feature, the covering feature, the mean squared error due to quantization, the error probability, and the closed property. Namely, a good lattice should be as compact as possible, and should be large enough to cover all of the elements; moreover, the quantization error and the error probability of the transmission in AWGN channels should be as small as possible; furthermore, any linear combination of two elements in the lattice should belong to this lattice [19, 20].
To the authors' best knowledge, most of the existing investigations focus on the multiple-access relay channels or unicast model rather than the multi-cast relay channel. There is only one published paper  that studies the power allocation problem for the multi-cast model with physical-layer network coding , where the system model is composed of two sources, N relays and two destinations. With the novel system model, an isolated precoder and a distributed precoder are designed to achieve the full relay diversity gain. However, power allocation is performed only for the sources while equal power is allocated for all relays. Considering the fact that the network's achievable rate is jointly determined by all of the sources and relays, the performance of the whole system could be further improved if source and relay powers are jointly optimized. In this paper, the joint power allocation issue for both sources and relays will be investigated in order to maximize the network's achievable rate. The difficulty lies in that the joint optimization problem is not convex with respect to (w.r.t.) the whole parameters, leading to a nonconvex optimization problem. The goal of this paper is to develop an iterative algorithm to tackle the nonconvex optimization problem for joint power allocation in the physical-layer network coding. The rest of the paper is organized as follows. Section 2 presents the multi-cast system model and the joint power allocation-based mathematical model. Section 3 first establishes an optimization problem for the joint power allocation based on the criterion of achievable rate maximization. Then, it is disclosed that the proposed problem is not convex w.r.t. the whole parameters. By neglecting the noise power of the second-hop channel under the medium to-high-SNR regimes, and replacing the original cost function with its bound, a convex problem w.r.t. the whole parameter set is obtained such that the convex optimization method can be used to solve the joint power allocation problem. Note that the high-SNR approximation is very accurate due to the very small power of the noise in the second-hop channel. As an alternative, we also design an iterative algorithm to solve the nonconvex problem by using the convex features of the original cost function w.r.t. part of the parameters. In Section 4, the lattice-based network coding that uses the joint power allocation schemes is studied. Section 5 presents the simulation results, with comparison to the existing power allocation schemes, in terms of achievable rate and cumulative distribution function. Section 6 concludes the paper.
denotes the absolute value; means the summation of a vector; means a transpose; is the Frobenius norm; denotes the complex Gaussian distribution with the zero mean and the unit covariance.
where and are the channel coefficients for the k th relay to d1 and d2 on the second subcarrier, respectively. All of the channel coefficients are distributed with . and are the AWGNs at d1 and d2 on the second subcarrier with the distribution of .
Note that this subtraction is called as analogue network coding(ANC) [21, 22]. In (8) and (9), the power coefficients and are optimized in  with a fixed value of the power coefficient . In the subsequent sections, we will jointly optimize all of the power coefficients, , , and to maximize the achievable rate of the whole network. Assume that full channel state information (CSI) of the whole networks is available for every node, which is considered to be feasible in the slow-varying scenario.
In this section, the optimization problem for the joint power allocation in the multi-cast system with the physical-layer network coding is first established using the criterion of achievable rate maximization. As the problem is nonconvex, that is, difficult to solve, it is then modified to obtain a convex power allocation problem by using the high-SNR approximation. As an alternative, an iterative algorithm whose convergence is guaranteed is also developed to optimize the power coefficients of all sources and relays.
3.1. Problem Formulation
and P is the total power available for the whole system over the two subcarriers and the K time slots. It is easy to verify that (13) is not convex with respect to (w.r.t.) the whole parameter ( ). In what follows, we would like to modify the cost function (14) by deriving an upper bound of the network's achievable rate to obtain a convex optimization problem.
3.2. Joint Power Allocation Using an Asymptotically Optimal Upper Bound
By examining the cost function (14), it is found that the nonconvexity only comes from the second and the fourth terms in . If these two terms are modified into convex, would become convex w.r.t. ( ), thus significantly simplifying the joint power allocation problem.
Here, the constrained minimization problem is convex since the Hessian matrix of is always semidefinite positive and the constraint is also convex. Thus, many convex optimization methods such as the interior method and the Lagrangian multiplier method can be employed to solve (17). The complexity of solving (17) is very low since the existing convex optimization techniques are very efficient in computing the convex solution .
3.3. Design of Iterative Algorithm for Joint Optimal Power Allocation
The joint power allocation problem (13) is not convex w.r.t. the whole parameter, yet it could be convex w.r.t. part of the whole parameter set when the other elements are fixed. By calculating the Hessian matrix of , it can be shown that the original cost function (13) is convex w.r.t. with fixed and convex w.r.t. with fixed . Therefore, the nonconvex joint power allocation problem can be solved by first optimizing the relays' power coefficients with fixed , and then optimizing the sources' power coefficients while is fixed. Moreover, each step in solving for or is convex . Therefore, by solving the nonconvex problem iteratively, a solution for ( ) can finally be attained. The iterative algorithm is summarized as follows
Algorithm 1 (iterative optimization of the nonconvex problem (13)).
Initialize and ; set the iteration index and the termination condition ; compute the initial value of the cost function in (13).
Calculate the cost function ; if , terminate the iteration. Otherwise, update and return to Step 2.
indicating that the cost function decreases with each iteration. Meanwhile, the cost function is lower-bounded since the sum rate of the whole system is upper bounded. Consequently, the lower bounded function decreases as iterations go on, ensuring the convergence of Algorithm 1.
By exploiting the convex feature of the nonconvex cost function w.r.t. only a part of the optimization parameters, we have successfully designed an iterative algorithm to solve the nonconvex problem by using a convex optimization method, where the convergence of the iterative algorithm is ensured. Moreover, the solution obtained by Algorithm 1 for the problem (13) is guaranteed to be optimal, yet not necessarily globally optimal due to the nonconvexity of the joint power allocation problem. Namely, the solution realized by Algorithm 1 is at least one local optimum (possibly the global optimum).
It has been shown that the power allocation problem is very important for the lattice-based network coding [25, 26]. In , the feasibility of different powers for two sources has been studied, where two sources want to exchange messages with the help of only one relay. It is also disclosed that the system performances would be enhanced if the sources use different transmit powers. However, there is no optimization of the power allocation for the sources. In , the power allocation is optimized for the underlying system in ; namely, the powers for the two sources are optimized by using an upper bound of the achievable rate. Unfortunately, the power for relay is fixed without any optimization. Thus, the joint power allocation schemes proposed in this paper can be used in the lattice-based network coding, where the power for the source lattice and that for the relay lattice are jointly optimized.
Figure 3 presents the achievable rate as a function of the source power with the fixed relay power dB and . From this figure, we can see that the proposed power allocation scheme using the high-SNR approximation achieves a better achievable rate than the existing one  in the multi-cast system with the lattice-based physical-layer. Moreover, the gain increases with the growing value of SNR.
In this paper, we have studied the joint power allocation problem for multi-cast systems with physical-layer network coding-based on the maximization of the achievable rate. To deal with the nonconvex optimization problem, a high-SNR approximation is employed to modify the original cost function in order to obtain a convex minimization problem, where the approximation is shown to be asymptotically optimal at the high-SNR regime. As an alternative, an iterative algorithm has been developed by utilizing the convexity property of the cost function w.r.t. a part of the whole power coefficients. Considering the low complexity of the physical-layer network coding in the multi-cast system, the lattice-based network coding that uses the proposed joint power allocation schemes has been suggested. The simulation results have shown the effectiveness of the proposed schemes.
This work was partly supported by the National Basic Research Program of China (2007CB310603), NSFC (60672093, 60702029), Important National Science and Technology Specific Project (2009ZX03003-004), and National High Technology Project of China (2007AA01Z262).
- Ahlswede R, Cai N, Li S-YR, Yeung RW: Network information flow. IEEE Transactions on Information Theory 2000, 46(4):1204-1216. 10.1109/18.850663MathSciNetView ArticleMATHGoogle Scholar
- Li S-YR, Yeung RW, Cai N: Linear network coding. IEEE Transactions on Information Theory 2003, 49(2):371-381.MathSciNetView ArticleMATHGoogle Scholar
- Dougherty R, Freiling C, Zeger K: Insufficiency of linear coding in network information flow. IEEE Transactions on Information Theory 2005, 51(8):2745-2759. 10.1109/TIT.2005.851744MathSciNetView ArticleMATHGoogle Scholar
- Zhang S, Liew SC, Lam PP: Physical-layer network coding. In Proceedings of the 12th Annual International Conference on Mobile Computing and Networking (MOBICOM '06), September 2006. ACM; 358-365.View ArticleGoogle Scholar
- Popovski P, Yomo H: Bi-directional amplification of throughput in a wireless multi-hop network. Proceedins of the IEEE 63rd Vehicular Technology Conference (VTC '06), May 2006 588-593.Google Scholar
- Zhang S, Liew SC, Lu L: Physical layer network coding schemes over finite and infinite fields. Proceedings of IEEE Global Telecommunications Conference (Globecom '08), November 2008 1-6.Google Scholar
- Katti S, Gollakota S, Katabi D: Embracing wireless interference: analog network coding. Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM '07), August 2007 397-408.Google Scholar
- Zhang S, Liew S-C: Channel coding and decoding in a relay system operated with physical-layer network coding. IEEE Journal on Selected Areas in Communications 2009, 27(5):788-796.View ArticleGoogle Scholar
- Li X, Jiang T, Zhang Q, Wang L: Binary linear multicast network coding on acyclic networks: principles and applications in wireless communication networks. IEEE Journal on Selected Areas in Communications 2009, 27(5):738-748.View ArticleGoogle Scholar
- Popovski P, Yomo H: Wireless network coding by amplify-and-forward for bi-directional traffic flows. IEEE Communications Letters 2007, 11(1):16-18.View ArticleGoogle Scholar
- Katti S, Rahul H, Hu W, Katabi D, Médard M, Crowcroft J: XORs in the air: practical wireless network coding. Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM '06), September 2006 243-254.Google Scholar
- Riemensberger M, Sagduyu YE, Honig ML, Utschick W: Comparison of analog and digital relay methods with network coding for wireless multicast. Proceedings of the IEEE International Conference on Communications (ICC '09), June 2009Google Scholar
- Lo ES, Letaief KB: Network coding versus superposition coding for two-way wireless communication. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '09), April 2009Google Scholar
- Shin W, Lee N, Lim JB, Shin C: An optimal transmit power allocation for the two-way relay channel using physical-layer network coding. Proceedings of the IEEE International Conference on Communications Workshops (ICC '09), June 2009Google Scholar
- Xu H, Li B: XOR-assisted cooperative diversity in OFDMA wireless networks: optimization framework and approximation algorithms. In Proceedings of the 28th Conference on Computer Communications (INFOCOM '09), April 2009. IEEE; 2141-2149.Google Scholar
- Lin Z, Vucetic B: Power and rate adaptation for wireless network coding with opportunistic scheduling. Proceedings of the IEEE International Symposium on Information Theory (ISIT '08), July 2008 21-25.Google Scholar
- Zamir R: Lattices are everywhere. Proceedings of the Information Theory and Applications Workshop (ITA '09), February 2009 392-421.Google Scholar
- Erez U, Litsyn S, Zamir R: Lattices which are good for (almost) everything. IEEE Transactions on Information Theory 2005, 51(10):3401-3416. 10.1109/TIT.2005.855591MathSciNetView ArticleMATHGoogle Scholar
- Nazer B, Gastpar M: Compute-and-forward: harnessing interference with structured codes. Proceedings of the IEEE International Symposium on Information Theory (ISIT '08), July 2008 772-776.Google Scholar
- Nazer B, Gastpar M: Compute-and-forward: harnessing interference with structured codes. http://arxiv.org/pdf/0908.2119
- Li J, Chen W: Joint power allocation and precoding for network coding-based cooperative multicast systems. IEEE Signal Processing Letters 2008, 15: 817-820.View ArticleGoogle Scholar
- Zhang R, Liang Y-C, Chai CC, Cui S: Optimal beamforming for two-way multi-antenna relay channel with analogue network coding. IEEE Journal on Selected Areas in Communications 2009, 27(5):699-712.View ArticleGoogle Scholar
- Tang X, Hua Y: Optimal design of non-regenerative MIMO wireless relays. IEEE Transactions on Wireless Communications 2007, 6(4):1398-1406.View ArticleGoogle Scholar
- Boyd S, Vandenberghe L: Convex Optimization. Cambridge University Press, Cambridge, UK; 2004.View ArticleMATHGoogle Scholar
- Baik I-J, Chung S-Y: Network coding for two-way relay channels using lattices. Proceedings of the IEEE International Conference on Communications (ICC '08), May 2008 3898-3902.Google Scholar
- Wilson MP, Narayanan K: Power allocation strategies and lattice based coding schemes for bi-directional relaying. Proceedings of the IEEE International Symposium on Information Theory (ISIT '09), July 2009 344-348.Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.