 Research
 Open Access
Link adaptation for energyefficient uplink coordinated multipoint receptions
 YoungHan Nam^{1}Email author,
 Lingjia Liu^{1, 2},
 Guowang Miao^{1} and
 Charlie Jianzhong Zhang^{1}
https://doi.org/10.1186/168714992011184
© Nam et al; licensee Springer. 2011
 Received: 29 June 2011
 Accepted: 23 November 2011
 Published: 23 November 2011
Abstract
We investigate link adaptation methods for energyefficient uplink coordinated multipoint receptions. A system model for practical cellular networks is introduced, in which only a subset of base stations participates in cooperative link adaptation and cooperative decoding for uplink transmissions. To cope with channelstateinformation (CSI) mismatch incurred from the system model, link adaptation controllers implementing rate backoff from the maximum achievable rate calculated with the mismatched CSI is introduced. From analytical and simulation results, it is concluded that under a certain condition, the rate backoff does not help to improve energy efficiency, where, for example, the condition holds when the CSI errors are modeled as additive Gaussian random variables. Furthermore, energy efficiency of multiuser spatialdivisionmultipleaccess uplink transmissions is studied in isolated cooperative cellular networks. In this scenario, an analytical expression for the optimal link adaptation achieving maximum energy efficiency is obtained.
Keywords
 coordinated multipoint receptions
 CoMP
 energy efficiency
1 Introduction
Spectral efficiency and energy efficiency are important metrics for wireless communication systems. While contemporary wireless telecommunications standards, e.g., LTEAdvanced mainly focuses on enhancing spectral efficiency (e.g., [1]), there is growing interest in improving energy efficiency, partly because the development of battery technology has not kept in pace with the demand of mobile communications [2, 3]. Energyefficient communications also tend to reduce electromagnetic interference and lessen environmental impacts, for example, heat dissipation and electronic pollution. Therefore, recent research starts to focus on energyefficient communication techniques [4–9].
Coordinated multipoint (CoMP) transmissions/reception is one example scheme studied in LTEAdvanced [1] primary targeting on increasing celledge user equipment's (UE's) spectral efficiency. In CoMP transmissions, multiple base stations coordinate their transmissions so that served UEs receive data streams with higher downlink (DL) spectral efficiency. In CoMP receptions, multiple base stations coordinate reception/decoding of packets from UEs to achieve higher uplink (UL) spectral efficiency.
While there have been lots of researches on throughput improvement of CoMP (for UL CoMP refer to [10] and references therein; for DL CoMP refer to [11, 12] and references therein), to the authors' best knowledge, little efforts have been put so far on energyefficient CoMP communications. In this article, we investigate energyefficient link adaptation for UL CoMP communications in cellular networks. For this purpose, we define an energyefficiency metric, in a unit of nats/Joule,^{1} by extending the energyefficiency metric introduced in [9]. The energyefficiency metric accounts for both transmit power and circuit power. The transmit power models all the power used for reliable data transmission. On the other hand, the circuit power represents the average power consumption of device electronics, e.g. filters, mixers, and digitaltoanalog converters, and this portion of power consumption excludes that of the power amplifier and is independent of the transmission state. The newly introduced metric can measure energy efficiency of both the singlecell operations and the CoMP operations, despite fundamental differences of available channel state information (CSI).
In the first part of the article, we investigate the energy efficiency of a CoMP reception scheme of a single UE's uplink transmission in a cellular network, where only a subset of base stations participate in the cooperative decoding, and the other UEs' signals intended to the other base stations in the network may interfere with the single UE's uplink signals. In this scenario, the CSI experienced during the actual transmission may be different from the CSI used for link adaptation, owing to uncoordinated interference from the other UEs.
To cope with the CSI mismatch, we consider a link adaptation controller implementing a rate backoff from the maximum achievable rate calculated with the mismatched CSI. Then, we analyze the energy efficiency of the link adaptation controller and we show that if a certain condition is satisfied, then the link adaptation controller may still rely on the mismatched CSI in order to achieve the maximum energy efficiency.
In the second part of the article, we analyze the optimal energy efficiency of a CoMP reception scheme of multiple UEs' uplink transmission in a cellular network, where all the base stations participate in the cooperative decoding. In this interferencefree scenario, we assume that perfect CSI is available at the link adaptation controller and analyzes conditions for achieving the optimal energy efficiency. In particular, we provide analytical expressions for the optimal power allocation for a twoUE twobasestation system.
The notations used in this article are summarized as in the following. Italic characters, e.g., K, h, are used for representing scalar variables. Boldface lowercase Roman characters, e.g., h, are used for representing vectors, and boldface uppercase Roman characters, e.g., H, are used for representing matrices. Boldface Italic lowercase characters, e.g., h, are used for representing either random variables or random vectors, while boldface Italic uppercase characters, e.g., H are used for representing random matrices. A^{ H }denotes the Hermitian transpose of matrix A, and h* denotes complex conjugate of a complex scalar h. h denotes the absolute value of a complex scalar h, and h denotes the L2 norm of a complex vector h. ℂ denotes the set of complex numbers.
2 System model
Under the system model considered in this section, we consider both singleuser and multiuser transmission scenarios with uplink CoMP receptions. In Section 3, we consider energyefficient uplink CoMP receptions of a single user in a partially cooperating cellular wireless network, in which only a subset of base stations performs cooperation. For singleuser transmissions, the further refined system model in Section 3 is general enough to reflect some important aspects of the reallife cellular networks, and at the same time, it is possible to obtain some analytical results. However, for multiuser multicell link adaptations, the system model of Section 3 is difficult to analyze. To obtain some insights of multiuser transmission scenarios aided by CoMP reception, we make further simplifying assumption of fully cooperating cellular networks in Section 4.
3 Energyefficient uplink transmission schemes with K= 1 and $M\ll \stackrel{\u0304}{M}$
3.1 Definition of energy efficiency with imperfect CSI
Now, once a distribution of the random variable h characterizing h is given, the optimal energy efficiency of the network U^{⋆} can be defined as ${U}^{*}\triangleq E\left({U}_{h}^{*}\right)$.
However, as the channel states used for the link adaptation is not necessarily the same as those for the receiver, we consider a general framework for taking potential imperfectness of CSI at the link adaptation controller and extend the energy efficiency definition accordingly. For the uplink transmission considered in this section, we make the following assumptions.

(Assumption 1) The actual channel vector of the network during the transmission of a packet is h ∈ ℂ^{M× 1}, and the m th component of h, denoted by h_{ m }, is the channel coefficient between BS m and the UE. The received signals at each BS are corrupted by circularly symmetric additive white Gaussian noise (AWGN) of zero mean and unit variance. We note that this assumption models a receiver treating interference signals from the other UEs transmitting to the other base stations as noise and scaling the received signal, so that the interferenceplusnoise power is one.

(Assumption 2) The receiver is aware of the actual channel vector h.

(Assumption 3) The link adaptation controller determines the transmission rate R and the transmission power P_{ T }based on CSI $\stackrel{\u0303}{h}=hw$, where w∈ ℂ^{M× 1}is a random vector characterizing the CSI error and models the uncoordinated interference from other UEs in the cellular network. The link adaptation controller is aware ^{2} of the distribution of w.

(Assumption 4) To cope with channel outages, the link adaptation controller applies a power backoff strategy to determine the transmission rate. The controller assumes that the transmission power is αP_{ T }where α ∈ (0,1] for the rate calculation, even though the actual transmission power is P_{ T }. In this case, the transmission rate R is determined as $R=R\left({P}_{T},h,\alpha \right)=ln\left(1+\alpha {P}_{T}{\u2225\stackrel{\u0303}{h}\u2225}^{2}\right)$.
Once a distribution of the random vector $\stackrel{\u0303}{h}$ characterizing $\stackrel{\u0303}{h}=h+w$ is given as well as the distribution of w, the optimal energy efficiency ${\u0168}^{*}$ of the network can be defined as ${\u0168}^{*}\triangleq E\left({\u0168}_{\stackrel{\u0303}{h}}^{*}\right)$.
3.2 With perfect CSI at the link adaptation controller
In this subsection, we consider the uplink transmission with perfect CSI at the link adaptation controller. To model this case, in addition to the four assumptions in Section 3.1, we further assume that the CSI error vector w is deterministically 0 so that $\stackrel{\u0303}{h}=h$, and that the link adaptation controller chooses α = 1, regardless of the CSI h. In this case, the maximization problem of (6) reduces to (2) and has been analyzed in [9], as in the following theorem.
We note that efficient algorithms are available to solve the power maximization (7), e.g., from [9].
3.3 With imperfect CSI at the link adaptation controller
We obtain the following theorem on this joint maximization.
Proof. See Appendix A
Theorem 2 states that when a condition of ${P}_{T}^{\left(1\right)}\le A$ holds, the link adaptation controller can maximize the expected energy efficiency by choosing ${P}_{T}={P}_{T}^{\left(1\right)}$ and $R=ln\left(1+{P}_{T}^{\left(1\right)}{\u2225\stackrel{\u0303}{h}\u2225}^{2}\right)$ with treating $\stackrel{\u0303}{h}$ as the actual CSI, where ${P}_{T}^{\left(1\right)}$ can be efficiently found by algorithms introduced in [9]. To see when the condition of ${P}_{T}^{\left(1\right)}\le A$ holds, we need to take a closer look at A, which is a parameter determined dependent on the distribution of w.
In general, given a distribution of $\stackrel{\u0303}{h}$, which is a random vector characterizing $\stackrel{\u0303}{h}$, the maximum expected energy efficiency ${\u0168}^{*}=E\left({\u0168}_{\stackrel{\u0303}{h}}^{*}\right)$ increases as we increase the M number of base stations participating in the cooperative link adaptation and decoding, because of the increased transmission rates and smaller outage probability for a given transmission rate thanks to the diversity reception of the coordinated multipoint reception.
4 Energyefficient uplink transmission schemes with K≥ 1 and $\stackrel{\u0304}{M}=M$
To get some insights of the possible advantages of employing uplink multiuser transmissions in a CoMP setting, we consider a relaxed setting of an isolated cellular network of M cooperating base stations in this section. In the isolated network, the uplink transmissions do not suffer from flashlight effect, and hence both link adaptation controller and the receiver have perfect knowledge of the actual channel states. To model this case, we make the following assumptions:

(Assumption 1) The actual channel matrix of the network during the transmission of a packet is H ∈ ℂ^{M×K}, and the (m, k) component of H, denoted by h_{ mk }, is the channel coefficient between BS m and UE k, where m ∈ {1,..., M} and k ∈ {1,..., K}. The received signals at each BS are corrupted by circularly symmetric additive white Gaussian noise (AWGN) of zero mean and unit variance.

(Assumption 2) The receiver is aware of the actual channel matrix H.

(Assumption 3) The link adaptation controller determines the K UE's transmission powers {P_{T,1},..., P_{T,K}} and rates {R_{1}, ..., R_{ K }} based on the perfect CSI H, such that the maximum sum rate is achieved with the selected powers when either successive interference cancellation (SIC) or joint maximumlikelihood (ML) decoding is used at the central controller.
When the distribution of a random matrix H characterizing H is given, we further define the optimal energy efficiency of the network as ${U}^{*}=E\left({U}_{H}^{*}\right)$.
When K = 1, the optimal link adaptation parameters ${P}_{T,1}^{*}$ and ${R}_{1}^{*}$ can be found as in Theorem 1. On the other hand, when K ≥ 1, we can prove the following lemma for the optimization problem (19).
Lemma 3. U_{ H }(p) defined in (18) is strictly quasiconcave in p.
Proof. As log det $\left(I+{\sum}_{k}{P}_{T,k}{h}_{k}{h}_{k}^{\mathsf{\text{H}}}\right)$ is concave in p, it can be straightforwardly verified that (18) is strictly quasiconcave in p.
If a strictly quasiconcave function has a local maximum, then the local maximum is global maximum. Relying on this property of U_{ H }(p), we prove the following.
or ${U}_{H}\left(p\right)={h}_{1}^{\mathsf{\text{H}}}{\left(I+{\sum}_{k}{P}_{T,k}{h}_{k}{h}_{k}^{\mathsf{\text{H}}}\right)}^{1}{h}_{1}$. Similarly, taking partial derivatives of U_{ H }(p) on p_{T,2}, ..., p_{ T,K }, we obtain the system of equations giving the optimal power allocation.
This relation implies that the link adaptation controller should assign more power to the user with better channel, which gives us a similar intuition as the waterfilling power control for parallel channels [13].
When substituting (30) to (19), the joint optimization (19) over two variables P_{T,1}and P_{T,2}is simplified into an easier optimization over a single variable P_{T,1}.
5 Conclusions
In this article, we investigated the energy efficiency of CoMP link adaptations and CoMP reception schemes of uplink transmission in a cellular network. To model typical implementation of CoMP in the cellular network, we first considered a scenario where only a subset of base stations participate in the cooperative link adaptation and cooperative decoding for a single UE, in which case, the other UEs' signals intended to the other base stations in the network interfere with the single UE's uplink signals, and hence the CSI experienced during the actual transmission is different from the CSI used for link adaptation. To cope with the CSI mismatch, we consider a link adaptation controller implementing a rate backoff from the maximum achievable rate calculated with the mismatched CSI. According to the analysis, we found that the maximum energy efficiency of the link adaptation controller is achieved when no rate backoff is employed, when a certain condition is satisfied. We also showed by simulation that the condition holds when the CSI errors are modeled as additive Gaussian random variables. Furthermore, in order to see benefits of multiuser uplink transmissions for energy efficiency, we analyzed the optimal energy efficiency of a link adaptation method and a CoMP reception scheme of multiple UEs' uplink transmissions in a cellular network, where all the base stations participate in the cooperative decoding. In this interferencefree or perfectCSI scenario, we obtained analytical expressions for the optimal power control with arbitrary numbers of UEs and base stations. The optimal power allocation gives similar intuition as waterfilling in parallel channels, that is, we need to assign more power to a betterquality channel, to achieve the maximum energy efficiency We also provided a simulation result showing that twouser spatialdivisionmultipleaccess transmissions achieves larger maximum energy efficiency than singleuser transmissions in the same cellular network.
Appendix A
Proof of Theorem 2
Conflicting interests
The authors declare that they have no competing interests.
^{1}For better mathematical representations and for convenience of analysis, we use "nats" instead of "bits," for the unit of information. 1 nats = log_{2}e bits.
^{2}This assumption can be justified as in the following. As mentioned earlier in this paper, because of the flashlight effect, the channel state used for demodulation, h and the one reported to the link adaptation controller, $\stackrel{\u0303}{h}$, are not necessarily the same. However, the link adaptation controller can obtain h from the receiver once the demodulation is finished. Therefore the link adaptation controller can determine the distribution of w.
Declarations
Authors’ Affiliations
References
 3GPP TR 36814, Evolved Universal Terrestrial Radio Access (EUTRA); Further advancements for EUTRA Physical layer aspects: Physical channels and modulation v1.0.0 2009.Google Scholar
 Lahiri K, Raghunathan A, Dey S, Panigrahi D: Batterydriven system design: a new frontier in low power design. In Proc Intl Conf on VLSI Design. Bangalore, India; 2002:261267.Google Scholar
 Miao GW, Himayat N, Y Li, Swami A: Crosslayer optimization for energyefficient wireless communications: a survey. 2009, 9(4):529542.Google Scholar
 Verdu S: Spectral efficiency in the wideband regime. IEEE Trans Inf Theory 2002, 48(6):13191343. 10.1109/TIT.2002.1003824MathSciNetView ArticleGoogle Scholar
 Meshkati F, Poor HV, Schwartz SC, Mandayam NB: An energyefficient approach to power control and receiver design in wireless networks. IEEE Trans Commun 2006, 5(1):33063315.Google Scholar
 Cui S, Goldsmith AJ, Bahai A: Energyconstrained modulation optimization. IEEE Trans Wirel Commun 2005, 4(5):23492360.View ArticleGoogle Scholar
 Miao G, Himayat N, Y Li, Bormann D: Energyefficient design in wireless OFDMA. In Proc IEEE Conf Commun. ICC'; 2008. 2008Google Scholar
 Miao GW, Himayat N, GY Li, Talwar S: Lowcomplexity energyefficient OFDMA. In Proc IEEE Conf Commun. ICC'; 2009:15. 2009Google Scholar
 Miao G, Himayat N, Li Y: Energyefficient link adaptation in frequencyselective channels. IEEE Trans Commun 2010, 58(2):545554.View ArticleGoogle Scholar
 Marsch P, Fettweis G: Uplink comp under a constrained backhaul and imperfect channel knowledge. IEEE Trans Wirel Commun 2010. (Submitted)Google Scholar
 Liu L, Nam Y, Zhang J: Proportional fair scheduling for multicell multiuser mimo systems. In Proc CISS. Princeton, NJ; 2010.Google Scholar
 Liu L, Zhang J, Yu JC, Lee J: Intercell Interference Coordination through Limited Feedback, Int. J Digit Multimedia Broadcasting 2010. Article ID 134919, 7 pages, 2010Google Scholar
 Cover TM, Thomas JA: Elements of Information Theory. Wiely; 1991.View ArticleGoogle Scholar
 Nam Y, Gopala PK, El Gamal H: Resolving collisions via incremental redundancy: Arq diversity. In Proc INFOCOM. Anchorage, AK; 2007.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.