- Open Access
Uplink interference protection and scheduling for energy efficient OFDMA networks
© Burchardt et al; licensee Springer. 2012
- Received: 14 September 2011
- Accepted: 28 May 2012
- Published: 28 May 2012
One of the key challenges for future orthogonal frequency division multiple access-based networks is inter-cell interference coordination. With full frequency reuse and small inter-site distances, coping with co-channel interference (CCI) in such networks has become increasingly important. In this article, an uplink interference protection (ULIP) technique to combat CCI is introduced and investigated. The level of uplink interference originating from neighbouring cells (affecting co-channel mobile stations (MSs) in the cell of interest) can be effectively controlled by reducing the transmit power of the interfering MSs. This is done based on the target signal-to-noise-plus-interference ratio (SINR) and tolerable interference of the vulnerable link. Bands are prioritised in order to differentiate those (vulnerable/victim) MSs that are to be protected from interference and those (aggressor/interfering MSs) that are required to sacrifice transmission power to facilitate the protection. Furthermore, MSs are scheduled such that those users with poorer transmission conditions receive the highest interference protection, thus balancing the areal SINR distribution and creating a fairer allocation of the available resources. In addition to interference protection, the individual power reductions also serve to decrease the total system uplink power, resulting in a greener system. It is shown through analytic derivation that the introduction of ULIP guarantees an increase in energy efficiency for all MSs, with the added benefit that gains in overall system throughput are also achievable. Extensive system level simulations validate these findings.
- inter-cell interference coordination
- uplink interference protection
- OFDMA networks
- fair scheduling
In wireless networks, there is an increasing demand for higher user and system throughput, along with growing expectation for all mobile stations (MSs) in a cell to be capable of supporting data-heavy multimedia and Internet services. This is especially difficult to maintain at the cell-edge, where received signal and service clearly deteriorate. Furthermore, the necessity for more energy efficient, or "green," technologies is growing. With base stations (BSs) requiring up to 1.5 kW, a typical wide area network can consume tens of MW per annum . In the uplink, while MSs do not consume nearly as much power, there are orders of magnitude more MSs then BSs in the network . In addition with traffic loads increasing approximately ten times every 5 years, a doubling of the energy consumption results over the same time period. Clearly, such an increase raises serious environmental concerns. Consequently, smaller cell sizes, femto-cell deployment, relays [3, 4] and especially inter-cell interference coordination (ICIC) techniques are envisioned for future wireless networks to improve user throughputs and network energy efficiency, while sacrificing minimal system capacity.
For future wireless networks, such a reduction in cell size is undertaken due to transmit power limitations and constraints on the link budget . The demand for higher data rates coupled with full frequency reuse results in an interference-limited system, which cannot achieve full capacity without the implementation of one or more viable interference mitigation/cancellation/coordination techniques . Furthermore, through the implementation of orthogonal frequency division multiple access (OFDMA) in the downlink and single carrier frequency division multiple access (SC-FDMA) in the uplink as multiple access schemes, future systems will provide orthogonality between resource blocks (RBs) in both directions, and hence also between all users within a cell . Thus, system performance is mainly limited by interference originating from users in neighbouring cells, which can be detrimental to the signal-to-noise-plus-interference ratio (SINR) and throughput performance of MSs using the same RBs . A typical solution is to force interferers to leave those RBs idle. However, this severely harms the trunking efficiency of the network . Hence, suppressing transmission is clearly suboptimal, and thus interference coordination techniques are necessary to achieve desired sum and individual throughputs.
For OFDMA systems, some traditional ICIC techniques, such as power control, interference cancellation, fractional frequency reuse, multiple-input multiple-output transmission and space division multiple access , have been proposed. Some of these strategies, however, require knowledge about the position of a MS relative to it's own and neighbouring BSs , which clearly increases the signalling burden in the network. In , other specific ICIC techniques are suggested, such as slow power control, frequency division multiplexing resource allocation, and coordination by MS alignment, though management of interference from other cells is not considered. Further research in  presents a distributed uplink power allocation technique based on a maximum sum rate optimisation, yielding superior results in terms of average system throughput, however ignoring the tradeoff between cell-edge performance and overall spectral efficiency. In , a softer frequency reuse scheme is introduced, where cell-edge power masks are used to mitigate inter-cell interference. These fixed masks cannot, however, adapt to the service-dependent requirements of the neighbouring cells, potentially wasting bandwidth. In , the downlink scheduling is formulated as an optimisation problem, and a decomposition of the problem is performed. Here, however, co-channel interference (CCI) (in future systems from neighbouring cells) is not taken into account, and hence the scheduling becomes suboptimal for multiple access channels and large networks.
In , a dynamic channel acquisition algorithm based on convex optimisation for the wireless downlink is considered, which provides optimal power and throughput performance for i.i.d. channels. This optimality suffers however for general ergodic channels, and hence is not suitable for mobile environments. In , the authors propose a low-complexity algorithm with fairness consideration to optimise the sum rate under individual rate and power constraints. Here though, because the water-filling solution is used for rate-optimal power allocation, a fair power distribution is neglected. In , an optimisation-based heuristic inter-cell coordination scheme is proposed to regulate the uplink transmission in neighbouring cells such that inter-cell interference is mitigated. As the scheme operates iteratively on a two-cell basis, however, it is clearly unsuitable for multi-cellular resource allocation. Finally, in , an energy-aware cross-layer radio management framework is proposed, that partitions the global optimisation problem into subproblems, which can be solved locally. While achieving substantial gains, the focus of the work is on multimode communication (i.e., cellular, WLANs, WMANs, etc.), and so an optimisation for pure cellular communication is not offered. In general, it is evident that the challenge of resource and power allocation has been thoroughly investigated as an optimisation problem, however in most cases these problems are non-convex, very hard to solve, and hence suboptimal heuristics are developed. In this work, a resource and power allocation technique based on local interference requirements will be developed to manage this challenge.
Much of the previous work on energy efficient systems concentrates on network optimisation and scheduling policies. Macro-cell size reduction for better energy efficiency is investigated in , with positive results. Of course, reducing the cell-sizes means increasing the number of BSs in an area, which is generally rejected due to the enhanced infrastructure expenses. In , game-theoretic approaches are utilised to, minimise the cost per reliable bit sent in energy constrained networks. However, it is seen that there is a clear tradeoff between energy and spectral efficiency, and hence the energy-efficient resource allocations tend to be spectrally inefficient. This is further highlighted in , where an analytical model determines the optimal energy-spectral efficiency tradeoff for the downlink in OFDMA networks. In this article, however, we present an ICIC technique which utilises interfering link gains to not only provide interference mitigation and spectral efficiency gains in the uplink, but also generate large energy savings.
An energy efficient interference protection technique for the uplink of OFDMA-based systems is introduced. By reducing the power on the interfering link, the SINRs of individual RBs can be enhanced. This power reduction also results in a more energy efficient system. By segregating the spectrum into priority bands, MSs allocated lower priority RBs provide interference protection for higher priority RBs in neighbouring cells by decreasing their transmit power. The priority bands (i.e., low to high) are allocated such that the same RBs in any neighbouring cells do not share the same priority class, and hence a priority reuse scheme  is established. Furthermore, the proposed power reduction is based on target SINRs, providing real-time service-dependent interference coordination and energy efficiency in the uplink.
The rest of the article is structured as follows: Section 2 describes the system and channel environment, Section 3 explains the uplink interference protection (ULIP) protocol and its performance in wireless networks is analysed in Section 4. In Sections 5 and 6 the resource scheduler and simulation are described, respectively. Finally, Section 7 portrays and discusses the simulation results, and some concluding remarks are offered in Section 8.
The reverse link of an OFDMA system is considered, where the system bandwidth B is divided into M RBs. A RB defines one basic time-frequency unit of bandwidth All MSs can transmit up to a maximum power Pmax, and hence up to on each RB. Perfect time and frequency synchronisation is assumed.
Adaptive modulation and coding table
min. SINR [dB]
where P u is the total transmit power of MS u , and C u the throughput from (5).
where k indicates the time slot, Nsys the number of MS in the system, and C u (k) the achieved throughput of MS u over all time slots 1: k.
2.1. Channel model
where describes the channel transfer function between transmitter k and receiver l on RB m , L(d) is the distance-dependent path loss (in dB) and X σ is the log-normal shadowing value (in dB) with standard deviation σ, as described in . The channel generally exhibits time and frequency dispersions, however channel fluctuations within a RB are not considered as the RB dimensions are significantly smaller than the coherence time and frequency of the channel . Furthermore, the large-scale path loss L(d) is identical on all RBs assigned to a MS. Finally, the delay profiles used to generate the frequency-selective fading channel transfer factor are taken from applicable propagation scenarios in [21, 23].
where d is the distance between transmitter and receiver.
Log-normal shadowing is added to all links through the use of correlated shadowing maps. These are generated such that the correlation between two points is distance-dependent.
Given the detailed description of the ULIP technique, the expected performance of a system employing this mechanism can be explored. There are multiple analysis techniques that deal with such problems, more specifically with system capacity analysis. In [29–31], a reverse link capacity analysis assuming non-cooperative BSs (similar to the design of practical cellular systems) is unfortunately shown to be a long-standing open problem in information theory, but has been solved when treating the interference as Gaussian noise . Clearly, since in ULIP the interference incident on each RB is dependent on the interference tolerances of other-cell high-priority MSs allocated that RB, the interference is most certainly not Gaussian. Hence, such an analysis is infeasible for a system employing ULIP. In [29, 33], the area spectral efficiency is introduced as a capacity measure that utilises stochastic geometry (statistical analysis of the positions and gains of MSs in the system) to estimate the expected capacity of a cellular network. Because in ULIP the users in a cell are split into three interdependent groups, such an analysis would be difficult as it is not always clear (by position) which MSs are assigned high-, mid-, or low-priority. Furthermore, in  the interference is estimated stochastically, and since in ULIP the interference is dependent on individual MS requirements, this analysis would be misguided.
On the other hand, optimisation techniques [11, 34] can be utilised to provide global solutions that optimise an overall performance goal (e.g., energy/spectral efficiency). Furthermore, these offer an overall characterisation of the wireless system. In ULIP, however, the aim is not to maximise/minimise any objective, but rather to provide individual MSs with the necessary interference mitigation such that these can achieve their SINR/rate requirements. This is clearly not a system-wide goal, and hence such a description of a ULIP system is not applicable.
In general, the main difficulty that is not overcome (in the aforementioned methods) is the multitude of interdependencies on each RB over the network. The transmit powers on an RB are dependent on the signal qualities of the users allocated this RB in other cells in the network. Furthermore, these interdependencies are constantly adapting depending on the SINRs of the individual MSs in each cell. Hence, the stochastic interference modelling used in capacity analysis techniques cannot be utilised to model cellular ULIP. Therefore, a theoretical comparison to the state-of-the-art is performed to highlight the potential benefits of ULIP for OFDMA networks. And while transmit power control is standard for the reverse link in future systems, it has been shown that maximum power transmission is capacity-achieving , and thus this is compared to ULIP here. Analytical derivations for the energy efficiency and system capacity performance of ULIP are presented.
4.1. Energy efficiency in ULIP
In a system that employs ULIP, the transmit powers of low-priority MSs (MSs allocated low-priority RBs) are reduced so that interference to other cells is mitigated. Clearly, the throughput of the low-priority MSs is diminished relative to the reduction in transmit power. However, given a measure for energy efficiency, it can be shown that ULIP guarantees energy efficiency gains.
In essence, it is shown that (22) is true, which, combined with the energy efficiency results demonstrates the potential of ULIP for future OFDMA-based wireless networks such as LTE and/or LTE-Advanced. The proof is found in Appendix.
Although in certain scenarios a loss in system capacity is incurred by the system-wide power reduction (as (22) suggests), the guaranteed energy efficiency gain can compensate this deficit. Furthermore, the possibility of gains in both performance metrics, i.e., when Csys is improved, is a good indication of the benefits ULIP can bring to future wireless networks.
To facilitate the interference protection, a scheduling procedure is designed to assign MSs to specific priority bands, enhancing the effect of ULIP in the system. In general, a random allocation of priority RBs can lead to undesired scenarios. For instance, the allocation of a high-priority RB to cell-centre MSs is wasteful, as such a MS-BS link is generally strong, and hence interference protection is unnecessary. At the cell-edge, allocating a low-priority RB to a MS is just as destructive. In this case, the MS will most probably be unable to sustain its γtar, and hence fall into outage. Therefore, an appropriate scheduling mechanism is necessary for ULIP to achieve its full potential.
In a fair allocation scheme, cell-edge MSs should be allocated high-priority RBs so as to be able to transmit at full power and achieve the maximum possible SINR. Cell-centre users, which are more likely to achieve their SINR target due to BS proximity, should be assigned low-priority RBs. In essence, the general rule is to allocate high-priority RBs to the MSs with the least favourable SINR conditions.
Therefore, an efficient scheduling procedure can increase the effectiveness of ULIP, and prevent throughput losses due to MS outages. In this section, a scheduling procedure relying on the reverse link signals of the active users is presented. By analysing the signals, an approximation of the relative positions of the MSs (and their interferers) can be obtained, which can then be used to schedule the users accordingly. This presents a low complexity scheduling solution, as the necessary information is readily available at the BS.
5.1. SINR scheduling
It is clear to see that the farther MSs (from the serving BS) have been allocated high-priority RBs, and to the nearer MSs, which are shielded from neighbouring cell interference, the low-priority RBs are assigned. The mid-priority RBs have been assigned to the remaining MSs.
When a new MS enters the cell, the initial allocation is performed using the SNR (which can be approximated using the RSRP), as no SINR information is available a priori. Mean SINR statistics are employed to eliminate fast fading effects and prevent a MS from rapidly changing priority class, so that the system can reach a stable operating point.
Monte Carlo simulations are used to provide performance statistics of the users and the system with and without ULIP. The simulator is built following LTE specifications.
6.1. Network construction and user distribution
The simulation area is comprised of a single-tier, tessellated hexagonal cell distribution. To eliminate border effects with regards to interference, an additional two tiers are simulated. However, statistics are only taken from the first tier (and centre cell). Users are distributed uniformly over the simulation area such that each cell hosts, on average, MSs. Further, BS-MS allocation is done based on path loss, such that each MS is assigned to the BS with the most favourable channel conditions.
Each cell is served by a sector of a macro-BS, where a BS has three 120° sectors. Each BS is placed at the junction of the three hexagonal cells it serves. Figure 3 shows an example of the network construction and priority band allocation, and Figure 6 shows an example of the inner tier simulation area.
where θ is the angle the MS-BS link deviates from the central lobe, θ3 dB is the angle at which the gain is half that of at the centre of the lobe, and A m is the maximum possible attenuation . Through (28), the horizontal signal attenuation due to MS position is determined.
6.2. Resource allocation
The priority classes in each cell are organised in the manner portrayed in Figure 3, such that when a MS is allocated to a particular priority class, its RBs (if it is assigned more than one) can be allocated contiguously, a feature particular to an LTE uplink. The allotment of users to priority classes is performed by the SINR scheduler introduced in Section 5. Within each class, the set of RBs is randomly (but still contiguously) allocated to the MSs assigned to that class, with each user receiving at minimum one RB.
6.3. Time evolution
Each run of the Monte Carlo simulation is iterated over z = 10 subframes, or, equivalently, one LTE frame, such that long-term SINR statistics can be gathered. Due to the random user distribution, plentiful runs with different network generations are considered in order to obtain statistically accurate results. In each run, i.e., at the start of each subframe, the scheduling and allocation of RBs is reperformed. The MSs are assumed to be quasi-static for the duration of a run.
inner 7 cells
Inter-site distance, dIS
Average MSs per cell,
Uplink FDD band
[2.50, 2.51] GHz
Number of available RBs, M
RB bandwidth, BRB
Subcarriers per RB, ksc
Symbol rate per subcarrier, ϱs
Subframe duration, tsf
Subframes (time slots), z
Thermal noise, η
Total MS transmit power
Sector θ3 dB
MS SINR target, γtar
Standard deviation, σ
To evaluate the performance of ULIP, two well-known benchmark systems have been implemented for comparison purposes. These are:
Maximum power transmission: In the first benchmark, no power allocation is performed, and all MSs transmit at the maximum power on each RB.
LTE power control: In the second benchmark, the transmit power is set dependent on the nominal SINR target Γ, the desired link path loss Ldes, the strongest interfering link loss Lint, and the average interference received on that RB . Here, LTE fractional power control (FPC)  is used, where(29)
which, depending on α, achieves a balance between conventional power control (α = 1) and maximum power transmission (α = 0).
For each of the benchmarks, the RB allocation from the ULIP system is adopted, resulting in a soft frequency reuse scheme . By comparing the performance of ULIP to these two benchmarks, the effect ULIP has on the performance of the system can be quantified.
The performance of the system is measured by three criteria: achievable throughput, energy efficiency and fairness (as defined in (5), (7), and (8), respectively). Multiple iterations are run for a system employing ULIP and the benchmark systems. The cumulative distribution functions (CDFs) of achievable throughput and energy efficiency of individual MSs and of the network are compared. From this, quantitative average gain/loss statistics are generated.
From the simulation, the CDFs of the achieved system throughput and energy efficiency are generated for systems employing ULIP and compared against the two benchmark systems, keeping the RB allocation unchanged. General simulation parameters are taken from Table 2 and , and full power control (i.e., α = 1) is implemented.
Also, although at the 90th percentile a 31% loss is incurred by the power reduction on low-priority (and therefore high-throughput) RBs, the crossing point of the CDFs signifies that 82% of the users achieve a better SINR (and consequently throughput) in ULIP. Furthermore, the ≈ 20% outage seen in both benchmarks is eliminated, and hence ULIP provides significant advantages for the users in a cellular network.
Furthermore, ULIP achieves energy efficiency gains for all MS over the maximum power benchmark, confirming the result of the performance analysis conducted in Section 4.1.
The substantial gains achieved by ULIP over maximum power transmission (3.3×) can be accounted for by the balancing of the system capacity from the cell-centre to the cell-edge, boosting high-priority throughput by sacrificing that of the low-priority MSs, and hence achieving a more throughput fair system.
This is a very encouraging result, as it shows that the throughput shift from low- to high-priority MSs is beneficial for the system, achieving larger throughput gains for the high-priority users than losses by the low-priority MSs. This is also a direct result of the link adaptation, as any excess SINR (i.e., γ > 20 dB) at the cell-centre can be transferred to the cell-edge without incurring any throughput losses for the low-priority (cell-centre) users. Furthermore, Figure 11 confirms the result achieved in Section 4.2, and shows further that system capacity gains are achievable.
All in all, ULIP dominates each of the two benchmarks over the three performance criteria, especially providing a much more energy efficient and fair system. Furthermore, by achieving considerable gains in network capacity, it is clear that both performance analysis proofs have been confirmed.
Full frequency reuse and the resulting large CCI in OFDMA networks brings forth the necessity for ICIC in future wireless networks. A technique for ULIP has been presented in this article, which provides protection from CCI through the power reduction of a subset of the neighbouring cell RBs, based on the SINR targets of the MSs in the cell of interest. Aside from the fact that no extra signalling is necessary over the control channels, a further benefit of ULIP is a guaranteed increase in energy efficiency of all MSs in the system, and of the system as a whole. Furthermore, it was shown that while a loss in system capacity is possible, this is not certain, and hence gains in achievable system throughput are also possible. This is especially the case in networks where cell-edge capacity is limited, and most of the cell throughput is concentrated in the cell-centre.
It was shown that ULIP, combined with the SINR scheduler, achieves not only a 15% system capacity gain, but also substantially increases the system energy efficiency and fairness by 3.5× and 3.3×, respectively. This is a direct result of the SINR displacement from the cell-centre to the cell-edge, and confirms the results in Section 4, highlighting the excellent energy efficiency of the ULIP protocol. A throughput drop is seen when power control is applied, mainly due to the SINR targeting of the system in comparison to maximum power, which does not restrict transmit power according to service requirements. Furthermore, ULIP eliminates the ≈20% outage suffered in the benchmarks, and provides throughput gains for over 80% of the MSs in the network. Consequently, ULIP diminishes the tradeoff between system capacity and fairness/energy efficiency, and provides significant gains in all three performance areas.
aIn Table 1, the modulation and coding schemes are taken from LTE , and the SINR ranges from . Here, the downlink values are used because no uplink implementation was found, as these values are operator specific. bThese denote the priority status of the RBs within each class, and have no relation to user traffic priorities, which are not considered here.
System capacity proof derivation
where α is the scaling factor by which MS1 reduces its transmit power.
And given P u = Pmax/M ≈ 6 dBm, the minimum received interference is , which is significantly larger than η. In fact, even for dIS = 500 m, the minimum average interference comes to -116.8 dBm, which is still more than double the noise power.
and that, hence, (30) is not true. Therefore, (22) is valid.
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