Interference Cancellation for Cellular Systems: A
Cellular networks today are interference-limited and will only become increasingly so in the future,due to the many users that need to share the spectrum to achieve high-rate multimedia communication.Despite the enormous amount of academic and industrial research in the past twenty years on interference-aware receivers and the large performance improvements promised by these multiuser techniques, today’s receivers still generally treat interference as background noise. In this paper, we enumerate the reasons for this widespread scepticism, and discuss how current and future trends will increase the need and viability of multiuser receivers for both the uplink, where many asynchronous users will be simultaneously detected, and the downlink, where users will be scheduled and largely orthogonalized, but the mobile handset will still need to cope with a few dominant interfering base stations.New results for interference cancelling receivers that use conventional front ends are shown to alleviate many of the shortcomings of prior techniques, particularly for the challenging uplink. This paper gives an overview of key recent research breakthroughs on interference cancellation, and highlights system-level considerations for future multiuser receivers.
The performance of today’s cellular networks is limited by interference, more than by any other single effect. Interference is distinguished from noise in that it is caused by other human-designed devices, often most of it from devices designed to use the same network, which makes it particularly interesting and aggravating. Whereas conventional noise can be overcome by increasing the transmit power, overall interference is increased by this simple-minded approach, since neighboring devices now have to contend with even more interference than before. In both centralized and ad hoc networks, overall system capacity1 can be maximized by having each device use the minimum required transmission power, so that the interference caused to other devices in the network is also minimized.
In the downlink each receiver only needs to decode a single desired signal from K intracell signals, while suppressing other cell interference from a few dominant sources as shown in Figure 1. On the other hand, in the uplink the base station receiver must decode all K desired users while suppressing other cell interference from many independent sources, as shown in Figure 2.
the uplink, since all users are at different distances from the base station Future cellular systems will employ sophisticated scheduling algorithms in the downlink so the primary function of the mobile unit will be to decode the desired signal in the presence of interference from the neighboring cells. This is fortunate, since the mobile units will still be highly power limited and hence have limited processing power. It is difficult to coordinate and accurately synchronize scheduling algorithms for nd have rapidly changing multipath channels. Although this is a more challenging task, the base station receivers will generally have a much higher complexity allowance than their mobile counterparts.
Figure 1: In the downlink scenario, each receiver only needs to decode its own signal, while suppressing other cell interference from just a few dominant neighboring cells. Because all K users’ signals originate at the base station, the link is synchronous and the K ¡ 1 intracell interferers can be orthogonalized at the base station transmitter. Typically though, some orthogonality is lost in the channel.
For these reasons, downlink receivers at the user terminals will employ relatively simple multiuser receivers that attempt to restore the orthogonality of the intracell users via either a chip-level equalizer (CDMA) or inter-carrier interference suppression (multiuser OFDM), while handling at most a few dominant and unknown other-cell interferers. And although multiuser scheduling may increase throughput and decrease the number of interfering users, at lower spreading factors interference suppression will become even more crucial.
2 Multiuser Detection: Historical Overview and Shortcomings
The idea of simultaneously receiving multiple interfering users is not particularly new. Most current wireless communication systems already have to cope with a large degree of multiple access interference
Figure 2: In the uplink scenario, the base station receiver must decode all K desired users, while suppressing other cell interference from many independent users. Because it is challenging to dynamically synchronize allK desired users, they generally transmit synchronously with respect to each other, making orthogonal spreading codes unviable.
In the important special case of CDMA systems like IS-95 and 3G cellular, an interference-limited system is willfully created in order to achieve capacity benefits deriving from voice activity and universal frequency reuse. While simple, proven, and robust, this technique is decidedly suboptimal in most circumstances from an information theory perspective, particularly when the number of interferers is large.
For synchronous systems like the cellular downlink, this approach doesn’t make sense – the better approach would simply be to assign the users orthogonal codes at the transmitter and maintain a simple, single user receiver
3 The future of multiuser detection
Despite the bleak picture for multiuser receivers that unfolded in the previous section, the future appears much brighter. The methodology and application of future wireless networks (beyond 3G) may change dramatically from the current voice-centric, circuit-switched paradigm, and other recent technical developments give new hope for certain classes of multiuser receivers.
Complexity, Cost, and Service Provider Motivation. New end user behavior is increasing the demand for uplink data rates in all types of communication systems. Peer-to-peer applications, Multimedia Messaging Service (MMS), camera phones, gaming, and other high data rate applications have made the assumption of asymmetric data rates questionable. Lower spreading factors are the norm for the higher rate standards that support these applications (e.g. HSDPA ) which makes multiuser receivers increasingly viable and valuable. concatenated block. For this reason, successive interference cancellation, parallel interference cancellation and iterative interference cancellation (Turbo MUD) appear quite attractive.
Table 1: Key General Trends of Different Multiuser Receivers, with spreading factor N, number of users K, and P receiver stages.
Table 1 gives a high-level comparison of some of the different types of multiuser receivers. Due to the vast number of different subtypes for each of these receivers in the literature, these values should be interpreted as general trends.They also incorporate error correction codes in different ways: in some systems like Turbo MUD and SIC, ECCs are directly integrated into the receiver structure whereas in other systems they must form a separate block independent from the user separation, which generally reduces performance since the coding redundancy competes with the spreading gain, N.
4 Interference Cancellation: A proven approach
Although the application of interference cancellation to multiuser systems is relatively new and unproven,other forms of interference cancellation have been in widespread use for years. Although the phrase has been used fairly loosely, interference cancellation should be interpreted to mean the class of techniques that demodulate and/or decode desired information, and then use this information along with channel estimates to cancel received interference from the received signalIn the DFE, the desired symbol x[n] at some time n is decoded. Since this symbol will interfere with many future symbols – i.e. from times n+1; n+2; : : : – given knowledge of the channel, this intersymbol interference can be cancelled. The DFE is known to work well in practice, and achieve far better performance than linear equalizers which suffer from noise enhancement.
Figure 3: Block Diagram of the Decision Feedback Equalizer.
The same reasoning applies to analogous types of interference, such as multiuser interference or spatial interference. The original BLAST system and industry adaptations of spatial interference cancellation receivers for multiantenna systems can be used to separate spatially multiplexed streams of data.These types of post processing receivers often significantly outperform stand-alone linear receivers such as MMSE or zero-forcing in noisy environments. Since the cellular environment will invariably have a high level of noise and background interference (from other cells, for example), these linear interference suppression techniques are not viable as they amplify this noise when inverting the spatial matrix channel.Analogous logic can be applied to multiuser systems, and it is well established that linear (dimensional) multiuser detectors have a noise enhancement penalty.
5 Recent research on multiuser interference cancellation
Interference cancellation for multiuser systems has generally been broken into two categories, parallel and successive, although recent developments in iterative interference cancellation have blurred the distinction.Both SIC and PIC have the important advantage over other types of multiuser receivers that error correction coding is integrated into the multiuser detection process. As previously noted, both SIC and PIC are primarily applicable to the uplink in a many user CDMA system. However, for a non-CDMA system, these same powerful techniques, suitably modified,can help suppress other-cell interference in the downlink. Parallel interference cancellation (PIC), as shown in Fig. 4, detects all the users simultaneously. This initial very coarse estimate can then be used to cancel some interference, and then the parallel detection can be repeated. Successive interference cancellation (SIC), shown in Fig. 5, detects just one user per stage.
Figure 4: Parallel Interference Cancellation.
This process can be repeated over several stages, hence PIC is sometimes called multistage interference cancellation . Since the first stage generally results in very noisy data estimates, soft interference cancellation is necessary .
Figure 5: Successive Interference Cancellation.
This can be subtracted from the composite received signal, which then allows subsequent users to experience a cleaner signal. All users have improved performance: earlier users because they can have disproportionately high received power, and later users because a large fraction of the total interference has been removed by the time they are detected.
There are a variety of tradeoffs between SIC and PIC. PIC has decreased latency, but higher overall complexity because K users must be detected in parallel, plus there are P cancellation stages. So the latency is proportional to P which is generally much smaller than K for cellular systems, but complexity is proportional to PK. SIC on the other han has complexity and latency proportional to K, and this latency may be prohibitive if there are many users with real-time data. Some authors have attempted to provide a smoother tradeoff between these techniques by introducing multistage SIC: a group of users are detected in parallel, and then has their aggregate interference subtracted from the composite received signal, and then another group is detected in parallel.
Channel estimation error and error propagation. The entire concept of interference cancellation is based on the premise that the received signal can be reliably estimated. Whereas communication systems are by definition designed to allow the transmitted signal to be recovered, reconstructing the received signal requires an accurate description of both what was transmitted, and what the channel did to that transmission. Inaccurate channel estimation is a problem for both PIC and SIC, especially for SIC since historically, the optimal received power distribution is based on the assumption that interference has been completely cancelled, which is never fully achieved in practice. This residual interference then causes the later users to have unacceptably bad performance, causing a major fairness problem as well as an overall degradation in bit-error rate and system capacity.
In Figure 6, it can be seen that if the channel estimation error is on average larger than about 20%, then the system was better off without SIC, assuming the traditional power control for SIC is used that assumed perfect interference cancellation. Whereas with a modified power control formulation that accounts for the statistics of the channel estimation error, even with dramatic estimation error as high as 50%, SIC nearly doubles the system capacity relative to no interference cancellation.
Power control. Whereas parallel interference cancellation functions best in the familiar case where all the received powers are equal, we have just seen that successive interference cancellation works best when a specific and unequal distribution of user powers is maintained, and furthermore when the distribution specifically considers imperfect interference cancellation. An unequal received power distribution has also been shown to be highly preferable for iterative interference cancellation . This apparent complication of CDMA power control has been frequently cited as a major shortcoming of SIC. But recently it has been shown using new results in power control theory that the optimum SIC power distribution, even with channel estimation error accounted for, can easily be accomplished using binary iterative feedback algorithms.
Multipath channels. Multipath channels are challenging for all wireless systems, but particularly for multiuser receivers. The reason is that each multipath component can appear to be a user of this system,so the quantity of perceived users grows not just with the number of users K, but also roughly with the number of multipath components L. Although interference cancellation receivers, which are based on conventional receivers, can easily employ a RAKE receiver to handle multipath, it may be difficult to accurately regenerate the interference for cancellation if there are many multipath components. If each multipath component has an independent amplitude and phase, then generally the estimation error for each will be independent. This can cause the capacity to decrease rapidly as the multipath profile worsens,since channel estimation errors effect all the users signals in every dimension when the multipath interference is regenerated in the time-domain by the RAKE “encoder”.
When multipath has been treated in the interference cancellation literature, researchers have often assumed that the channel is perfectly known,or they have not adapted to the imperfect channel estimation. In addition to the more recent joint channel estimation/detection approaches discussed another promising technique is multicarrier CDMA (MC-CDMA), which then allows the channel estimation to be done in the frequency domain, where channel estimation errors don’t compound each other as they do in the time domain. When MC-CDMA is used for the interference cancellation large gains are attained in realistic multipath fading channels
Iterative Interference Cancellation By iteratively passing probabilistic estimates between two decoders, dramatic performance improvements over prior decoding techniques were observed. Downlink multiuser receivers. Uplink multiuser receivers need to suppress interference from a large number of independent transmitters, while downlink receivers should be able to suppress interference from just a few neighboring base stations, but with extreme complexity limitations. Along these lines,recent research has attempted to develop modified maximum likelihood and linear detectors for the GSM downlink that are able to suppress other cell interference. While this is for a single-carrier,non-CDMA system, it provides some important lessons for how future cellular systems may be able to cope with other-cell interference in the downlink.
Figure 6: An iterative interference cancelling receiver.
Exploiting the characteristics of GMSK modulation allows signal reception to be split into two virtual paths, which can then be processed using classical linear processing techniques such as ZF or MMSE . Hybrid schemes that employ a combination of linear and non-linear processing are also possible.The hybrid schemes contrast to joint detection as they do not require estimation of the channel response of the interferer, and are thus referred to as “blind” techniques. An advantage of being blind with respect to the interferer’s channel response is that these techniques are amenable to asynchronous networks, where the channel training codes of the interferer will not typically overlap with the training codes of the desired signal. However, joint detection is also possible in asynchronous networks provided the mobile terminal platform can handle the complexity. Collectively, these joint detection and hybrid/linear receivers are referred to as Single Antenna Interference Cancellation (SAIC) receivers. It should be noted that these kinds of interference “cancelling” receivers may employ either maximum likelihood detection or pre-detection processing rather than the post-detection interference cancellation emphasized in this paper.
SAIC techniques have proven successful in field trials in suppressing other cell interference in GSM systems, and will likely play a major role in future cellular systems as a method to decrease the spatial reuse distance, which is critical to achieving high overall spectral efficiency.
Complexity and Implementation. As the literature on multiuser receivers has become increasingly comprehensive in recent years, the bottleneck for the adoption of multiuser receivers has increasingly become issues relating to complexity and implementation. Many of these implementation issues are not simply a matter of efficient integrated circuit design, but rather require system-level thinking about the crucial features of multiuser receivers.
A WCDMA-compliant iterative MUD prototype has been shown to increase capacity by a factor of 2-3 and coverage by around 50%. In short, significant steps have been taken recently towards the realization of practical multiuser receivers, but more research and development is needed to make multiuser receivers practical for future standards.
6 The role of interference cancellation in future cellular systemsAs we have attempted to establish, due to evolving applications, future cellular systems are likely to carry high data rates with more burstiness than voice. The synchronous downlink will likely employ opportunistic scheduling across time, frequency, and/or codes, and so the majority of the interference at the mobile stations will come from a small number of neighboring base stations, rather than the present scenario where much of the interference in intracell. For these reasons, multiuser receivers will play an important, but different, role in the downlink and uplink of future cellular systems. In the uplink, there will be many asynchronous users, although fewer than in a 2G or 3G system as the cells will continue to grow smaller while data will be burstier and higher rate, and hence the spreading factor smaller. This makes interference cancellation techniques all the more desirable and practical. Recent research on interference cancellation, highlighted in this paper, has made significant strides, but more research and development is required to prototype these systems and adapt them to real-world environments.For the downlink, the need will be to attenuate the interference from a small number of neighboring base stations while maintaining the orthogonality of the users within the cell in time, frequency, code,and/or space, as the case may be. This implies that the success of the recent research and implementation of Single Antenna Interference Cancellation (SAIC) techniques for single-carrier GSM systems should be extended through future research to higher-bandwidth and more complex multicarrier and CDMA systems.
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